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Stock Market Volatility and Return Analysis: A Systematic Literature Review

Roni bhowmik.

1 School of Economics and Management, Jiujiang University, Jiujiang 322227, China

2 Department of Business Administration, Daffodil International University, Dhaka 1207, Bangladesh

Shouyang Wang

3 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China; nc.ca.ssma@gnawys

In the field of business research method, a literature review is more relevant than ever. Even though there has been lack of integrity and inflexibility in traditional literature reviews with questions being raised about the quality and trustworthiness of these types of reviews. This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed. The stock market plays a pivotal role in today’s world economic activities, named a “barometer” and “alarm” for economic and financial activities in a country or region. In order to prevent uncertainty and risk in the stock market, it is particularly important to measure effectively the volatility of stock index returns. However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008–2019) and in 50 different papers. The study found that there has been a significant change in research work within the past 10 years and most of researchers have worked for developing stock markets.

1. Introduction

In the context of economic globalization, especially after the impact of the contemporary international financial crisis, the stock market has experienced unprecedented fluctuations. This volatility increases the uncertainty and risk of the stock market and is detrimental to the normal operation of the stock market. To reduce this uncertainty, it is particularly important to measure accurately the volatility of stock index returns. At the same time, due to the important position of the stock market in the global economy, the beneficial development of the stock market has become the focus. Therefore, the knowledge of theoretical and literature significance of volatility are needed to measure the volatility of stock index returns.

Volatility is a hot issue in economic and financial research. Volatility is one of the most important characteristics of financial markets. It is directly related to market uncertainty and affects the investment behavior of enterprises and individuals. A study of the volatility of financial asset returns is also one of the core issues in modern financial research and this volatility is often described and measured by the variance of the rate of return. However, forecasting perfect market volatility is difficult work and despite the availability of various models and techniques, not all of them work equally for all stock markets. It is for this reason that researchers and financial analysts face such a complexity in market returns and volatilities forecasting.

The traditional econometric model often assumes that the variance is constant, that is, the variance is kept constant at different times. An accurate measurement of the rate of return’s fluctuation is directly related to the correctness of portfolio selection, the effectiveness of risk management, and the rationality of asset pricing. However, with the development of financial theory and the deepening of empirical research, it was found that this assumption is not reasonable. Additionally, the volatility of asset prices is one of the most puzzling phenomena in financial economics. It is a great challenge for investors to get a pure understanding of volatility.

A literature reviews act as a significant part of all kinds of research work. Literature reviews serve as a foundation for knowledge progress, make guidelines for plan and practice, provide grounds of an effect, and, if well guided, have the capacity to create new ideas and directions for a particular area [ 1 ]. Similarly, they carry out as the basis for future research and theory work. This paper conducts a literature review of stock returns and volatility analysis based on generalized autoregressive conditional heteroskedastic (GARCH) family models. Volatility refers to the degree of dispersion of random variables.

Financial market volatility is mainly reflected in the deviation of the expected future value of assets. The possibility, that is, volatility, represents the uncertainty of the future price of an asset. This uncertainty is usually characterized by variance or standard deviation. There are currently two main explanations in the academic world for the relationship between these two: The leverage effect and the volatility feedback hypothesis. Leverage often means that unfavorable news appears, stock price falls, leading to an increase in the leverage factor, and thus the degree of stock volatility increases. Conversely, the degree of volatility weakens; volatility feedback can be simply described as unpredictable stock volatility that will inevitably lead to higher risk in the future.

There are many factors that affect price movements in the stock market. Firstly, there is the impact of monetary policy on the stock market, which is extremely substantial. If a loose monetary policy is implemented in a year, the probability of a stock market index rise will increase. On the other hand, if a relatively tight monetary policy is implemented in a year, the probability of a stock market index decline will increase. Secondly, there is the impact of interest rate liberalization on risk-free interest rates. Looking at the major global capital markets, the change in risk-free interest rates has a greater correlation with the current stock market. In general, when interest rates continue to rise, the risk-free interest rate will rise, and the cost of capital invested in the stock market will rise simultaneously. As a result, the economy is expected to gradually pick up during the release of the reform dividend, and the stock market is expected to achieve a higher return on investment.

Volatility is the tendency for prices to change unexpectedly [ 2 ], however, all kinds of volatility is not bad. At the same time, financial market volatility has also a direct impact on macroeconomic and financial stability. Important economic risk factors are generally highly valued by governments around the world. Therefore, research on the volatility of financial markets has always been the focus of financial economists and financial practitioners. Nowadays, a large part of the literature has studied some characteristics of the stock market, such as the leverage effect of volatility, the short-term memory of volatility, and the GARCH effect, etc., but some researchers show that when adopting short-term memory by the GARCH model, there is usually a confusing phenomenon, as the sampling interval tends to zero. The characterization of the tail of the yield generally assumes an ideal situation, that is, obeys the normal distribution, but this perfect situation is usually not established.

Researchers have proposed different distributed models in order to better describe the thick tail of the daily rate of return. Engle [ 3 ] first proposed an autoregressive conditional heteroscedasticity model (ARCH model) to characterize some possible correlations of the conditional variance of the prediction error. Bollerslev [ 4 ] has been extended it to form a generalized autoregressive conditional heteroskedastic model (GARCH model). Later, the GARCH model rapidly expanded and a GARCH family model was created.

When employing GARCH family models to analyze and forecast return volatility, selection of input variables for forecasting is crucial as the appropriate and essential condition will be given for the method to have a stationary solution and perfect matching [ 5 ]. It has been shown in several findings that the unchanged model can produce suggestively different results when it is consumed with different inputs. Thus, another key purpose of this literature review is to observe studies which use directional prediction accuracy model as a yardstick from a realistic point of understanding and has the core objective of the forecast of financial time series in stock market return. Researchers estimate little forecast error, namely measured as mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) which do not essentially interpret into capital gain [ 6 , 7 ]. Some others mention that the predictions are not required to be precise in terms of NMSE (normalized mean squared error) [ 8 ]. It means that finding the low rate of root mean squared error does not feed high returns, in another words, the relationship is not linear between two.

In this manuscript, it is proposed to categorize the studies not only by their model selection standards but also for the inputs used for the return volatility as well as how precise it is spending them in terms of return directions. In this investigation, the authors repute studies which use percentage of success trades benchmark procedures for analyzing the researchers’ proposed models. From this theme, this study’s authentic approach is compared with earlier models in the literature review for input variables used for forecasting volatility and how precise they are in analyzing the direction of the related time series. There are other review studies on return and volatility analysis and GARCH-family based financial forecasting methods done by a number of researchers [ 9 , 10 , 11 , 12 , 13 ]. Consequently, the aim of this manuscript is to put forward the importance of sufficient and necessary conditions for model selection and contribute for the better understanding of academic researchers and financial practitioners.

Systematic reviews have most notable been expanded by medical science as a way to synthesize research recognition in a systematic, transparent, and reproducible process. Despite the opportunity of this technique, its exercise has not been overly widespread in business research, but it is expanding day by day. In this paper, the authors have used the systematic review process because the target of a systematic review is to determine all empirical indication that fits the pre-decided inclusion criteria or standard of response to a certain research question. Researchers proved that GARCH is the most suitable model to use when one has to analysis the volatility of the returns of stocks with big volumes of observations [ 3 , 4 , 6 , 9 , 13 ]. Researchers observe keenly all the selected literature to answer the following research question: What are the effective GARCH models to recommend for performing market volatility and return analysis?

The main contribution of this paper is found in the following four aspects: (1) The best GARCH models can be recommended for stock market returns and volatilities evaluation. (2) The manuscript considers recent papers, 2008 to 2019, which have not been covered in previous studies. (3) In this study, both qualitative and quantitative processes have been used to examine the literature involving stock returns and volatilities. (4) The manuscript provides a study based on journals that will help academics and researchers recognize important journals that they can denote for a literature review, recognize factors motivating analysis stock returns and volatilities, and can publish their worth study manuscripts.

2. Methodology

A systematic literature examination of databases should recognize as complete a list as possible of relevant literature while keeping the number of irrelevant knocks small. The study is conducted by a systematic based literature review, following suggestions from scholars [ 14 , 15 ]. This manuscript was led by a systematic database search, surveyed by cross-reference snowballing, as demonstrated in Figure 1 , which was adapted from Geissdoerfer et al. [ 16 ]. Two databases were selected for the literature search: Scopus and Web-of-Science. These databases were preferred as they have some major depositories of research and are usually used in literature reviews for business research [ 17 ].

Literature review method.

At first stage, a systematic literature search is managed. The keywords that were too broad or likely to be recognized in literature-related keywords with other research areas are specified below. As shown in Table 1 , the search string “market return” in ‘Title‘ respectively “stock market return”, “stock market volatility”, “stock market return volatility”, “GARCH family model* for stock return”, “forecasting stock return”, and GARCH model*, “financial market return and volatility” in ‘Topic’ separately ‘Article title, Abstract, Keywords’ were used to search for reviews of articles in English on the Elsevier Scopus and Thomson Reuters Web-of-Science databases. The asterisk (*) is a commonly used wildcard symbol that broadens a search by finding words that start with the same letters.

Literature search strings for database.

At second stage, suitable cross-references were recognized in this primary sample by first examining the publications’ title in the reference portion and their context and cited content in the text. The abstracts of the recognized further publications were examined to determine whether the paper was appropriate or not. Appropriate references were consequently added to the sample and analogously scanned for appropriate cross-references. This method was continual until no additional appropriate cross-references could be recognized.

At the third stage, the ultimate sample was assimilated, synthesized, and compiled into the literature review presented in the subsequent section. The method was revised a few days before the submission.

Additionally, the list of affiliation criteria in Table 2 , which is formed on discussions of the authors, with the summaries of all research papers were independently checked in a blind system method. Evaluations were established on the content of the abstract, with any extra information unseen, and were comprehensive rather than exclusive. In order to check for inter-coder dependability, an initial sample of 30 abstracts were studied for affiliation by the authors. If the abstract was not satisfactorily enough, the whole paper was studied. Simply, 4.61 percent of the abstract resulted in variance between the researchers. The above-mentioned stages reduced the subsequent number of full papers for examination and synthesis to 50. In order to recognize magnitudes, backgrounds, and moderators, these residual research papers were reviewed in two rounds of reading.

Affiliation criteria.

3. Review of Different Studies

In this paper, a large amount of articles were studied but only a few were well thought out to gather the quality developed earlier. For every published article, three groups were specified. Those groups were considered as index and forecast time period, input elements, econometric models, and study results. The first group namely “index and forecast time period with input elements” was considered since market situation like emerging, frontier, and developed markets which are important parameters of forecast and also the length of evaluation is a necessary characteristic for examining the robustness of the model. Furthermore, input elements are comparatively essential parameters for a forecast model because the analytical and diagnostic ability of the model is mainly supported on the inputs that a variable uses. In the second group, “model” was considered forecast models proposed by authors and other models for assessment. The last group is important to our examination for comparing studies in relationships of proper guiding return and volatility, acquired by using recommended estimate models, named the “study results” group.

Measuring the stock market volatility is an incredibly complex job for researchers. Since volatility tends to cluster, if today’s volatility is high, it is likely to be high tomorrow but they have also had an attractive high hit rate with major disasters [ 4 , 7 , 11 , 12 ]. GARCH models have a strong background, recently having crossed 30 years of the fast progress of GARCH-type models for investigating the volatility of market data. Literature of eligible papers were clustered in two sub groups, the first group containing GARCH and its variations model, and the second group containing bivariate and other multivariate GARCH models, summarized in a table format for future studies. Table 3 explains the review of GARCH and its variations models. The univariate GARCH model is for a single time series. It is a statistical model that is used to analyze a number of different kinds of financial data. Financial institutions and researchers usually use this model to estimate the volatility of returns for stocks, bonds, and market indices. In the GARCH model, current volatility is influenced by past innovation to volatility. GARCH models are used to model for forecast volatility of one time series. The most widely used GARCH form is GARCH (1, 1) and this has some extensions.

Different literature studies based on generalized autoregressive conditional heteroskedastic (GARCH) and its variations models.

Notes: APARCH (Asymmetric Power ARCH), AIC (Akaike Information Criterion), OHLC (Open-High-Low-Close Chart), NSE (National Stock Exchange of India), EWMA (Exponentially Weighted Moving Average), CGARCH (Component GARCH), BDS (Brock, Dechert & Scheinkman) Test, ARCH-LM (ARCH-Lagrange Multiplier) test, VAR (Vector Autoregression) model, VEC (Vector Error Correction) model, ARFIMA (Autoregressive Fractional Integral Moving Average), FIGARCH (Fractionally Integrated GARCH), SHCI (Shanghai Stock Exchange Composite Index), SZCI (Shenzhen Stock Exchange Component Index), ADF (Augmented Dickey–Fuller) test, BSE (Bombay Stock Exchange), and PGARCH (Periodic GARCH) are discussed.

In a simple GARCH model, the squared volatility σ t 2 is allowed to change on previous squared volatilities, as well as previous squared values of the process. The conditional variance satisfies the following form: σ t 2 = α 0 + α 1 ϵ t − 1 2 + … + α q ϵ t − q 2 + β 1 σ t − 1 2 + … + β p σ t − p 2 where, α i > 0 and β i > 0 . For the GARCH model, residuals’ lags can substitute by a limited number of lags of conditional variances, which abridges the lag structure and in addition the estimation method of coefficients. The most often used GARCH model is the GARCH (1, 1) model. The GARCH (1, 1) process is a covariance-stationary white noise process if and only if α 1 + β < 1 . The variance of the covariance-stationary process is given by α 1   /   ( 1 − α 1 − β ) . It specifies that σ n 2     is based on the most recent observation of φ t 2   and the most recent variance rate σ n − 1 2 . The GARCH (1, 1) model can be written as σ n 2 = ω + α φ n − 1 2 + β σ n − 1 2 and this is usually used for the estimation of parameters in the univariate case.

Though, GARCH model is not a complete model, and thus could be developed, these developments are detected in the form of the alphabet soup that uses GARCH as its key component. There are various additions of the standard GARCH family models. Nonlinear GARCH (NGARCH) was proposed by Engle and Ng [ 18 ]. The conditional covariance equation is in the form: σ t 2 = γ + α ( ε t − 1 − ϑ σ t − 1   ) 2 + β σ t − 1 2 , where α ,   β ,   γ > 0 . The integrated GARCH (IGARCH) is a restricted version of the GARCH model, where the sum of all the parameters sum up to one and this model was introduced by Engle and Bollerslev [ 19 ]. Its phenomenon might be caused by random level shifts in volatility. The simple GARCH model fails in describing the “leverage effects” which are detected in the financial time series data. The exponential GARCH (EGARCH) introduced by Nelson [ 5 ] is to model the logarithm of the variance rather than the level and this model accounts for an asymmetric response to a shock. The GARCH-in-mean (GARCH-M) model adds a heteroskedasticity term into the mean equation and was introduced by Engle et al. [ 20 ]. The quadratic GARCH (QGARCH) model can handle asymmetric effects of positive and negative shocks and this model was introduced by Sentana [ 21 ]. The Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) model was introduced by Glosten et al. [ 22 ], its opposite effects of negative and positive shocks taking into account the leverage fact. The threshold GARCH (TGARCH) model was introduced by Zakoian [ 23 ], this model is also commonly used to handle leverage effects of good news and bad news on volatility. The family GARCH (FGARCH) model was introduced by Hentschel [ 24 ] and is an omnibus model that is a mix of other symmetric or asymmetric GARCH models. The COGARCH model was introduced by Klüppelberg et al. [ 25 ] and is actually the stochastic volatility model, being an extension of the GARCH time series concept to continuous time. The power-transformed and threshold GARCH (PTTGARCH) model was introduced by Pan et al. [ 26 ], this model is a very flexible model and, under certain conditions, includes several ARCH/GARCH models.

Based on the researchers’ articles, the symmetric GARCH (1, 1) model has been used widely to forecast the unconditional volatility in the stock market and time series data, and has been able to simulate the asset yield structure and implied volatility structure. Most researchers show that GARCH (1, 1) with a generalized distribution of residual has more advantages in volatility assessment than other models. Conversely, the asymmetry influence in stock market volatility and return analysis was beyond the descriptive power of the asymmetric GARCH models, as the models could capture more specifics. Besides, the asymmetric GARCH models can incompletely measure the effect of positive or negative shocks in stock market return and volatility, and the GARCH (1, 1) comparatively failed to accomplish this fact. In asymmetric effect, the GJR-GARCH model performed better and produced a higher predictable conditional variance during the period of high volatility. In addition, among the asymmetric GARCH models, the reflection of EGARCH model appeared to be superior.

Table 4 has explained the review of bivariate and other multivariate GARCH models. Bivariate model analysis was used to find out if there is a relationship between two different variables. Bivariate model uses one dependent variable and one independent variable. Additionally, the Multivariate GARCH model is a model for two or more time series. Multivariate GARCH models are used to model for forecast volatility of several time series when there are some linkages between them. Multivariate model uses one dependent variable and more than one independent variable. In this case, the current volatility of one time series is influenced not only by its own past innovation, but also by past innovations to volatilities of other time series.

Different literature studies based on bivariate and other multivariate GARCH models.

The most recognizable use of multivariate GARCH models is the analysis of the relations between the volatilities and co-volatilities of several markets. A multivariate model would create a more dependable model than separate univariate models. The vector error correction (VEC) models is the first MGARCH model which was introduced by Bollerslev et al. [ 66 ]. This model is typically related to subsequent formulations. The model can be expressed in the following form: v e c h   ( H t ) = ℂ + ∑ j = 1 q X j   v e c h   ( ϵ t − j   ϵ t − j ' ) + ∑ j = 1 p Y j   v e c h   ( H t − j   )   where v e c h is an operator that stacks the columns of the lower triangular part of its argument square matrix and H t is the covariance matrix of the residuals. The regulated version of the VEC model is the DVEC model and was also recommended by Bollerslev et al. [ 66 ]. Compared to the VEC model, the estimation method proceeded far more smoothly in the DVEC model. The Baba-Engle-Kraft-Kroner (BEKK) model was introduced by Baba et al. [ 67 ] and is an innovative parameterization of the conditional variance matrix H t . The BEKK model accomplishes the positive assurance of the conditional covariance by conveying the model in a way that this property is implied by the model structure. The Constant Conditional Correlation (CCC) model was recommended by Bollerslev [ 68 ], to primarily model the conditional covariance matrix circuitously by estimating the conditional correlation matrix. The Dynamic Conditional Correlation (DCC) model was introduced by Engle [ 69 ] and is a nonlinear mixture of univariate GARCH models and also a generalized variety of the CCC model. To overcome the inconveniency of huge number of parameters, the O-GARCH model was recommended by Alexander and Chibumba [ 70 ] and consequently developed by Alexander [ 71 , 72 ]. Furthermore, a multivariate GARCH model GO-GARCH model was introduced by Bauwens et al. [ 73 ].

The bivariate models showed achieve better in most cases, compared with the univariate models [ 85 ]. MGARCH models could be used for forecasting. Multivariate GARCH modeling delivered a realistic but parsimonious measurement of the variance matrix, confirming its positivity. However, by analyzing the relative forecasting accuracy of the two formulations, BEKK and DCC, it could be deduced that the forecasting performance of the MGARCH models was not always satisfactory. By comparing it with the other multivariate GARCH models, BEKK-GARCH model was comparatively better and flexible but it needed too many parameters for multiple time series. Conversely, for the area of forecasting, the DCC-GARCH model was more parsimonious. In this regard, it was significantly essential to balance parsimony and flexibility when modeling multivariate GARCH models.

The current systematic review has identified 50 research articles for studies on significant aspects of stock market return and volatility, review types, and GARCH model analysis. This paper noticed that all the studies in this review used an investigational research method. A literature review is necessary for scholars, academics, and practitioners. However, assessing various kinds of literature reviews can be challenging. There is no use for outstanding and demanding literature review articles, since if they do not provide a sufficient contribution and something that is recent, it will not be published. Too often, literature reviews are fairly descriptive overviews of research carried out among particular years that draw data on the number of articles published, subject matter covered, authors represented, and maybe methods used, without conducting a deeper investigation. However, conducting a literature review and examining its standard can be challenging, for this reason, this article provides some rigorous literature reviews and, in the long run, to provide better research.

4. Conclusions

Working on a literature review is a challenge. This paper presents a comprehensive literature which has mainly focused on studies on return and volatility of stock market using systematic review methods on various financial markets around the world. This review was driven by researchers’ available recommendations for accompanying systematic literature reviews to search, examine, and categorize all existing and accessible literature on market volatility and returns [ 16 ]. Out of the 435 initial research articles located in renowned electronic databases, 50 appropriate research articles were extracted through cross-reference snowballing. These research articles were evaluated for the quality of proof they produced and were further examined. The raw data were offered by the authors from the literature together with explanations of the data and key fundamental concepts. The outcomes, in this research, delivered future magnitudes to research experts for further work on the return and volatility of stock market.

Stock market return and volatility analysis is a relatively important and emerging field of research. There has been plenty of research on financial market volatility and return because of easily increasing accessibility and availability of researchable data and computing capability. The GARCH type models have a good model on stock market volatilities and returns investigation. The popularity of various GARCH family models has increased in recent times. Every model has its specific strengths and weaknesses and has at influence such a large number of GARCH models. To sum up the reviewed papers, many scholars suggest that the GARCH family model provides better results combined with another statistical technique. Based on the study, much of the research showed that with symmetric information, GARCH (1, 1) could precisely explain the volatilities and returns of the data and when under conditions of asymmetric information, the asymmetric GARCH models would be more appropriate [ 7 , 32 , 40 , 47 , 48 ]. Additionally, few researchers have used multivariate GARCH model statistical techniques for analyzing market volatility and returns to show that a more accurate and better results can be found by multivariate GARCH family models. Asymmetric GARCH models, for instance and like, EGARCH, GJR GARCH, and TGARCH, etc. have been introduced to capture the effect of bad news on the change in volatility of stock returns [ 42 , 58 , 62 ]. This study, although short and particular, attempted to give the scholar a concept of different methods found in this systematic literature review.

With respect to assessing scholars’ articles, the finding was that rankings and specifically only one GARCH model was sensitive to the different stock market volatilities and returns analysis, because the stock market does not have similar characteristics. For this reason, the stock market and model choice are little bit difficult and display little sensitivity to the ranking criterion and estimation methodology, additionally applying software is also another matter. The key challenge for researchers is finding the characteristics in stock market summarization using different kinds of local stock market returns, volatility detection, world stock market volatility, returns, and other data. Additional challenges are modeled by differences of expression between different languages. From an investigation perception, it has been detected that different authors and researchers use special datasets for the valuation of their methods, which may put boundary assessments between research papers.

Whenever there is assurance that scholars build on high accuracy, it will be easier to recognize genuine research gaps instead of merely conducting the same research again and again, so as to progress better and create more appropriate hypotheses and research questions, and, consequently, to raise the standard of research for future generation. This study will be beneficial for researchers, scholars, stock exchanges, regulators, governments, investors, and other concerned parties. The current study also contributes to the scope of further research in the area of stock volatility and returns. The content analysis can be executed taking the literature of the last few decades. It determined that a lot of methodologies like GARCH models, Johansen models, VECM, Impulse response functions, and Granger causality tests are practiced broadly in examining stock market volatility and return analysis across countries as well as among sectors with in a country.

Author Contributions

R.B. and S.W. proposed the research framework together. R.B. collected the data, and wrote the document. S.W. provided important guidance and advice during the process of this research. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

Competitive pricing on online markets: a literature review

• Research Article
• Open access
• Published: 14 June 2022
• Volume 21 , pages 596–622, ( 2022 )

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• Torsten J. Gerpott 1 &
• Jan Berends 1  

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Past reviews of studies concerning competitive pricing strategies lack a unifying approach to interdisciplinarily structure research across economics, marketing management, and operations. This academic void is especially unfortunate for online markets as they show much higher competitive dynamics compared to their offline counterparts. We review 132 articles on competitive posted goods pricing on either e-tail markets or markets in general. Our main contributions are (1) to develop an interdisciplinary framework structuring scholarly work on competitive pricing models and (2) to analyze in how far research on offline markets applies to online retail markets.

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Introduction

Setting prices relative to competitors, i.e., competitive pricing, Footnote 1 is a classical marketing problem which has been studied extensively before the emergence of e-commerce (Talluri and van Ryzin 2004 ; Vives 2001 ). Although literature on online pricing has been reviewed in the past (Ratchford 2009 ), interrelations between pricing and competition were rarely considered systematically (Li et al. 2017 ). As less than 2% of high-impact journal articles address pricing issues (Toni et al. 2017 ), pricing strategies do not receive proper research attention according to their practical relevance. This research gap holds even more for competitive pricing. In the past, the monopolistic assumption that demand for homogeneous goods mostly depends on prices set by a single firm may have been a viable simplification since price comparisons were difficult. Today, consumer search costs Footnote 2 shrink as the prices of most goods can be compared on relatively transparent online markets. Therefore, demand is increasingly influenced by prices of competitors which therefore should not be ignored (Lin and Sibdari 2009 ).

In the early 1990s, few people anticipated that business-to-consumer (B2C) online goods retail markets Footnote 3 would develop from a dubious alternative to conventional “brick-and-mortar” retail stores to an omnipresent distribution channel for all kinds of products in less than two decades (Balasubramanian 1998 ; Boardman and McCormick 2018 ). In 2000, e-commerce accounted for a mere 1% of overall retail sales. In 2025, e-retail sales are projected to account for nearly 25% of global retail sales (Lebow 2019 ). Traditional offline channels are nowadays typically complemented by online technologies (Gao and Su 2018 ). With digitization of various societal sectors in general and the COVID-19 pandemic in particular, the shift toward online channels is unlikely to stop in the future. Besides direct online shops, two-thirds of e-commerce sales are sold through online marketplaces/platforms like Alibaba, Amazon or eBay (Young 2022 ). The marketplace operator acts as an intermediary (two-sided platform) who matches demand (online consumers) with supply (retailers). Whereas the retailer retains control over product assortment and prices, he has to pay a commission to the marketplace operator (Hagiu 2007 ). However, these intermediaries often act as sellers themselves, thereby posing direct competition to retailers who have to decide between direct or marketplace channels (Ryan et al. 2012 ).

Online consumer markets fundamentally differ from offline settings (Chintagunta et al. 2012 ; Lee and Tan 2003 ; Scarpi et al. 2014 ; Smith and Brynjolfsson 2001 ). Factors which make competition even more prevalent for online than for offline markets are summarized in Table 1 .

To date, a number of scholarly articles reviews various aspects of pricing under competition or online pricing (Boer 2015a ; Chen and Chen 2015 ; Cheng 2017 ; Kopalle et al. 2009 ; Ratchford 2009 ; Vives 2001 ). Vives ( 2001 ) provides an overview of the history of pricing theory and its evolution from the early work of Bertrand ( 1883 ) who studied a duopoly with unconstrained capacity and identical products to Dudey ( 1992 ) who set the foundation for today’s dynamic pricing Footnote 4 research with constrained capacities and a finite sales horizon. Ratchford ( 2009 ) reviews the influence of online markets on pricing strategies. Although he depicts factors shaping the competitive environment of online markets and compares online versus offline channels, he does not include competitive strategies specifically. This also holds for review papers on dynamic pricing which treat competition rather novercally (Boer 2015a ; Gönsch et al. 2009 ). With emphasis on mobility barriers, multimarket contact and mutual forbearance, Cheng ( 2017 ) studies competition mechanisms across strategic groups. Kopalle et al. ( 2009 ) discuss competitive effects in retail focusing on different aspects such as manufacturer interaction and cross-channel competition. To the best of our knowledge, Chen and Chen ( 2015 ) are the only scholars who review existing competitive pricing research by classifying model characteristics along product uniqueness (identical vs. differentiated), type of customer (myopic vs. strategic), pricing policy (contingent vs. preannounced) and number of competitors (duopoly vs. oligopoly). However, competition is only one of three pricing problems they analyze forcing them to reduce scope and depth and to exclude online peculiarities. In addition, significant competitive pricing contributions were published since 2015 (chapter 2.2). Overall, given the limitations of previous reviews of the pricing literature makes revisiting the current state of research a worthwhile undertaking.

Most often, competitive pricing literature uses simplifying assumptions limiting the applicability of presented models. The simplifications are required to circumvent challenges like the curse of dimensionality (Harsha et al. 2019 ; Kastius and Schlosser 2022 ; Li et al. 2017 ; Schlosser and Boissier 2018 ), endogeneity problems (Cebollada et al. 2019 ; Chu et al. 2008 ; Fisher et al. 2018 ; Villas-Boas and Winer 1999 ), uncertain information (Adida and Perakis 2010 ; e.g., Bertsimas and Perakis 2006 ; Chung et al. 2012 ; Ferreira et al. 2016 ; Keskin and Zeevi 2017 ; Shugan 2002 ) and simultaneity bias (Li et al. 2017 ). As a consequence, early work on pricing strategies with competition was restricted to theoretical discussions (Caplin and Nalebuff 1991 ; Mizuno 2003 ; Perloff and Salop 1985 ). This holds especially true in combination with other practical circumstances such as capacity constraints, time-varying demand or a finite selling horizon (Gallego and Hu 2014 ).

Armstrong and Green ( 2007 ) find empirical evidence that competitive pricing, especially for the sake of gaining market share, harms profitability. Similarly, some researchers cursorily ascribe competitor-based pricing as a sign of a poor management because it signals a lack of capabilities to set prices independently (Larson 2019 ). Revenue management researchers therefore often assume that monopoly pricing models implicitly capture the dynamic effects of competition. The so-called market response hypothesis is the key rationale to neglect the effects of competition altogether (Phillips 2021 ; Talluri and van Ryzin 2004 ). According to this reasoning, competition does not have to be considered as all relevant effects are already included in historical sales data. However, this intuitive argument can be easily rebutted as Simon ( 1979 ) already showed that price elasticities change over time. Furthermore, Cooper et al. ( 2015 ) study the validity of the market response hypothesis and conclude that this monopolistic view is rarely adequate. Monopolistic pricing models can only be applied to stable markets with little time-varying demand and little expected competitive reactions.

Detrimental outcomes of ignoring competition in pricing strategies are shown by Anufriev et al. ( 2013 ), Bischi et al. ( 2004 ), Isler and Imhof ( 2008 ), Schinkel et al. ( 2002 ), and Tuinstra (2004). The negative effects are even more harmful in fierce competitive settings such as situations with a high number of competitors or price sensitive customers (van de Geer et al. 2019 ). Empirical evidence on the influence of competition on pricing decisions is provided by Richards and Hamilton ( 2006 ) who find that retailers compete on price and variety for market share. Li et al. ( 2017 ) observe that competition-based variables explained 30.2% of hotel price variations in New York—compared to 22.3% attributed to demand-side variables. Similarly, Hinterhuber ( 2008 ) assesses competitor-based pricing as a dominant strategy from a practical perspective. Li et al. ( 2008 ) argue that because of its relevance, competition should be considered in operational revenue management and not be treated stepmotherly as an abstract strategic constraint.

Although striving to simplify pricing models is desirable, researchers should thus not simply ignore effects of competition on price setting in a non-monopolistic (online) world. Blindly pegging pricing strategies to competitors or undercutting competitors to gain market share may favor detrimental price wars and not profit-maximizing market structures. Nevertheless, no significant market player can operate isolated on online markets—decisions made always affect competing firms and consumer demand (Chiang et al. 2007 ). In such dynamic markets (chapter 3.3), competition must be considered with time-varying attributes (Schlosser et al. 2016 ).

Against this background, we suggest a conceptual framework to structure research covering competitive online pricing. It can serve scholars as a map to direct future research on the one hand and provide practicing managers with a guide to locate relevant pricing contributions on the other hand. Although the framework can be applied to a variety of markets with competitive dynamics, we concentrate our review on research covering B2C online goods retail markets. Thus, related research with a focus on auction pricing, multichannel peculiarities, behavioral pricing and multi-dimensional pricing approaches such as Everything-as-a-Service (XaaS) or bundle pricing is only assessed when findings are crucial to the competition-related discussion. In the remainder, we proceed as follows. The next chapter provides a descriptive overview of the competitive pricing literature for the subsequent discussion. Chapter 3 puts the identified literature into the perspective of online retail markets considering product and environmental characteristics. Section 4 concludes with practical implications and directions for future research.

Overview of competitive pricing research

Initially, properties of the reviewed literature are briefly summarized. Besides (a) the journal representation, (b) the historical development of online market considerations and (c) research domains, we classify research according to (d) the geographical and industry context as well as (e) research design and empirical foundation.

We identified relevant references through a semi-structured multi-pronged search strategy. Following Tranfield et al. ( 2003 ), we firstly screened the literature reviews mentioned in chapter 1 to obtain an overview of existing research streams. Second, we created a set of potentially relevant contributions by searching multiple keywords in the journal databases EBSCO, Scopus and Web of Science (c.f. Baloglu and Assante 1999 ). Footnote 5 Third, high-impact journals (see Appendix 1) in the academic fields economics, marketing management, and operations were screened. With focus on highly cited (> 10 citations in Scopus), recent (published later than 2000) research, we identified an initial sample of 996 unique papers. Fourth, we studied the abstract and skimmed the text of all papers for relevance to competitive online pricing, reducing our initial set to 174 papers. Fifth, we screened the references of the papers and identified literature cited which we not already included in our set. Sixth, especially for research areas with limited coverage in peer-reviewed journals, we uncovered gray literature through searches with Google Scholar. As a result, this study concentrates on papers published between 2000 and 2022 and only sparsely utilizes literature from the pre-internet era. The final sample of the papers with relevance to competitive B2C online pricing encompasses 132 entries. A complete list of the papers reviewed in great depth is provided in Appendix 2. 94% are peer-reviewed articles. Book chapters, conference papers and preprint/working papers each account for 2%.

Journal representation

Competitive pricing literature is widely dispersed over a broad range of journals as roughly half of the articles considered are from journals with less than three articles in our review. Notably, journals with a higher density of competitive pricing contributions are from the fields of operations, economics or are interdisciplinary. Table 2 reports the distribution of articles among the journals with the highest representation. In addition, it provides the considered articles subject to a content analysis in chapter 3.

Online pricing contributions over time

Between 1976 and the end of the second millennium, the number of papers on competitive pricing in an internet context is naturally limited (Fig.  1 ). Parallel to the dissemination of online use among residential households, interest of researchers in online pricing in a competitive environment started to take off. 71.8% of the papers published from 2015 to 2022 consider online settings specifically. The corresponding statistic from 2000 to 2005 amounts to 43.8%.

Competitive pricing literature and its consideration of online peculiarities accumulated by year

Development of research domains

Competitive pricing literature typically can be assigned to one of the following research domains:

The economics domain takes a market perspective across individual firms. It elaborates on the existence and uniqueness of competitive equilibria also including all subjects regarding econometrics.

The marketing management domain analyzes competitive pricing problems from the perspective of a single firm with a focus on customer reactions to pricing decisions. It includes all subjects linked to marketing, strategy, business, international, technology, innovation, and general management.

The operations domain considers quantitative pricing solutions for, among others, quantity planning, choice of distribution channels, and detection of algorithm driven price collusion. It includes all subjects regarding computer science, industrial and manufacturing engineering, and mathematics.

Separating the last 47 years of competitive pricing research into three intervals, all reviewed papers are assigned to their most affiliated research domain. Although the domains are similarly represented in our review (see Fig.  2 ), we see differences in their temporal change. Whereas rather theoretical economic subjects are covered relatively constant over time, more practice-oriented marketing management and operations subjects gained momentum since 2000. This suggests a shift from model conceptualization toward applicable research, frequently based on empirical data.

Distribution of competitive pricing literature over research domain and time interval

Geographical and industry context

As the origin of revenue management lies in transportation and hospitality optimization problems, one could expect that competitive pricing research also originates in these dynamic sectors. However, our analysis reveals a different picture: Almost half of the papers in our review do not concentrate on a specific industry. Besides, most industry-specific competitive pricing articles focus on retail, with 38% concentrating on the retail industry versus 8% and 4% on transportation and hospitality, respectively (see Fig.  3 ). This supports our proposition in chapter 1 that effects of competition on industry-specific pricing are particularly relevant for online markets.

Competitive pricing research by focal industry and location of lead authors’ institution

Competitive pricing literature is predominantly driven by researchers employed by U.S. institutions (60%). The remaining 40% consist of Europe (19%), Asia (17%) and Canada (4%).

Research approach

A lack of empirical testing is an issue that hampers competitive pricing research. Liozu ( 2015 ) reported that only 15% of general pricing literature include empirical data. For competitive pricing, the situation appears even more aggravated. In addition to parameters such as price elasticities and stock levels of the company under study, comprehensive, real-time information of other market participants is crucial to add practical value.

For instance, to solve a simple Bertrand equilibrium, Footnote 6 full information of all competitors is needed, which is rarely available in real-life settings. Therefore, many problems covered in the literature are of a theoretical nature. In accordance with Liozu ( 2015 ), we find that only 18% of reviewed articles use empirical evidence to validate hypotheses. An additional 23% strive to ameliorate this shortage through simulation data and numerical examples. The remaining 59% fail to bring any empirical evidence or numerical examples.

As can be taken from Fig.  4 , missing empirical support is particularly prevalent for equilibrium models which use empirical data in only 7% of all papers.

Competitive pricing research by research design and empirical validation

Competitive pricing on online markets

In this chapter, we assess the applicability of competitive pricing work to online markets. Typical characteristics of competitive B2C pricing models were derived from literature described in chapter 2. Competitive pricing literature can be classified along four characteristics depicted in Table 3 that form the market environment in which firms compete for consumer demand.

In the remainder of chapter 3, we discuss the four key questions in more depth and elaborate on their applicability to online retail markets.

Product similarity

In general, products in competitive pricing models are either identical (homogeneous) or differentiated by at least one quality parameter (heterogeneous). In case of homogeneous products, pricing is the only purchase decision variable—a perfectly competitive setting (Chen and Chen 2015 ). However, many firms strive to differentiate their products as this shifts the focus from the price as competitive lever to other product-related features (Afeche et al. 2011 ; Boyd and Bilegan 2003 ; Thomadsen 2007 ). According to Lancaster ( 1979 ), there are two types of product differentiation: vertical and horizontal differentiation. Vertical differentiation Footnote 7 encompasses all product distinctions which are objectively measurable and quantifiable regarding their quality level. Horizontal differentiation Footnote 8 can manifest in many variants and includes all product-related aspects which cannot be quantified according to their quality levels. Footnote 9 A key difference in the modeling of substitutable yet differentiated versus identical goods is that customers have heterogenous preferences among products. Footnote 10 A recent stream of literature approaches unknown differentiation criteria by assessing online consumer-generated content (DeSarbo and Grewal 2007 ; Lee and Bradlow 2011 ; Netzer et al. 2012 ; Ringel and Skiera 2016 ; Won et al. 2022 ).

Besides the chosen price level, Cachon and Harker ( 2002 ) argue that firms compete with the operational performance level offered and perceived, i.e., service level in online retail, to differentiate an otherwise homogenous offering. In situations, where resellers with comparable service and shipping policies offer similar products, price is a major decision variable for potential buyers (Yang et al. 2020 ). Often, e-tailers do not possess the right to exclusively distribute a certain product. For example, Samsung’s Galaxy S21 5G was offered by 69 resellers on the German price comparison website Idealo.de. Footnote 11 As some products in e-tail can be differentiated and others cannot, both identical and differentiated product research have their raison d’être for competitive online pricing.

Most competitive pricing models only address the effects of single-product settings. This simplification is reasonable if there is no interdependence between products of an e-tailer (Gönsch et al. 2009 ). Taking up on the smartphone example, the prices of close substitutes, such as Huawei’s P30 Pro, nonetheless have an impact on the demand of Samsung’s Galaxy S21 5G. To further extent product differentiation, price models have to incorporate multi-product pricing problems in non-cooperative settings (Chen and Chen 2015 ). Such models have to account not only for demand impact of directly competing products but also for synergies, cannibalization/substitution effects of (own) differentiated goods. Although there is a recent research stream regarding product assortment (Besbes and Sauré 2016 ; Federgruen and Hu 2015 ; Heese and Martínez-de-Albéniz 2018 ; Nip et al. 2020 ; Sun and Gilbert 2019 ), multi-product work is still underdeveloped. Thus, competitive multi-product pricing constitutes an area which should be addressed in future research.

Product durability

The durability of products is an important feature to differentiate between competitive pricing model types. Durable (non-perishable) products do not have an expiration date, for example consumer durables such as household appliances. Perishable products can only be sold for a limited time interval and have a finite sales horizon. After expiration date, unused capacity is lost or significantly devalued to a salvage value. Footnote 12 Combined with limited capacities, the firm objective is thus most often to maximize turnover under capacity constraints and finite sales horizon (Gallego and van Ryzin 1997 ; McGill and van Ryzin 1999 ; Weatherford and Bodily 1992 ).

Perishability can be of relevance for products with seasonality effects or short product life cycles (i.e., finite selling horizon) such as apparel, food groceries or winter sports equipment. This is especially relevant because online retailers of perishable products are severely restricted in their shipment, return handle policies and supply chain length (Cattani et al. 2007 ). Sellers cannot replenish their inventory after the planning phase and cannot retain goods for future sales periods (Perakis and Sood 2006 ). Some products like apparel—albeit reducing in value after a selling season—still have a certain salvage value and can be sold at reduced prices (Anand and Girotra 2007 ).

It depends on the type of product to decide whether perishability should be included in competitive pricing models. There is a fundamental distinction in the underlying optimization objective for models with or without perishability. Whereas models with perishable products tend to focus on revenue maximization over a definite short-term time horizon, models with durable products tend to focus on profit maximization over an indefinite or at least long-term time horizon by balancing current revenues of existing and future revenues of new customers. To account for this trade-off, models with durable products need to discount future cash flows incorporating time value of money, stock-keeping, opportunity and other costs related to prolonged sales (Farias et al. 2012 ). To conclude, perishability cannot be treated as an extension to durable models but rather as a separate class of pricing models. Depending on the product and/or setting in focus, both are relevant for online retailing. Further research could investigate the performance of models with and without consideration of perishability in various (online) settings to determine when it is appropriate to use which class of pricing models. Also, an interesting field of future studies arises around the question which instruments (e.g., service differentiations or price diffusion) are used by online retailers to differentiate otherwise homogeneous offerings.

Time dependence

A key differentiator of competitive pricing models is the consideration of either a static (time-independent) setup with definite equilibrium or a dynamic (time-dependent) constellation with changing environmental factors and equilibria. Albeit static pricing models have no time component, many consist of multiple stages to investigate the interplay of different factors. Footnote 13 In contrast, dynamic models allow for varying competitive (re-)actions over time. Footnote 14 Within the latter category, there are models with a finite (Afeche et al. 2011 ; Levin et al. 2008 ; Liu and Zhang 2013 ; Yang and Xia 2013 ) and an infinite (Anderson and Kumar 2007 ; Li et al. 2017 ; Schlosser and Richly 2019 ; Villas-Boas and Winer 1999 ; Weintraub et al. 2008 ) time horizon.

Historically, competitive pricing models assumed fixed prices over the considered time horizon. Limited computational power made it impossible to appropriately estimate models dynamically due to dimensionality issues (Schlosser and Boissier 2018 ). A lack of reliable demand information, high menu and investment costs to implement dynamic approaches were additional reasons why pricing models remained inherently static without incorporating changing competitive responses (Ferreira et al. 2016 ). The focus in retail has conventionally rather been on long-term profit optimization and to a lesser degree on dynamically changing price optimizations (Elmaghraby and Keskinocak 2003 ).

The literature disagrees on whether firms should opt for static or dynamic pricing strategies. A static environment allows to simplify and concentrate on a specific topic such as equilibrium discussions. For instance, Lal and Rao ( 1997 ) study success factors of everyday low pricing and derive conditions for a perfect Nash equilibrium between an everyday low price retailer and a retailer with promotional pricing. With Zara as an example for a company with a successful static pricing strategy, Liu and Zhang ( 2013 ) argue that with the presence of strategic customers who prolong sales in anticipation of price decreases, firms might even be better off to deploy static over dynamic price setting processes. Studying the time-variant pricing plans in electricity markets, Schlereth et al. ( 2018 ) suggest that consumers might prefer static over dynamic pricing because of factors like choice confusion, lack of trust in price fairness, perceived economical risk or perceived additional effort. Further support for a static pricing strategy is found in Cachon and Feldman ( 2010 ) and Hall et al. ( 2009 ).

Nevertheless, to generalize that static should strictly be preferred over dynamic pricing models could be short-sighted. Firms cannot generally infer future behavior of competitors from past observations to assess how competitive (re-) actions may influence the optimal pricing policy (Boer 2015b ). Corresponding to the surge of revenue management systems in the airline industry during the 70s and 80s, increased price and demand transparency, low menu costs and an abundance of decision support software created fierce competition among online retailers (Fisher et al. 2018 ). Taking up on the above mentioned example by Liu and Zhang ( 2013 ), Caro and Gallien ( 2012 ) show that even Zara does not solely rely on static pricing. They supported Zara’s pricing team in designing and implementing a dynamic clearance pricing optimization system—to generate a competitive advantage in addition to the fast-fashion retail model Zara mainly pursues (Caro 2012 ). Zhang et al. ( 2017 ) discuss various duopoly pricing models with static and dynamic pricing under advertising. They find that market surplus is highest when one firm prices dynamically, profiting from the static behavior of the other. Chung et al. ( 2012 ) provide numerical evidence that a dynamic pricing model with an appropriately specified demand estimation always outperforms static pricing strategies—also in settings with incomplete information. Xu and Hopp ( 2006 ) show that dynamic pricing outperforms preannounced pricing, especially with effective inventory management and elastic demand. Further support for advantages of dynamic pricing can be found by Popescu ( 2015 ), Wang and Sun ( 2019 ), and Zhang et al. ( 2018b ). Empirical evidence of the negative consequences of sticking to a static strategy in a changing environment is found in the cases of Nokia, Kodak, and Xerox.

While some scholars distinguish between discrete and continuous dynamic pricing systems (Vinod 2020 ), we suggest to classify dynamic pricing models according to their level of sophistication into two evolutionary stages: the (in e-commerce widely applied) manual rule-based pricing approach and the data-driven algorithmic optimization approach (Popescu 2015 ; Le Chen et al. 2016 ). Footnote 15 For the rule-based approach, “if-then-else rules” are defined and updated manually. Footnote 16 However, the mere number of stock-keeping units (SKUs) in today’s retailer offerings aggravate the initial setup and handling of rule-based pricing and make real-time adjustments unmanageable (Schlosser and Boissier 2018 ). In addition, rule-based approaches are rather subjective than sufficiently data-driven. Faced with a large range of SKUs, competitor responses and heterogeneous demand elasticities, canceling out the human decision-making process on an operational level is the next evolutionary step for competitive pricing systems (Calvano et al. 2020 ). Data-driven algorithmic pricing strategies use observable market Footnote 17 data to predict sales probabilities based on consumer demand and competitive responses (Schlosser and Richly 2019 ).

As online marketplaces benefit from an increased number of retailers on their platforms, they typically support sellers to establish automated dynamic pricing systems (Kachani et al. 2010 ). Footnote 18 However, Schlosser and Richly ( 2019 ) claim that current dynamic pricing systems are not able to deal with the complexity of competitor-based pricing and therefore most often ignore competition altogether or solely rely on manually adjusted rule-based mechanics. Challenges include the indefinite spectrum of changing competitor strategies, asymmetric access to competitor knowledge, a large solution space under limited information and the black-box character of dynamic systems, which exacerbates an intervention in case of a pricing system malfunction. Besides, researchers did not yet identify an algorithm which consistently outperforms other methodologies in competitive situations. Instead, it depends on the specific setting and other competitors’ pricing behavior to assess which pricing algorithm is optimal (van de Geer et al. 2019 ) exacerbating the application of such systems.

Reflecting the literature findings for both static and dynamic pricing strategies, we conclude that pricing managers should develop dynamic pricing models in most e-commerce situations. As long as demand and competitor price responses vary over time on online markets, dynamic models are naturally superior to time-independent approaches. Static models on the other hand are only appropriate in market constellations with little time-varying demand and competitor behavior. As static research can be expected to remain a vivid field of literature, further research with regard to the transferability of static models to dynamic settings is desirable. In addition, more research is needed that helps to better understand the implications of widely applied rule-based dynamic pricing methods and their transition toward algorithmic approaches (Boer 2015a ; van de Geer et al. 2019 ; Kastius and Schlosser 2022 ; Könönen 2006 ).

Market structure

The market structure describes the number of competing firms such as duopoly or oligopoly in a demand setting with an indefinite number of consumers. 60% of the reviewed papers studied duopolies, 49% oligopolies, 7% monopolistic competition, and 3% perfect competition. Footnote 19

Especially for research in the economics stream, many papers assume a perfectly competitive market. Pricing research with perfectly competitive markets (e.g., van Mieghem and Dada 1999 or Yang and Xia 2013 ) is likely to be of very limited value to online retailers. Building on the notion of Diamond ( 1971 ), Salop ( 1976 ) argues that if customers have positive information gathering costs, no perfect competition can occur as firms have room to slightly increase prices without losing demand. Christen ( 2005 ) found evidence that even with strong competition and low information costs, cost uncertainty could decrease the detrimental effect of competition for sellers and could increase prices above Bertrand levels. Similarly, Bryant ( 1980 ) showed that perfect competition is not possible in a market with uncertain demand, even if the number of firms is large and customers have no search costs. Rather, price dispersion reflects uncertain demand (Borenstein and Rose Nancy L. 1994 ; Cavallo 2018 ; Clemons et al. 2002 ; Obermeyer et al. 2013 ; Wang et al. 2021 ). Israeli et al. ( 2022 ) empirically show that the market power of individual firms does not only depend on the number and intensity of competitors but also on the firm’s ability to adjust prices in response to varying inventory levels of product substitutes, especially with low consumer search costs. This is of relevance for e-commerce as e-tailers could exploit this dependence by incorporating competitors’ stock levels into pricing decisions (Fisher et al. 2018 ).

Some papers discuss (quasi) monopolistic competition (e.g., Xu and Hopp 2006 ) in which small firms charge the (higher) monopoly price rather than the (lower) competitive price. From an empirical study in the U.S. airline industry, Chen (2018) concludes that, as firms can price discriminate late-arriving consumers, competition is softened, profits are increased, and the only single-price equilibrium could be at the monopoly price. This supports Lal and Sarvary ( 1999 ) who show that online retailers enjoy a certain amount of monopoly power in cases where buyers cannot switch suppliers for repeated purchases (e.g., technical incompatibility reasons). In such cases, switching costs could increase online prices (Chen and Riordan 2008 ). However, this contradicts Deck and Gu ( 2012 ) who empirically show that, although the distribution of buyer values of competing products might theoretically lead to higher prices through competition, intensity of competition rarely allows for an occurrence of this phenomenon in e-tail settings.

Although duopoly settings can serve to assess the relevant strength of pricing strategies, which is not directly possible for oligopoly markets due to the curse of dimensionality (Kastius and Schlosser 2022 ), they cannot be transferred to more competitive environments (van de Geer et al. 2019 ). In online retail, a duopoly market structure is a rare exemption. Like for perfectly competitive markets, findings of duopoly research must be carefully assessed in terms of their applicability to online retail oligopolies.

Bresnahan and Reiss ( 1991 ) found empirical evidence that markets with an increasing number of dealers have lower prices than in less competitive market structures such as monopolies or duopolies. Although applicable to many online retail markets, where retailers face dozens, if not hundreds of thousands of competitors (Schlosser and Boissier 2018 ), few research attention is currently given toward a structure with a large number of competitors in an imperfect market (cf. Li et al. 2017 ). A way to assess the current competitive structure of markets is the utilization of online consumer-generated content such as forum entries (Netzer et al. 2012 ; Won et al. 2022 ) or clickstream data (Ringel and Skiera 2016 ) and actual sales data (Kim et al. 2011 ).

In many countries with well-developed B2C online markets, one or few major retailers dominate on an oligopolistic market. For example, the top three online retailers in the United States accounted for over 50% of the revenue generated on the national e-commerce market in 2021. Footnote 20 Due to lower locational limitations in conjunction with substantial economies of scale and scope, online markets tend to become more concentrated than their offline counterparts (Borsenberger 2015 ). Although one could expect that increased market transparency leads to a higher intensity of competition (Cao and Gruca 2003 ), limiting the market power of established firms and leaving growth potential for smaller firms (Zhao et al. 2017 ), it appears reasonable to predict that most online markets will ultimately resemble an oligopoly setting with a with a relatively small number of players—enabling increased tacit pricing algorithm collusion in the future (Calvano et al. 2020 ). With few exceptions (e.g., Noel 2007 ), there is little research (Brown and Goolsbee 2002 ; Wang et al. 2021 ; Cavallo 2018 ) exploring what type of competitor-based pricing strategies are used and what competitive dynamics are found on e-tail markets. Thus, more research is needed to investigate the current state of market structure and intensity of competition in today’s e-commerce markets as drivers of the selection and the outcomes of pricing approaches.

Implications and directions

We contribute to the literature by providing an interdisciplinary review of competitive online retail research. Competitive pricing problems can most often be assigned to one of the academic fields of economics, marketing management or operations. In a first step, this review offered a descriptive portrayal of the relevant literature. Motivated by practical issues and common features in competitive pricing research, we then structured competitive pricing contributions along four properties of pricing models. First, do firms compete with identical or quality differentiated products? Second, are products to be considered as perishable or durable goods? Third, is the market setting to be regarded time-independent or not? Fourth, which market structure prevails on e-tail markets? The framework is derived from an analysis of pricing research not exclusively restricted to online retail settings. Therefore, it could be extended to other online or offline markets, with little loss of generalizability.

We focused on e-tail markets because the relevance of competition for pricing strategies is disproportionally higher in such environments. On e-tail online markets, products are rarely offered exclusively so that the likelihood of substitutive competition is high. Nevertheless, products can be differentiated through other factors than prices such as generous shipping, customer retention (e.g., loyalty reward programs) or return and issue handling policies. With a look on product similarities, accounting for product interdependencies and multi-product situations are important improvements of prevailing pricing models. Second, pricing models with both a focus on perishable and/or durable products are relevant on e-tail markets. However, further research is needed exploring which of the respective perishability considerations are appropriate for different settings. Third, we conclude that, albeit time-independent static models may occasionally serve to simplify pricing issues, dynamic models outperform their static counterparts in constantly changing market environments such as in e-commerce. Fourth, we show that in most practical settings, online markets resemble either an oligopolistic market structure or a structure with many firms under imperfect competition. Thus, future research should consider these two “real” competitive settings instead of further looking at simplifying market structures such as monopolistic or duopolistic competition. This should ease a transfer of theoretical insights into practical applications. To sum, firms should be able to improve their competitive position by developing a profit optimizing dynamic pricing strategy for identical products in an oligopolistic setting with a varying number and relevance of competitors.

Due to space limitations, we had to focus on competitive pricing model characteristics related to four overall product and market attributes. Thus, more work is needed on other characteristics of competitive pricing models, particularly firm- and consumer-related characteristics. Firm-related characteristics encompass various additional properties of interacting firms (e.g., similarity or capacity constraints). Similarly, consumer-related characteristics entail further properties of interacting buyers (e.g., certainty, discreteness, sophistication, and homogeneity of demand).

In the selection process of literature, this study only considered papers in peer-reviewed journals and conference proceedings in English. Subsequent research could complement our findings by including industry-funded, unpublished and non-peer-reviewed articles, also in other languages. In addition, we do not claim that our research captures all competitive pricing publications of the considered field. As our study spans almost 50 years of a frequently discussed topic in the domains of economics, marketing management, and operations, we had to constrain the scope to the most influential work. Although we mutually evaluated our selection decisions and consulted outside peers for validation and further input, we cannot eliminate the element of subjectivity. Consequently, other authors could have selected slightly different papers. However, this shortcoming is unlikely to significantly affect our results as our literature selection was derived from a broad array of competitive pricing research and would therefore be only marginally influenced by a few omitted articles.

Competitive pricing includes all activities and processes to price products with the consideration of competitors. This does not only include rigidly pegging prices to competitor prices but rather a comprehensive consideration of current and expected price (re-)actions of competing firms to sustainably ensure profit maximization. In this article, the terms competitive pricing, competitor-oriented pricing and competitor-based pricing are used synonymously.

Search costs are defined as the costs of time and resources to acquire information with respect to price, assortment, and quality characteristics of the goods provided by different sellers. The internet dramatically reduces search costs through price comparison websites such as Google Shopping, Shopzilla (USA) or Idealo (Germany).

Business-to-consumer (B2C) online retail sales encompass all forms of electronic commerce markets in which residential end customers can directly buy goods from a seller over the internet through a web browser or a mobile app. In this paper, the terms business-to-consumer (B2C) online goods retail, online retail, e-tail, e-retail and e-commerce markets are used synonymously.

In contrast to classical quantity-based revenue management, dynamic pricing, also known as surge pricing, is the practice of adjusting prices according to current market demand (Boer 2015a ). Revenue management, also known as yield management, is a type of price discrimination which originates from the airline and hospitality industries. Typically, revenue management models assume fixed capacities, low marginal cost, varying demand and highly perishable inventory (Talluri and van Ryzin 2004 ).

Keywords used for abstract, title and keyword screening were “competitive pricing”, “competitor-based pricing”, “competition” AND “pricing”. To find literature for online pricing in particular, the search was combined with the keywords “online”, “e-retail”, “ecommerce” and “e-commerce”. Whereas the combination was scanned in great depths, the three competitive keywords were screened for influential papers with implications for online markets.

Bertrand competition is a simplified model of competition to explain price competition among (at least) two firms for an identical product at equal unit cost of production. Prices are set simultaneously, and consumers buy without search costs from the firm with the lowest price. When all firms charge the same price, consumer demand is split evenly between firms. A firm is willing to supply unlimited amounts of quantities above the unit cost of production and is indifferent to supply at unit cost as it will earn zero profit. The only Bertrand equilibrium exists when prices are equal to unit cost (i.e., competitive price) as each firm otherwise would have an incentive to undercut all other competitors and thereby rake in the entire market demand. Therefore, there can be no equilibrium at prices above the competitive price and price dispersion cannot occur.

In vertical differentiation, consumer choice depends on specific quality levels of product attributes. At the same price, all consumers prefer one product over other products, for example because of superior design. In the simplest form, products differ in one attribute and customers are willing to pay marginal increments of this attribute.

In horizontal differentiation, consumer choice depends on preferences for products. At the same price, some customers would buy one product and others other products.

We consider product differentiation only to product-related differentiation attributes. However, in competitive pricing literature firm-related differences such as firm loyalty or distribution channels are occasionally attributed to differentiation. For example, Abhishek et al. ( 2016 ) differentiate online distribution channels of otherwise homogeneous products and firms.

Heterogeneous customer preferences are a key requirement for product differentiation, otherwise price constitutes the only driver of the buying decision (Li et al. 2017 ). Without heterogeneity in consumers’ marginal willingness to pay for different levels of product quality, there can be no product differentiation (Pigou 1920 ).

Accessed 14–03-2022.

Salvage value is defined as the residual cash-flow of a good after its expiration date.

For example, game settings on the foundation of Stackelberg games necessarily comprise ≥ 2 stages (Geng and Mallik 2007 ; Gupta et al.; Wang et al. 2020 ; Yao and Liu 2005 ). Another example would be Anand and Girotra ( 2007 ) who propose a 3-stage model in which they include the supply chain configuration and determination of production quantities in addition to the actual price setting.

As such, we classify n-stage models as dynamic models when not all individual stages serve a specific time-independent purpose.

For instance, the Brandenburg consumer advice center (Verbraucherzentrale Brandenburg) examined dynamic price differentiation in online retail and found that 15 of the 16 observed German online shops dynamically changed their prices in 2018 (Dautzenberg et al. 2018 ).

A typical rule would be to set prices always x% lower than competitor prices up to a certain profit threshold.

Observable market data include price and stock levels of competitors (Fisher et al. 2018 ) or clickstream and keyword data of customers (Li et al. 2017 ).

Examples for support programs by online marketplaces are Amazon’s Seller Central ( https://sellercentral.amazon.com/gp/help/external/G201994820?language=en_US&ref=efph_G201994820_cont_43381 ; Accessed 14–03-2022), eBay’s Seller Tools ( https://pages.ebay.com/sell/automation.html ; Accessed 14–03-2022) or Idealo’s Partner Program ( https://partner.idealo.com/de ; Accessed 14–03-2022).

Cumulatively, these values exceed 100% as some articles discussed more than one kind of market structure. Applied by economists to simplify real markets as the foundation of price theory, perfect competition relates to a market structure which is controlled entirely by market forces and not by individual firms. Instead, individual firms only act as price takers and cannot earn any economic profit. The conditions for a perfect competition, such as full information, homogeneous products, fully rational buyers, no scale, network or externality effects, no entry barriers, and no transaction costs, are rarely attainable in practical settings (Stigler 1957 ). If not all conditions for perfect competition are fulfilled, the market structure is imperfect which applies to most practical settings. Besides a monopoly with only one seller on the market, three market structures with competing firms exist: Monopolistic, duopolistic, and oligopolistic competition. An oligopoly is characterized by a small number of firms in which the behavior of one firm drives the actions of other firms. A duopoly is a particular case of an oligopoly in which two firms control the market. An extreme case of imperfect competition is (quasi) monopolistic competition in which products are differentiated and firms maintain a certain spare capacity giving them a certain degree of pricing power to maximize their (short-term) profits. In consequence, prices can be higher than corresponding the competitive (Bertrand) price (Vives 2001 ).

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Gerpott, T.J., Berends, J. Competitive pricing on online markets: a literature review. J Revenue Pricing Manag 21 , 596–622 (2022). https://doi.org/10.1057/s41272-022-00390-x

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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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See an example

Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
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• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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McCombes, S. (2023, September 11). How to Write a Literature Review | Guide, Examples, & Templates. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/dissertation/literature-review/

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Market orientation and hotel industry: literature review and implications for periods of market turmoil.

1. Introduction

2. literature review, 3. data and methods, 3.2. methods, 4.1. descriptive analysis, 4.2. co-citation analysis, 5. discussion, future research, and conclusions, 5.1. discussion, 5.2. future research proposals, 5.3. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Sampaio, C.; Régio, M. Market Orientation and Hotel Industry: Literature Review and Implications for Periods of Market Turmoil. J. Risk Financial Manag. 2022 , 15 , 497. https://doi.org/10.3390/jrfm15110497

Sampaio C, Régio M. Market Orientation and Hotel Industry: Literature Review and Implications for Periods of Market Turmoil. Journal of Risk and Financial Management . 2022; 15(11):497. https://doi.org/10.3390/jrfm15110497

Sampaio, Carlos, and Mónica Régio. 2022. "Market Orientation and Hotel Industry: Literature Review and Implications for Periods of Market Turmoil" Journal of Risk and Financial Management 15, no. 11: 497. https://doi.org/10.3390/jrfm15110497

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Reference management. Clean and simple.

What is a literature review? [with examples]

What is a literature review?

The purpose of a literature review, how to write a literature review, the format of a literature review, general formatting rules, the length of a literature review, literature review examples, frequently asked questions about literature reviews, related articles.

A literature review is an assessment of the sources in a chosen topic of research.

In a literature review, you’re expected to report on the existing scholarly conversation, without adding new contributions.

If you are currently writing one, you've come to the right place. In the following paragraphs, we will explain:

• the objective of a literature review
• how to write a literature review
• the basic format of a literature review

Tip: It’s not always mandatory to add a literature review in a paper. Theses and dissertations often include them, whereas research papers may not. Make sure to consult with your instructor for exact requirements.

The four main objectives of a literature review are:

• Studying the references of your research area
• Summarizing the main arguments
• Identifying current gaps, stances, and issues
• Presenting all of the above in a text

Ultimately, the main goal of a literature review is to provide the researcher with sufficient knowledge about the topic in question so that they can eventually make an intervention.

The format of a literature review is fairly standard. It includes an:

• introduction that briefly introduces the main topic
• body that includes the main discussion of the key arguments
• conclusion that highlights the gaps and issues of the literature

➡️ Take a look at our guide on how to write a literature review to learn more about how to structure a literature review.

First of all, a literature review should have its own labeled section. You should indicate clearly in the table of contents where the literature can be found, and you should label this section as “Literature Review.”

➡️ For more information on writing a thesis, visit our guide on how to structure a thesis .

There is no set amount of words for a literature review, so the length depends on the research. If you are working with a large amount of sources, it will be long. If your paper does not depend entirely on references, it will be short.

Take a look at these three theses featuring great literature reviews:

• School-Based Speech-Language Pathologist's Perceptions of Sensory Food Aversions in Children [ PDF , see page 20]
• Who's Writing What We Read: Authorship in Criminological Research [ PDF , see page 4]
• A Phenomenological Study of the Lived Experience of Online Instructors of Theological Reflection at Christian Institutions Accredited by the Association of Theological Schools [ PDF , see page 56]

Literature reviews are most commonly found in theses and dissertations. However, you find them in research papers as well.

There is no set amount of words for a literature review, so the length depends on the research. If you are working with a large amount of sources, then it will be long. If your paper does not depend entirely on references, then it will be short.

No. A literature review should have its own independent section. You should indicate clearly in the table of contents where the literature review can be found, and label this section as “Literature Review.”

The main goal of a literature review is to provide the researcher with sufficient knowledge about the topic in question so that they can eventually make an intervention.

Use of Market Research Panels in Transit (2013)

Chapter: chapter two - literature review.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

8 accommodate new technologies, such as telephone sampling and computerization. Since Frankel and Frankel wrote their article in 1987, the Internet has become another technologi- cal advance, requiring innovations in sampling to accommo- date the technology. The Brick article notes that 10 years ago, no generally accepted method of sampling from the Internet had been established, and that as of the writing of the article in 2011, it was still the case. As developed by Neyman in 1934, probability survey sam- pling became the basis for virtually all sampling theory, with a very specific framework for making inferences. The frame- work assumes that all units in the population can be identified, that every unit has a probability of being selected, and that the probability can be computed for each unit. Once the population is selected, the framework assumes that all char- acteristics can be accurately measured. Non-response fac- tors and the inability to include portions of the population (coverage error) violate the pure assumptions of probability sampling. A variety of techniques has been developed to adjust for non-response and coverage error, such as model- based and design-based sampling methods. Technological advances that have changed sampling meth- ods include: the introduction of telephone surveying, which eventually replaced face-to-face interviews as the primary mode of household surveying; the shift from landline tele- phones to cell phones; and the advent of the Internet. Each of these not only affected methods of sampling, it is intertwined with the others, with changes in one leading to new develop- ments in the others. New Concerns with Traditional Quantitative Research The science of traditional quantitative market research rests on two fundamental assumptions: (1) the people who are surveyed approximate a random probability sample; and (2) the people who are surveyed are able and willing to answer the questions they are asked (Poynter 2010). In addition to these concerns with the theoretical underpinnings of market research, operational concerns are also putting pressure on traditional methodologies. Random Probability Sample The random probability sample is an essential ingredient of traditional market research. Without it, quantitative market This literature review consists of four sections: • The first section provides a brief history of survey sam- pling and the theoretical basis for market research anal- ysis, providing context for what became the standard procedures and expectations of market research. This is followed by an overview of some of the issues facing market research today, and how they are impacting the statistical underpinning of market analysis. • The second section introduces traditional panel surveys and panel survey techniques. A summary of a typical tra- ditional case example, the Puget Sound Transportation Panel Survey, is provided in Appendix A. • The third section introduces relatively newer concepts of online panel research, with definitions particular to online panel surveys and techniques, issues with online panel research, and the special concerns of market research in the public sector. • The fourth section circles back to the concerns raised in the first section, and looks at what lies ahead for the market research industry on these issues. MARKET RESEARCH CONTEXT A Brief History of Survey Sampling In a 2011 special edition of the Public Opinion Quarterly, Brick provides an article on the future of survey sampling (Brick 2011). For the purposes of the article, survey sampling is defined as the methodology for identifying a set of obser- vations from a population and making inferences about the population from those observations. Prior to 1934, full enu- meration was considered necessary to understanding char- acteristics of a population; everyone needed to be contacted. Neyman, in his 1934 article “On the Two Different Aspects of the Representative Method: The Method of Stratified Sample and the Method of Purposive Selection,” planted the seeds that resulted in the overthrow of full enumeration and established the paradigm of survey sampling. The move to telephone surveying in the mid-20th century was another significant change in survey sampling methods. The pressures for timely and cost-efficient estimates were stimulants for change then, and are even more relevant today. The article by Brick draws from a 1987 article by Frankel and Frankel, “Fifty Years of Survey Sampling in the United States.” In the 1987 article, sampling is described as having two phases: (1) the establishment of the basic methods of prob- ability sampling; and (2) innovations in the basic methods to chapter two LITERATURE REVIEW

9 research studies and analysis cannot be conducted. It is this underpinning of sampling theory that allows the calculation of sampling and expressing confidence in the results, such as results being ±3% with a confidence interval of 95%. As tele- phones became standard in every household, random-digit- dial techniques for landline telephones became the foundation of probabilistic sampling, with a solid theoretical basis. Results for a survey conducted between January and June 2011 by the National Center for Health Statistics found that 31.6% of American homes had only cell phones, and that an additional 16.4% of homes received all or almost all calls on wireless telephones despite also having a landline telephone (Blumbery 2011). Expert disagree, but it has been suggested that if more than 30% of the population has a 0% chance of being selected, then a random probabilistic sample cannot be selected (Brick 2011). The implication is profound—that with the incidence of landline phones declining, random-digit-dial telephone survey- ing, the mainstay of traditional quantitative market research, no longer provides a probabilistic sample. Cell phones are considered unsuitable for random-digit-dialing, for a variety of reasons, including the possibility of respondents having more than one cell phone resulting in duplication within the sample; respondent resistance; and legislation that prohibits the practice. Online recruitment is fast and economical, but does not provide a probabilistic sample, as is discussed later in this chapter. Willingness and Ability to Answer Research Questions Market researchers started out assuming that people could answer direct questions about their attitudes and behavior. Early on, it became clear that these questions were difficult to answer, so psychometrics and marketing science method- ologies were developed to facilitate responses and analysis of results. More recently even these techniques have been chal- lenged, as the industry realizes that respondents are unreliable witnesses about themselves. Operational Issues Other problems with the traditional market research process are operational. It is perceived as slow and costly, and increas- ingly, organizations are relying on techniques that may be providing quick results at the expense of quality. The cost of traditional survey research often is driven up by the decline in use of landline telephones, making it more difficult to obtain a traditional random probabilistic sample. In addition, more people have answering machines to screen calls or otherwise refuse to participate, again making it difficult to achieve a suf- ficient sample without additional time and expense. If cost is arguably the single most important factor in the search for new survey techniques, the Internet offers a potential solution, even though it doesn’t provide a probabilistic sample. This shift from probability sampling to non-probability sam- pling is a paradigm change of the magnitude of the shift from enumeration to probability sampling theory in 1934—which, Brick notes in his article, was spurred by the cost of enumera- tion. Today researchers find themselves in a similar situation, driven by rising costs away from probabilistic sampling toward non-probabilistic sampling. The issues raised here may fundamentally change the way all market research is conducted. How the market research indus- try is responding and what may lie ahead is discussed in the last section of this chapter, “The Future of Market Research.” TRADITIONAL MARKET RESEARCH PANELS Panel surveys have been conducted for many years, and have been used in the transportation industry for topics such as travel behavior changes and tracking customer satisfaction. The concepts discussed in this section are applicable to both traditional and online panel research. Definition of a Traditional Panel The meaning of a market research panel depends on the con- text, industry, and time period in which the term is being used. The AMA acknowledges this with the following distinctions: • True panel: A sample of respondents who are measured repeatedly over time with respect to the same variables. • Omnibus panel: A sample of respondents who are mea sured repeatedly over time but on variables that change from measurement to measurement [http:// www.marketing power.com/_layouts/Dictionary. aspx?dLetter=P (accessed Mar. 11, 2012)]. Traditional Panel Survey Techniques Traditional panel members were recruited through probabilis- tic sampling techniques so that survey results could be extra p- olated to the general population. Developing and maintaining a panel was an expensive proposition, made more difficult by the challenge of keeping track of people and households as they moved and changed phone numbers. Panel survey research was typically used to determine individual travel behavior changes over time, such as to understand the relation- ship between changes in household characteristics and choice of travel mode. Another use was for “before and after” studies to measure impacts of a change in policy or service; for exam- ple, adding a new light rail line or carpool lanes. These studies were often conducted by a MPO for the purpose of developing regional travel demand and forecasting models. Panels were rarely set up and maintained for the purpose of ad hoc, on-call market research (omnibus panels). Panel data collection is described as “a survey of a group of preselected respondents who agree to be panel members on a continuous basis for a given period of time and provide demographic data, allowing selection of special groups and

10 cally, each wave consists of the same core questions along with some new questions. In a travel behavior survey, the panel provides information on how the travel behavior of each participant evolves in response to changes in the travel environment, household background, or other factors. Rotating or revolving panel surveys are a combination of repeated and cross-sectional designs, in that they collect panel data on the same sample for a specified number of waves, after which portions of the panel are dropped and replaced with comparable members. The strength of this design is its abil- ity to allow for both short-term panel member analysis and long-term analysis of population and subgroup change. Like repeated cross-sectional designs, rotating panels periodically draw new members from the current population, obtaining similar measurements on them. Benefits of Panel Designs The most important benefit of a panel survey is that it directly measures changes at the individual level and can provide repeated measurements over time. This rich source of infor- mation on personal and household behavior is essential for determining causal relationships between travel behavior and the factors that influence personal travel decisions and developing predictive models for personal travel behavior. This same benefit applies to the ability to measure and under- stand trends in population behavior. Panel studies can be especially useful for before-and-after surveys that measure the impacts of transportation policy and service changes on travel behavior, rider attitudes, and safety. For example, a before-and-after study of the implementation of a new rail line (replacing existing bus service) shows that a shift in mode split occurred after the implementation of the new line. Results using a cross-sectional survey showed a shift from auto to train after opening of the rail line suggesting overall growth in transit use shifting car drivers to rail riders. A panel study measuring individual specific changes captured a shift from bus to car in addition to the shift from car to rail. This finding fundamentally changed the implications of the cross- sectional study: the new service attracted former car drivers, but also shifted former bus riders into cars. Additional benefits of the panel approach include statisti- cal efficiency (it requires a smaller sample size); lower cost (it requires fewer surveys); and speed (easy access to the panel allows faster survey implementation than when a fresh sample must be obtained). Limitations with Panel Designs Three primary limitations of panel surveys are identified: panel attrition, time-in-sample effects, and seam effects. 1. Panel attrition refers to panel member non-response in later waves of data collection. The Puget Sound Trans- portation Panel conducted its first wave of surveys in permitting the use of surveys to monitor responses over time” (Elmore-Yalch 1998). This maximizes the use of a sample in that the sampling need be done only once, after which the panel is accessible for future research efforts. Panel member attrition and replacement is an element of maintaining the panel, and is discussed elsewhere in this chapter. The remainder of this section is a summary of An Introduc- tion to Panel Surveys in Transportation Studies (Tourangeau et al. 1997), which provides a solid overview of the basics of traditional panel survey research, especially as applied to travel behavior studies. The report has a four-fold purpose for the development and implementation of travel behavior studies: (1) to highlight the differences between cross-sectional and panel surveys; (2) to discuss the limitations of both cross-sectional and panel sur- veys; (3) to identify situations where panel surveys are the preferred method; and (4) to provide guidelines for design- ing a panel survey and maintaining the panel. A panel survey approach is recommended when the purpose of the survey is to develop travel demand models and forecast future demand; to measure and understand trends in behavior; to assess the impact of a change in transportation policies or services; or to collect timely information on emerging travel issues. Definition of Cross-Sectional and Panel Designs There are two broad types of surveys, cross-sectional and panel surveys. A cross-sectional survey uses a fresh sample each time, whereas a panel survey samples the same persons (or households) over time. In addition, the questions may be the same or change with each survey. This creates four basic approaches to travel behavior surveys. One-time cross-sectional surveys provide a “snapshot” of travel behavior at a particular point in time, and show how behavior differs among members of the population, but pro- vide no direct information on how it changes over time. This type of survey makes no attempt to replicate conditions or questions from previous studies, and as a result is not well suited for assessing trends in population behavior. Repeated cross-sectional surveys measure travel behav- ior by repeating the same survey on two or more occasions. In addition to repeating the questions, the sampling is conducted in a similar manner to allow comparisons between or among separate survey efforts. Repeated cross-sectional surveys are sometimes referred to as a “longitudinal survey design” because they measure variations in the population over time. A more restrictive definition of a longitudinal survey design is where survey questions are repeated with the same sample over time. Longitudinal panel designs collect information on the same set of variables from the same sample members at two or more points in time. Each time the panel is surveyed, it provides what is called a “wave” of data collection. Typi-

11 1989. The fourth round of surveying in 1993 had a par- ticipation rate from the original panel member of about 55%, meaning 45% of the panel had left and needed to be replaced. 2. The time-in-sample effect refers to reporting errors or bias as a result of participants remaining in the panel over time. This is also called condition, rotation bias, or panel fatigue; and generally refers to respondents reporting fewer trips or fewer purchases in later rounds of a panel survey than in earlier ones. 3. Seam effects are another type of reporting error and refer to reporting changes at the beginning or ending of the interval between rounds rather than in other times covered by the interview. Design Issues in Conducting a Panel Survey There are four design issues that need to be considered in conducting a panel survey: definition of the sample unit; the number and spacing of rounds; method of data collection; and sample size. 1. Most traditional travel surveys conducted by MPOs use households as the sampling unit; however, sampling individuals is another option. When a household is the sampling unit, the panel survey sample can become com- plicated as household members are born, die, divorce, or mature and move out. For travel surveys, the report sug- gests using the household as the sampling unit, follow- ing initial respondents to new households, and adding any additional household members to the panel. 2. The number and spacing of survey rounds depends on factors such as the rate of changes in travel behavior and the need for up-to-date information. If changes in travel behavior are the result of external factors, such as rap- idly increasing gas prices, or if administrative reporting requires monthly or quarterly updates, this may shorten the intervals between survey waves. Panel travel surveys are collected at six-month or annual intervals, balancing the potential for respondent burden with the desire for regular data collection. The report recommends annual data collection for travel behavior studies. 3. Data collection methods differ in terms of cost, cover- age of the population, response rates, and data quality (inconsistent or missing data). In-person data collec- tion is typically the most expensive, but produces the highest percentage of coverage, highest response rates, and potentially most accurate data, as the interviewer can assist the respondent. Telephone data collection tends to be the next most expensive methodology, and eliminates the population without a telephone. This used to be limited since almost all households had a landline phone, but since the report was written the per- centage of mobile phone-only households has grown significantly. Data collection by mail is the cheapest of the three traditional modes, but has the lowest response rates and poorest data quality. [Since the report was written, Internet surveying has become another inex- pensive alternative method of data collection. Online surveying is covered in other portions of the literature review.] The report recommends using the telephone for data collection in the first wave of a travel behavior panel study and considering less expensive methods for successive waves, if necessary. 4. Selecting the sample size requires specifying the desired level of precision for the survey estimates. The preci- sion level is determined by the requirements for ana- lyzing the goals and objectives of the survey, typically rates of change in travel behavior at the household or sub-regional level. After the level of precision is deter- mined, traditional statistical formulas can be applied to determine the sample size, which is then adjusted for anticipated non-response, attrition, and eligibility rates. Issues with Maintaining the Panel The report points up three issues that need to be considered in terms of maintaining a panel: freshening the sample, main- taining high response rates across waves, and modifying the questionnaires across rounds. 1. “Freshening the sample” is the process of adding new panel members over time to ensure that the sample accurately reflects changes in the population from newly formed households or those who have recently moved to the study area. The longer the panel is con- tinued, the less likely it is to represent the study area. The report suggests that, if a panel continues for more than five years or there is significant in-migration to the study area, a supplemental sample be implemented. Another reason for freshening the sample is to off- set attrition, recruiting new panel members compa- rable to those who drop out and thereby maintaining the panel make-up and sample size for the duration of the panel effort. The report suggests that the initial sample size be large enough to accommodate antici- pated attrition in later waves, and that steps are taken to minimize attrition. Replacement of panel members should only be done as a last resort. 2. There are three techniques for maintaining high response rates: tracing people or households who move; main- taining contact with panel members between rounds; and providing incentives for participation. Methods of tracing panel members who move include mailing a let- ter several months in advance of the next wave request- ing updated contact information; and asking the post office to provide new addresses rather than forwarding the mail, to ensure that the contact files get updated. If new contact information is not provided, researchers may attempt a manual search through existing databases. The report suggests that a protocol be developed at the outset of the survey effort to track respondents between waves and reduce attrition. Another way of reducing attrition is to maintain respondent interest and contact information between

12 waves by sending postcards, holiday greetings, and sur- vey results. Incentives such as small amounts of cash can also be helpful. Cross-sectional surveys have shown that a small prepaid incentive (for example, a $2 bill) is effective in increasing participation rates and reducing attrition. Unfortunately, there was limited research at the time as to the effect of incentives on panel surveys over time. It is noted that non-respondents in one wave may still participate in the next, so that only those who refuse to respond to more than one round of the study would be dropped from the panel. 3. A defining element of a traditional panel survey is the ability to administer the same questions to panel mem- bers over time, which is what provides the direct mea- surement of change that is so valuable to travel behavior studies. Two situations arise that may make it neces- sary to modify the questionnaire across waves. First, a new issue may arise that can be advantageously posed to the panel. This then becomes a cross-sectional sur- vey, where the data are collected once. If the question is repeated in later waves, it becomes part of the panel effort. Although this is easy, fast, and less expensive than conducting a separate study, it can add to respon- dent fatigue by making the questionnaire longer. For this reason, it is suggested that new questions be kept to a minimum. The second reason for changing a question that there is a problem with the question itself (e.g., it is poorly worded, yields unreliable results, or becomes irrelevant). In this instance, it is important to revise the question as soon as possible. The report recommends that a calibration study be done to determine the effect of any core changes. Weighting the Panel Data The final section of the report deals with how to weight panel survey data. Weighting is done to account for differences in the probability of being selected, to compensate for differences in response rates across subgroups, and to adjust for random or systematic departures from the composition of the popula- tion. Weighting is done at two points: after the initial wave, following the procedures for standard cross-sectional surveys; and then after each wave to account for changes in the panel membership. Although weighting is fairly straightforward for the first wave, subsequent waves can be complicated if the sampling unit is a household, as is typical of travel behav- ior panel studies. Elements that must be taken into account are how to treat households who add or lose members over the course of the panel; and how to define a “responding” or “non-responding” household, for example, whether all survey waves are completed by all household members or only cer- tain household members. It is sometimes necessary to gener- ate different weights for different survey analyses. Detailed guidelines for developing panel survey weights are provided in the report appendices. ONLINE MARKET RESEARCH PANELS This section will discuss the types of online panels, sampling strategies, and issues and concerns with using the Internet for market research purposes. The current literature reviewed in this synthesis discusses sampling and recruitment for online panels using the Internet, e-mail, or other new technologies, such as quick response (QR) codes scanned by a smart phone. Multi-frame sampling, where a mix of sampling techniques is used for developing the panel, poses additional issues which are only now being explored and disseminated within the market research industry. Because this is an emerging area of research, this literature review does not include multi-frame sampling. Types of Online Panels Three types of panels are discussed by Poynter in his 2010 book, The Handbook of Online and Social Media Research: Tools and Techniques for Market Researchers. The first is a traditional panel, typically called a client panel or in-house panel, developed to meet specific criteria and recruited either in-house by the agency or through the assistance of a vendor. The panel can be recruited through a variety of techniques, including telephone; in-person intercepts (on a vehicle, or on the street); through existing agency customer databases; or online, through the agency website or pop-up invitations to join the panel. The critical elements of this type of panel are the definition and control that is exercised by the agency, and the intention for the agency to maintain the panel over time. An online access panel, also referred to as an access panel or online panel, is developed by independent market research firms and can provide samples for most markets that have a significant volume of research activity. The researcher provides the panel company with the desired sample speci- fication, and then either the researcher provides a link to the online survey, or the panel company scripts and hosts the online survey. The third type of panel survey is an online research com- munity, also known as a market research online community or MROC, which combines attributes of panel research with elements of a social media community. Although it is sometimes grouped with social media techniques, the online research community has been included here because it meets the definition of “a group of persons selected for the purpose of collecting data for the analysis of some aspect of the group or area.” In-House Panels As the name implies, in-house panels are owned by the research department of the agency, and are not purchased from a vendor’s existing panel. The in-house panel is used

13 for market research, not public relations, marketing or sales; and panel members are aware that they will be contacted for research, insight, and advice. The primary advantages of in-house panels are cost savings, speed of feedback, and control over the panel. Disadvantages include the workload required to manage a panel and that the possibility that panel members may become sensitized to the research approaches. In-house panels can be conducted simply from a list of people and an off-the-shelf survey program using e-mail and a way to unsubscribe from the panel. For small-budget projects or a low-key exploratory concept, a simple approach may be the most appropriate. More sophisticated panel management may require methods to prevent people from signing up mul- tiple times, the ability to draw sub-samples, protocols for han- dling and managing incentives, panel member replacement strategies, quotas on survey responses, online focus groups or bulletin board groups, and rules for creating an online panel community. The more sophisticated the approach, the more advanta- geous it is to contract with a vendor to run the panel. Using internal staff may make the research more responsive to man- agement needs while saving in consultant fees. A vendor, how- ever, can handle more work without overburdening agency staff, using employees familiar with the latest thinking and best practices. These different strengths often lead to a strong partnership between the vendor and staff. Traditionally, panel research was done with standard ques- tionnaires, implemented by means of mail or telephone. New developments in technology and the Internet have made it easy to expand the activities of a panel even further, creating online focus groups, photo projects where panel members take pictures with their cell phones and upload them to an agency website, brainstorming through collaborative systems such as “wiki” sites, and quick “fun polls” that encourage participa- tion, generate panel engagement, and provide almost instant answers to questions of the moment. Tips for using an in-house panel include: 1. Manage the expectations of panel members by letting them know at the outset how many surveys/activities they should expect. 2. Let panel members know you value their participation and that they are making a difference. 3. Recognize that panels will usually be skewed toward members who are knowledgeable about the product or service, and that they may not represent the opinion of the general public. 4. Complement conventional incentives (such as cash) with intrinsic rewards, such as information about upcoming events or new products before it hits the general market. Online Access Panels Online access panels have fundamentally changed how mar- ket research is conducted. An online access panel “is a col- lection of potential respondents who have signed up with a vendor which provides people for market research surveys.” These respondents are aware that they are joining a market research panel, and that they will be receiving invitations to online surveys. The vendor keeps some information on the panel members so that it can draw samples, if requested, but does not share this information with the client. Panel mainte- nance, including the provision of incentives, is the vendor’s responsibility. In selecting a panel vendor, six factors need to be considered: 1. Does the vendor provide only the sample, or will it also host surveys? If so, can the brand image on the survey maintain the agency’s brand, or does it become folded into the vendor’s survey branding? 2. What is the quality of the panel? Not all panels are created equal, and the results can vary based on the panel used. ESOMAR formulated “26 Questions” (later, “28 Questions”) for agencies to ask vendors in order to understand their procedures and the potential quality of the survey results. The questions can be found at: http://www.esomar.org/index.php/ 26-questions.html. 3. In looking at vendor costs, caution must be exercised to ensure that price quotes are on similar services so they can be correctly compared. 4. Make sure that the vendor has the capacity to complete the study, including any potential future waves of the study. It is common practice for panel survey vendors to outsource a portion of or even the entire project to another firm if they do not have the resources to com- plete it as scheduled. Outsourcing to another panel sur- vey firm can result in double-sampling people who are members of both panels. More importantly, because dif- ferent panels often have varying results, this can lead to confusion as to whether an apparent change is real or a reflection of the panel used. 5. The more data a vendor has on its panel members, the more closely a survey can be targeted to the appropriate respondents. This results in fewer respondents being screened out and a shorter survey with fewer necessary questions. 6. As with any service, it is helpful to have a supportive vendor who is willing to stay late if needed, help clean up errors, and respond quickly to issues and concerns. After selecting a vendor, it is essential to ensure a good working relationship. This can be facilitated by: • Clarifying the quote for the project to make sure it includes all work needed;

14 • Booking the fielding time for the job as soon as the ven- dor is selected so there is flexibility if dates need to be changed for holidays, computer maintenance, etc.; and • Developing and agreeing on the timeline, including final- izing the sample specification, scripting the survey or sending the link to the survey, having a soft launch to test the survey, agreeing on the full implementation and end date, and specifying the frequency of communica- tion with the panel company, especially regarding prob- lems that may occur. Once the survey is in the field, it is important to monitor progress and report any issues immediately to the panel ven- dor, including problems reaching the target quotas for com- pleted surveys. The sooner action is taken, the easier it will be to rectify the issue. It is advisable to work closely with the vendor supplying the panel to take advantage of its experi- ence with data issues with long surveys and improving the survey experience. Online Research Communities Using social media to create online research communities or MROCs for research purposes is a relatively new field. Research communities have been offered by third-party ven- dors since about 2000, but did not become widely used until about 2006. Online research communities typically have a few hundred members, and straddle the divide between quantita- tive and qualitative research. The communities can be short- term, developed for one research question and then dissolved; or can be a long-term resource, allowing research on a wide variety of topics over a period of six months or more. The benefits of online research communities are that they provide access to the authentic voice of the customer; go beyond the numbers to provide qualitative discussion; provide quick turnaround at a low marginal cost because the sampling and recruitment is complete; and create an active dialogue with the customers, letting them feel they “make a difference.” These communities can either be open to anyone who wishes to join (within the requirements of screening criteria, such as age or geographic location), or closed, in which case panel members are invited to participate. It is important to note that open communities tend to be more about involvement, advo- cacy, and transparency rather than insight and research. Incentives are important to maintaining a high level of par- ticipation for all types of research panels; however, several issues are to be considered when structuring an incentive pro- gram. (It should be noted that it is illegal for some public agen- cies to use incentives.) The argument for using incentives is that they represent a small payment for the time and contributions of the panel members, and may be necessary to obtain the level of engage- ment needed to make the community succeed. The type of incentive (cash versus intrinsic rewards) must also be con- sidered. A chance to win a transit pass or seeing the results immediately upon completing an instant poll are examples of incentives. Finally, the agency must decide how to allocate the incentives. Options include giving all members an incen- tive regardless of participation levels; giving members who participate in a specified time frame the incentive; offering a chance to win a prize; and awarding a prize to the “best” contribution in a specified time frame. Agencies should avoid starting with a high-value incentive, because lowering the incentive later will seem to panel members that the agency is taking away a benefit, resulting in a loss of participation. As with all research techniques, the online community can be developed and maintained either in-house or through a vendor. Online research communities require significant and continuous management. Even if the community is maintained by a vendor, significant input by staff is needed to ensure that the community is addressing issues of concern to the agency. The advantages of a having a research-only community are that it can be much smaller than broader-topic communities, and members may be more open if they know they will not be “sold to” another interest. Opening the community up to other department managers may result in too many surveys and e-mails being sent to members, with research being pushed aside in favor of other topics. Likewise, it is important not to allow community members to usurp the purpose of the research community for their own agendas. Part of managing the community is monitoring and ending any member activ- ity that begins to create an agenda separate from that of the agency, even removing a panel member if necessary. The steps to and guidelines for setting up an online com- munity include determining: • What type of community is best (short versus long term, open versus closed, and the number of members); • The “look and feel” (i.e., makeup) of the community; • Community tools; • Methods of recruiting members; • Terms and conditions (including intellectual prop- erty, member expectations, restricted activities, anti- community behavior, privacy and safety, incentive rules, eligibility, data protection and privacy), and the ability to change terms and conditions; • Methods of moderating and managing communities (moderator function, community plan, dealing with neg- ativity, creating member engagement); and • Requirements for finding and delivering insights. The rapid pace of change among social media makes it difficult to project how this type of research activity will be conducted in the future. Four considerations are identified in Poynton’s book: 1. Market research organizations typically do not allow activities that would influence the outcome of the

15 research. Because interaction and relationships built between community members and the sponsoring com- munity agency may sensitize panel members to organi- zational issues, MROCs may be declared “not research.” 2. Currently, online research communities are used for more qualitative work rather than large-scale quantita- tive work. The ability to expand online research to larger projects (e.g., international research) will increase this as a mainstream research tool. 3. Respondent fatigue may set in, resulting in a less engaged community. This may be especially true if panel mem- bers belong to more than one community. 4. Alternative (not research-based) methods may be more successful, such as having a very large community that can serve both marketing and research functions, or tap- ping into other existing communities to conduct research rather than establishing one specific to the organization. One of the primary concerns with online research commu- nities has been that the relationship with the organization may cause heightened brand awareness and affinity, and that this will lead to a positive bias in research results. However, Aus- tin notes in an article in Quirk’s Marketing Research Media (Austin 2012) that while engagement builds a relationship with the company, community members remain candid and critical despite their relationship with the brand. If anything, members became slightly more critical as their tenure length- ened, not less. The article recommends that in moving to a new research paradigm, organizations make two changes from the traditional research approach to take advantage of this finding: trade anonymity for transparency because transparency builds engagement; and trade distance for relationship because rela- tionship creates candor. Together, the community members “work harder, they share more and they stay engaged in the research longer.” Online Panel Sampling Techniques A few online panels employ traditional random sampling tech- niques, such as random-digit-dialing, and then conduct the research online; but the majority of panels are recruited using a non-probability approach online, such as pop-up or web ban- ner ads. The AAPOR Report on Online Panels (Baker 2010) covers both types of panels. This review will cover probability and non-probability sampling techniques as they relate to panels; it also discusses “river sampling,” although it is not a panel sampling technique, per se. Lastly, it provides an over- view of strategies for adjusting non-probability samples to represent a population. Probability sampling techniques for online survey research have been slow to be adopted, despite being around for more than 20 years. The recruitment is similar to voluntary, non- probabilistic samples, except that the initial contact is based on probabilistic sampling techniques such as random-digit- dialing, or other techniques for which the population is known. Computers may sometimes be provided to persons with no online access to remove bias that might exist from only includ- ing persons or households with Internet access. Once the sample is determined, panels are built and maintained in the same way, regardless of whether they are probability- or non- probability-based. A probability-based sample is more expen- sive to develop than a non-probabilistic sample. Consequently, systematic replacement or the replacement of panel members lost through attrition is also more costly. The benefit is that a panel can be built that represents the general population and allows analysis of results based on probability theory. Non-probability and volunteer online panel members are recruited through a variety of techniques, all of which involve self-selection. The invitations to join a panel can be delivered online (through pop-up or banner advertisements), in maga- zines, on television, or through any other medium where the target population is likely to see the advertisement. The recruit- ment entices respondents by offering an incentive, talking about the fun of taking surveys, or other proven techniques. A common practice in the industry for developing online panels is through co-registration agreements. An organization will compile e-mail lists of its website visitors and ask if they would like to receive offers from partner agencies. The e-mail list is then sold to a research panel company. Off-line recruit- ment strategies include purchasing an organization’s cus- tomer contact database and asking participants in a telephone survey if they would like to become part of an online panel for future surveys. A technique used for both online and off-line recruitment is to ask existing panel members to refer their friends and relatives, sometimes offering a reward for each new panel member recruited. No two panels are recruited the same way, and the panel research companies carefully guard their methodologies for recruiting panel members. River sampling is an online technique that uses pop-up sur- veys, banner ads, or other methods to attract survey respon- dents when they are needed. In river sampling, the ad presents a survey invitation to site visitors and then directs or “down- streams” them to another, unrelated website to complete the survey. (Using this analogy, a panel would be a pond or reser- voir sample.) Knowing on which websites to place the ads is critical to the success of river sampling. This technique is not related to developing a panel, although sometimes the respon- dent is invited to join a panel at the completion of the sur- vey. There is generally a reward of some kind for completing the survey, such as cash, online merchant gift cards, frequent flyer miles, etc. This type of sampling may be on the rise as researchers seek larger and more diverse sample pools, and to get respondents who are less frequently surveyed than those provided through online access panels. The AAPOR report provides an overview of strategies for adjusting self-selected (non-probability-based) online panels, and reviews complex weighting, quotas, benchmarking, and modeling methodologies for creating a more representative

16 sample. Complex weighting uses detailed information about the population to balance respondents so that they mirror the population. Quotas, which match key demographics of the respondents with the demographics of the target population, are the most common technique. Benchmarking keeps the sample specifications the same over multiple waves, under the assumption that any changes are the result of changes in the element being measured, regardless of whether the sample is representative of the population. Modeling refers to linking the benchmark results to the real world to model what a sur- vey score of X means in terms of actual outcomes. When applying statistical significance testing to the panel sample, it is important to recognize that the significance is not how representative it is of the population, but of the panel. “The error statistics indicate how likely it is that another sample from the same panel will be different, which is a valid and rele- vant measure of reliability” (Poynter, p. 74). It is not, however, an estimate of the population sampling error, as is commonly understood with traditional random (probabilistic) sampling. Response rates for online access panels have little impact on how representative the research is, but do provide a measure of the quality of the panel. Issues and Concerns with Online Panel Surveys: AAPOR Report on Online Panels Online surveys have grown rapidly because of the lower cost, faster turnaround time, and greater reliability in building tar- geted samples, at the same time that traditional survey research methods are plagued by increasing costs, higher non-response rates, and coverage concerns. The quality of online access panel survey data came into focus in 2006 when the VP of Proctor and Gamble’s Global Consumer Market Knowledge gave a presentation on the range of problems P&G had faced with online access panel reliability. It fielded a survey twice with the same panel, two weeks apart, with results that pointed to two different business conclusions. This focused the market research industry’s attention on the need to provide understand- ing, guidance, and research on the topic of online research. The traditional probabilistic sample, such as random-digit- dialing, is the underpinning of market research. Probabilistic samples are based on the probability of being selected out of a specified population (such as households within the city lim- its). Based on probability theory, the results can be projected to the population with a statistical level of certainty. Online panel surveys typically use non-probability samples, which are a significant departure from traditional methods. The AAPOR Report on Online Panels, produced by the AAPOR task force on opt-in online panels, is a seminal work on concerns and issues with online panel (i.e., non-probability sample) survey research. The scope was to “provide key infor- mation and recommendations about whether and when opt-in panels might be best utilized and how best to judge their quality” (Baker 2010). Sampling Error, Coverage Error, and Non-Response Bias A sample is, by definition, a subset of a population. All sur- veys, regardless of sampling method, have some level of imprecision owing to variation in the sample. This is known as sampling error. A probabilistic sample is one where sam- pling theory provides the probability by which the member of the sample is selected from the total population. In traditional sampling methods, such as random-digit-dialing of house- holds within a geographic area, the total population of home telephone numbers is known. With address-based sampling, the total number of addresses in a specific area is known. Thus the total population is known and the probability of selecting any one phone number (or address) is known. This allows the data to be projected to the population as a whole. The difficulty with online sampling is that the population is unknown. Typically an e-mail address is used as the sampling unit (rather than a home telephone, as in the earlier example). The issues with e-mail addresses include duplication problems, in that one person may have more than one e-mail address; and clustering problems, where an e-mail address represents more than one person. As a result, online sampling differs from tra- ditional sampling in three significant ways: (1) the concept of a sampling frame is discarded and the focus is shifted to recruit- ing as large and diverse a group as possible; (2) instead of a representative sample of all households, a diverse group of persons with the attributes of interest for the panel is recruited; (3) the panel membership is rarely rotated, with panel mem- bers being retained as long as they keep completing surveys. Over time, this can lead to a very different panel membership than the initial profile of the panel. Coverage error occurs when persons, or groups of persons, have zero chance of being selected to participate in the sur- vey. Lack of access to the Internet creates significant cover- age bias. The AAPOR report includes data from 2008 stating that although 85% of the households in the continental United States have some level of Internet service, those without Inter- net access differ significantly from those who do. Those with- out access are more than twice as likely to be over the age of 65 as the general population. They are also more likely to be members of a minority group, to have incomes less than $25,000, to have a high school education (or less), to be unem- ployed, not to own a home, and to live in rural counties or the South Census Region. It can also be noted that having access to the Internet does not necessarily make for active users of the Internet. In 1970, household telephone coverage estimates of 88% led to the acceptability of using telephone surveys in place of in person interviewing. Coverage estimates of Inter- net usage are currently lower than 88%, indicating that it has not yet reached a level where it can be used to represent the general population. Commercial online access panels are even more problem- atic, in that a person has to have Internet access, receive an invitation to become a panel member, sign up for the panel,

17 and then participate in the surveys. Current estimates are that less than 5% of the population has signed up for an online panel, meaning that more than 95% of the population has a 0% chance of being selected. Non-response bias is when some of the persons in the sam- ple choose not to respond to the survey, or some of the ques- tions within the survey. Four stages of panel development are discussed, and how online panel survey development is affected by non-response bias: Stage 1: Recruitment of panel members. The previous discussion on coverage error points out issues with Internet access. In addition, there is bias regarding which Internet users are likely to join a panel. The report cites several studies that found online panels are more likely to be comprised of white, active Internet users with high education levels who are considerably more involved in civic and political activities; and who place less importance on religion and traditional gender roles and more importance on environmental issues. Stage 2: Joining and profiling the respondents. Most panels require a respondent to click through from the online ad to the company’s website to register for the panel and complete some profile information, including an e-mail address. An e-mail is sent to the prospective panel mem- ber, who must respond in order to join the panel. A study by Alvarez et al. (2003) reported that just over 6% of those who clicked on the banner ad completed all of the steps to become a panel member. Stage 3: Completing the questionnaire. This is similar to random-digit dialing when a person refuses to par- ticipate in the survey or does not meet the eligibility requirements. Online surveys have an additional non- response bias from technical problems that can prevent delivery of the e-mail invitation or completion of the survey itself. Some panels will oversample groups that are known to have low response rates in order to have a representative sample after data collection is complete. Although this may result in a balanced sample on that particular dimension, it does not ensure that the sample is representative on other points. Stage 4: Panel maintenance. Attrition can be “normal,” when people opt out for whatever reasons; or can be forced, when panel members are automatically dropped from the panel after a set period of time to keep the panel fresh. Many strategies are used to reduce panel attrition, but little research exists on reducing or determining the most “desirable” attrition rate to balance the costs of adding panel members with the potential concerns of long-term membership, such as panel conditioning. Measurement Error Measurement error is defined as the difference between an observed response and the underlying true response. This can be random error, as when a respondent picks an answer other than the true response, without any systematic direc- tion in the choice made. Systematic measurement error, or bias, occurs when the responses are more often skewed in one direction. Much of the literature regarding measurement error is related to the benefits and potential biases of personal interviewers and self-administered surveys, including paper and online surveys. Because this is an issue that is related to data collection methodology for any survey, not specific to panel surveys, it is beyond the scope of this project and will not be covered in this literature review. However, this is an important issue for all survey efforts, and researchers are encouraged to look at the issues related to both interviewers and self-administered surveys. One measurement issue directly related to panel surveys is that of panel conditioning. Repeatedly taking surveys on a par- ticular topic is known to make respondents more aware of that topic, pay more attention to it in their daily lives, and therefore have different responses on future surveys than if they had not been on the panel. The research on panel conditioning with online panels has mixed findings. Some studies have shown a marked bias towards an increased likelihood to purchase; other studies show that this effect can be mitigated by vary- ing topics from survey to survey. Other research studies have shown no difference in attitudinal responses between infre- quent and very experienced panel survey members. There are two theories on the effects of taking large numbers of surveys: Experienced survey-takers may be more likely to answer in a way that they believe is best for themselves (e.g., it will earn them more incentives, or get more surveys to complete; alter- natively, experienced survey takers will understand the pro- cess better, resulting in more accurate and complete responses. So far, there is no definitive research on the effects of panel members completing large numbers of surveys regarding the accuracy of the survey results. Sample Adjustments to Reduce Error and Bias It is agreed by most researchers that online panels are not representative of the general population, and that techniques are needed to correct for this if the results are used. Four tech- niques have been used to attempt to correct for the known biases with a goal of making the sample representative of the population: sampling to represent a population; modeling; post-survey adjustment; and propensity weighting. 1. The most common form of sampling to represent a certain population is quota sampling, with the quotas often being demographics to match the census. Other elements can be factored in by, for example, balancing members by political affiliation. There does not appear to be any research on the reliability or validity of this type of sampling applied to panel surveys. 2. Models are frequently used in the physical sciences and in epidemiological studies to reduce error and bias. Online panels are much more complex than epidemio- logical studies, however, making it more difficult to apply model-based techniques.

18 3. The most common post-survey adjustment is the weight- ing of survey data. The difference between the sample and sampling frame with probability samples is handled through probability theory principles. Because there is rarely a sampling frame in an online sample, the census and other sources are typically used to adjust the results for under-representation of certain groups of respon- dents. Work conducted by Dever et al. (2008) found that inclusion of enough variables could eliminate coverage bias, but did not address problems associated with being a non-probability sample. 4. To apply propensity weighting, a second “reference” survey with a probability-based sample is conducted at the same time as the online panel survey, using the same questions. A model is built that can be used to weight future online surveys to better represent the target pop- ulation. Although this technique can be used success- fully, it can also increase other types of error, leading to erroneous conclusions from the resulting data. The AAPOR report (Baker 2010) provides an extensive discussion of and guidance on applying these techniques. The reader is encouraged to review the report before applying a sampling adjustment technique. Panel Data Quality Panel data cleaning is an important step in delivering results from respondents who are real, unique, and engaged in the sur- vey. Three areas of cleaning panel data are discussed: eliminat- ing fraudulent respondents, identifying duplicate respondents, and measuring engagement. Fraudulent respondents are those who sign up for a panel multiple times under false names and lie on the qualifying questionnaire to maximize their chances of participation. Duplicate responses occur when respondents answer the questionnaire more than once from the same invita- tion or when they are invited to complete the survey more than once because they belong to more than one panel. Measuring engagement is the most controversial technique. Four basic cleaning strategies are used to weed out respon- dents who may not be engaged with completing the survey, but are simply answering to earn the incentives: recognizing respondents with very short survey times (compared with all surveys); identifying respondents who answer all questions in a matrix format (usually scaled questions) the same way; recording an excessive selection of non-substantive answers, such as “don’t know”; and noting nonsense answers or identi- cal answers provided for all open-ended questions. Although there was no research at the time that demon- strated the effects of using cleaned data on the sample or final results, it is generally accepted that negative respondent behavior is detrimental to data quality. Industry Focus on Quality The market research industry has been focused on panel data quality, with virtually every national and international asso- ciation incorporating principles and guidelines for conducting online and panel research. Four key efforts are highlighted in the report: 1. The Council of American Survey Research Organiza- tion (CASRO) revised its Code of Standards and Eth- ics for Survey Research in 2007 to include specific clauses related to online panels. 2. ESOMAR developed comprehensive guidelines titled “Conducting Market and Opinion Research Using the Internet.” This was supplemented by its “26 Questions to Help Research Buyers of Online Samples.” 3. The International Organization for Standardization technical committee that developed ISO 20252— Market, Opinion and Social Research also developed ISO 26362—Access Panels in Market, Opinion and Social Research. The standard defines key terms and concepts in an attempt to create a common vocabu- lary for online panels, and details the specific kinds of information that a research panel is expected to make available to a client at the conclusion of every project. 4. The Advertising Research Foundation established the Online Research Quality Council, which in turn designed and executed the Foundations of Quality project. Work was in progress as of the writing of the AAPOR report, and as of the writing of this synthesis, results of the effort were just being made public. Recommendations The AAPOR Report on Online Panels makes the following recommendations to market researchers who are considering using online access panels: • A non-probability online panel is appropriate when precise estimates of population values are not required, such as when testing the receptivity to product concepts and features. • Avoid using non-probability online access panels when the research is to be used to estimate population values. There is no theoretical basis for making projections or estimates from this type of sample. • The accuracy of self-administered computer surveys is undermined because it is a non-probability sample. A random-digit-dial telephone survey is more accurate than an online survey because it is a probability sam- ple, despite the coverage error arising from households without a landline phone. • It has not yet been demonstrated that weighting the results from online access panel surveys is consistently effective and can be used to adjust for panel bias.

19 • There are significant differences in the composition and practices of various online access panels, which can affect survey results. Different panels may yield significantly different results on the same questionnaire. Market Research by the Public Sector Poynter’s book devotes a section to issues specific to pub- lic sector research. Although most of the marketing research principles apply equally to the private and public sectors, there are a few areas where the public sector researcher needs to be particularly attentive, because public funds are being used to conduct the research and the results may determine how public funds are expended. Areas for particular attention are identified as: operating in the public eye, “representativ- ity,” geographical limitations, social media and the public sector, and ethics. In the Public Eye Public sector research is subject to audit, inspection, and reporting in the media. Freedom of information laws ensure that the public has a right to see how public funds are being spent. Poorly conducted research could be brought to light in a public forum, creating public relations problems for a per- ceived waste of taxpayer money and jeopardizing the ability to conduct future research. As a result, care must be taken to ensure public sector research is conducted to the highest qual- ity and ethical standards. Representativity Having a representative sample is always important, but is of special concern for public agencies. Many public services, such as public transportation, target specific groups which may have multiple challenges. Much of the target population may not have Internet access, and those that do may not be typical of the market segment they are expected to represent. For each study, the researcher must carefully assess whether an online survey is appropriate for that market and research purpose, and whether the sampling and recruitment strategies provide survey results that can be defended in public. Geographical Limitations Public agencies have strict geographic boundaries from which a sample population can be drawn. Face-to-face or telephone surveys are often simplified by these restrictions. Surveys using an online access panel, however, can be problematic, as there may not be an adequate sample of persons from the target area. This is further exacerbated when the sample is also required to be representative of the population within a speci- fied geographical area. Social Media and the Public Sector There are several ways in which social media are being used for research in the public sector. Online communities engage in a range of activities, including information-sharing, research, community-building, and engagement. Online research com- munities are typically closed communities, operated by a ven- dor, with membership by invitation only as part of an overall sampling plan (see the MnDOT case example of an online research community). Twitter, blogs, and public discussions are resources for passive research, using data mining tools to monitor trends in what people are saying about the agency. Although useful information can be elicited from these sources, it should be noted that they do not provide a representative sample, and should be considered public comment rather than research. Social media is often used to reach out to groups that are otherwise hard to reach, such as young adults. It should be noted that using a variety of social media techniques, such as Facebook, YouTube, and Twitter, is likely to reach the same people multiple times. If multiple social media channels are used to recruit online survey participants, for example, the researcher must be prepared for the potential duplication of survey responses. Ethics There is an expectation that research will be reliable, and can be used by a decision-making body in a public forum. First and foremost, the researcher must provide unbiased market research. Often a vendor is used to conduct the research so as to provide a wall between the agency and the research and avoid the appearance of leading the respondents, or “spinning” the results. The second concern is that quantitative research based on random probability sampling has been the standard method for achieving that level of reliability expected of a pub- lic agency. Since online research is typically not from a proba- bilistic sample, the researcher should recognize the potential lack of statistical reliability inherent in the research design and ensure that decision makers understand the limitations of the data. FUTURE OF MARKET RESEARCH Technology has fundamentally changed how society com- municates and how it does business. Whereas people used to communicate by means of the telephone at home, cell phones make communication possible virtually anywhere. Cell phone numbers do not represent a physical address; they have become a moving, real-time “personal” address. The Internet provides instant access to information and communication through e-mail, websites and social media. The smart phone combines mobile communication with the Internet, creating

20 a completely new, technology-based world. Panels can now be developed online, quickly and easily. Household and per- sonal contact information is no longer tied to a home address, but exists outside of the person’s geographic location. This technology has led to a revolution in market research. Recruiting survey respondents is easier; developing a panel is faster; and surveys are online, resulting in automation of survey tabulation and reporting. As a result, recruiting and maintaining research panels is simpler, less expensive, and very attractive to decision makers who want results “now.” But these changes have created a myriad of concerns, pri- marily related to using non-probabilistic sampling practices. The history of sampling theory provides some insights into what may occur in the future. In 1934, although sam- pling theory had not yet been developed, Anders Kaier con- vinced an international audience that representative samples could be used to represent a population. Morris Hansen of the U.S. Census Bureau greatly expanded the theory and prac- tice of sampling and helped convince the bureau to accept sampling and quality control methods in the 1940 Census. Through the leadership of these two important individuals, the practice was adopted. From there, sampling theory was developed—it followed the practice, rather than the theory creating the practice. Brick states that data collection costs will continue to put pressure on agencies to use non-probability samples from online recruitment. If this cannot be done within design-based probability sampling theory, he suggests two potential out- comes: A new paradigm that accommodates online surveys is introduced, which replaces or supplements traditional prob- ability sampling; or online surveys using non-probabilistic sampling are restricted to specific applications as a result of the weak theoretical basis. One potential solution is the use of multiple-frame sam- pling to reduce coverage error, a fundamental concern with online panel research. For example, to reach transit riders, an online survey could be placed on the agency website and be supplemented with paper surveys on board vehicles. Stat- isticians are working on establishing a theoretical basis for conducting sampling using the multiple-frame technique (Brick 2011). In addition to the changes in survey practice that led to the historical development of sampling theory, two additional factors are cited as creating the paradigm shift from popula- tion surveying to representative sampling in 1934. The first was the wealth of scientific development and statistical ideas, not necessarily related to survey sampling, which neverthe- less supported the growth and change in methods. The sec- ond factor was society’s demand for information on a wide range of topics that made population sampling cumbersome and expensive. This desire for faster, cheaper research drove the development of probability sampling and our current market research paradigm. These characteristics are in place today, almost 80 years after probabilistic sampling made its debut. With the rapid changes in technology and society’s insatiable thirst for more information, more quickly, and for less cost, a new research paradigm with a theoretical founda- tion to support non-probabilistic online surveying may be on the horizon.

TRB’s Transit Cooperative Research Program (TCRP) Synthesis 105: Use of Market Research Panels in Transit describes the various types of market research panels, identifies issues that researchers should be aware of when engaging in market research and panel surveys, and provides examples of successful market research panel programs.

The report also provides information about common pitfalls to be avoided and successful techniques that may help maximize research dollars without jeopardizing the quality of the data or validity of the results.

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A Comprehensive Literature Review on Marketing Strategies Adopting by Various Industries

9 Pages Posted: 30 Dec 2022

Rashini Hansika

Uva Wellassa University, Faculty of Management

Date Written: December 20, 2022

Marketing is the process of promoting and selling products and services, including market research and advertising. Today, marketing is an essential part of any organization's growth strategy. Many firms use marketing methods unknowingly to promote themselves and increase sales of their products and services. Marketing is one of the most crucial elements of organizations nowadays. Any strategy used to assist a group, company, or individual in accomplishing their objectives falls under the very broad category known as marketing. The goals of marketing are to raise brand awareness among the target market, increase market share, introduce the firm to new domestic or international markets, boost ROI, increase business profit, optimize the conversion funnel, acquire new leads, foster customer loyalty, and boost sales. A marketing strategy is a company's overarching plan for connecting with potential customers and persuading them to purchase its products or services. A marketing plan typically includes the value proposition of the company, key brand messages, data on target customer demographics, and other significant elements.

Keywords: Marketing Strategy, Premium pricing

Suggested Citation: Suggested Citation

Rashini Hansika (Contact Author)

Uva wellassa university, faculty of management ( email ), do you have a job opening that you would like to promote on ssrn, paper statistics.

Global Literature Review Software Market Report By Type (Cloud-Based, On-Premise), By Application (Large Enterprises, Small and Medium-sized Enterprises (SMEs) And By Regions - Industry Trends, Size, Share, Growth, Estimation and Forecast, 2023-2032

The global demand for Literature Review Software Market is presumed to reach the valuation of nearly USD XX MN by 2030 from USD XX MN in 2022 with a CAGR of XX% during the period of 2023-2030. A literature review talks about the published material in a specific subject area, and at times within a certain time period. It can be just a humble synopsis of the sources, but typically a literature review includes structural patterns and blends both synopsis and synthesis. A synopsis is an outline of the important information drawn from the source; however, synthesis is a re-establishment of the same information. It may offer a newly analyzed version of old material or blend new with old analysis. A literature review software places each work in the context of its role and impact to have an insight into the research problem being examined in an automated way. The key focus of a literature review software is to outline, decode and synthesize the assertions and inklings of others without reinforcing new contributions in an automated way.  Market Dynamics The ever-changing industry trends generate favorable prospects as well as complications with it. Literature Review Software helps to decode and analyze current industry trends and helps businesses to stay ahead of competitors and take the right business decisions at the right time. With the rise in industrialization and scientific research, the literature review software market is expected to flourish. The report covers Porter's Five Forces Model, Market Attractiveness Analysis and Value Chain analysis. These tools help to get a clear picture of the industry's structure and evaluate the competition attractiveness at a global level. Additionally, these tools also give inclusive assessment of each application/product segment in the global market of literature review software. Market Segmentation The entire literature review software market has been sub-categorized into type, application. The report provides an analysis of these subsets with respect to the geographical segmentation. This research study will keep marketer informed and helps to identify the target demographics for a product or service. By Type

• Cloud-Based

  By Application

• Large Enterprises
• Small and Medium-sized Enterprises (SMEs)

Frequently Asked Questions (FAQs) about this Report

What is the Global Literature Review Software Market segmentation covered in the report?

Who are the leading Global Literature Review Software Market manufacturers profiled in the report?

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Baazar Style Retail IPO allotment date likely today: Latest GMP, how to check status in 4 steps

Baazar style retail's ipo, backed by rekha jhunjhunwala, raised ₹ 834.68 crore and was oversubscribed 40.6 times. share allotment is expected on september 4, with listing on bse and nse set for september 6..

Step 3: enter your PAN or application number or DP/Client ID or Account No.

Step 4: Click on Search

Your Baazar Style Retail IPO allotment status will be displayed on the screen.

How to check Baazar Style Retail IPO share allotment status on the BSE website

Step 1: Go to the BSE's allotment page through this link: https://www.bseindia.com/static/investors/application_statuschecksystem.aspx

Step 2: Select 'Equity' in the 'Issue Type' section.

Step 3: From the drop-down menu, pick the IPO under 'Issue Name'.

Step 4: Enter your application number or PAN.

How to check Baazar Style Retail IPO share allotment status on the NSE website

Step 1: Go to the official website of NSE using the following link- https://www1.nseindia.com/products/dynaContent/equities/ipos/ipo_login.jsp

Step 2: To register, click on the 'Click here to sign up' option and provide your PAN on the NSE website.

Step 3: Enter your username, password, and captcha code.

Step 4: After you are redirected to the next page, verify the status of your IPO allocation.

Baazar Style Retail IPO GMP today

Baazar Style Retail IPO GMP (grey market premium) today is +65. This indicates Baazar Style Retail share price was trading at a premium of ₹ 65 in the grey market, according to investorgain.com.

Based on the upper end of the IPO price range and the current premium in the grey market, Baazar Style Retail's estimated listing price is predicted to be ₹ 454 per share, representing a 16.71 per cent increase from the IPO price of ₹ 389.

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