Exploring the Recent Trend Shift in Marketing Research: Social Media (Big) Data and its Sampling Property

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1 1 Exploring the Recent Trend Shift in Marketing Research: Social Media (Big) Data and its Property Acknowledgement We are indebted to anonymous reviewers of our earlier submission no. 425 for providing insightful comments and providing invaluable directions for additional work. Without the anonymous reviewers, this revised paper would not have been possible. Authors Ben Binsardi (Glyndwr University) Kaouther Kooli (Bournemouth University) Jan Green (Glyndwr University) Citation: Binsardi., B, Kooli, K. and Green J Exploring the Recent Trend Shift in Marketing Research: Social Media (Big) Data and its Property, Conference Proceedings, Best Paper Award (Nomination), Pedagogy Track, Academy of Marketing (AM) Conference 2015, 7-9 July, University of Limerick, Ireland, Abstract This paper attempt explores a recent trend shift in marketing research. More marketing researchers are using sources of big data, such as Facebook posts, Twitter threads, blogs, Instagram, and other social media data. However, the infrastructure of marketing research methodologies has not progressed in parallel with the advancement of technology and the availability of big data. For example, marketing students have been using big data and employing samples without fully appreciating the property of the big data population. Understanding the sampling properties of big data will provide invaluable hallmarks for collecting and analysing big data so that more sampling diversity can be achieved and findings that are more rigorous can be discovered to enhance research discoveries. This paper pedagogically offers an initial conceptual, theoretical, and empirical discussion that is useful for providing direction for further research analysing big data, which is in its infancy. 1. Background The emergence of big data has created a trend shift in marketing research (Chang et al., 2013; Lavelle et al., 2011; Loveman, 2003). In this paper, big data is simply defined as extremely large data sets of social media on the Internet that may be analysed computationally to reveal patterns, trends and associations relating to marketing, buyer behaviour and others. Relevant literature (Mayer-Schönberger and Cukier, 2013; Marz and Ritchie, 2012; Manyika et al., 2011) reveals that big data is characterised as 5Vs; namely volume, variety, velocity, value and veracity. Correspondingly, because of the development of social media (big) data, today s marketing students need not employ traditional research methods by interviewing customers and distributing survey questionnaires to the public in order to collect and analyse data and information; instead, they can employ a massive amount of big data on the Internet using, for example, Facebook posts, Twitter threads, blogs and other social media data (Figure 1, Appendix 1). Thus, these social media landscapes have created invaluable opportunities for marketing students to observe real-world marketing phenomena and study consumer behaviour and attitudes at tremendously no cost (Shroff, 2014; Marz and Ritchie, 2012). Following earlier rationales, the aim of this paper is to explore what the trend shift means for marketing students, to enable them to optimise and take advantage of big data in their daily research and to inform decision makers in all marketing aspects. This paper particularly explores pedagogically in detail sampling aspects and properties of big data to answer questions relating to how accurate, valid and representative are samples taken from big data. Marketing students normally employ traditional sampling techniques such as convenience sampling (Figure 2, Appendix 1) by selecting a few respondents to be analysed from the whole population. The respondents can be selected fractionally using non-random sampling techniques such as convenience, purposive and quota sampling techniques. However, using big data, marketing students face a huge number of communicated texts. Instead of a small number of communicated texts, they face thousands or even millions of communicated texts via, for example, Twitter, blogs, Facebook and websites, which cannot be analysed directly by employing NVivo software packages because of the nature of the data mixed data (both structured and unstructured). That is, these communicated texts need to be filtered using Haddop and Map Reduce software packages before being analysed by NVivo software packages. In addition, analysing big data (using NVivo software packages) can take months, even years, because of its size (massiveness). Other problems include how to select the number of samples from the whole population (for example, from particular Twitter threads). Correspondingly,

2 2 following the above rationales, several aspects affect the recent shift in marketing research such as (a) data collection aspects and (b) sampling property aspects. Correspondingly, the aim of this paper is to explore the sampling aspects of big data as portrayed in social media such as Twitter threads, blogs, Facebook posts, Flickr and YouTube (Figure 1). In this paper, sampling is simply defined as the practice of selecting a subset of data (some Twitter threads) from the population (whole Twitter threads). is widely used in marketing research because it is more efficient to analyse a subset of data (samples), if this would give similar results to analysing all of the data (the population). In addition, sampling speeds up processing for writing final reports when the volume of data is large. property here can be defined simply as the strength of sampling selected. This paper is divided into three sections. Section 1 introduces the trend shift in marketing research in relation to social media data. Section 2 assesses the sampling property aspects of big data. Finally, Section 3 concludes and pedagogically offers some further sampling strategic implications for marketing students. 2. Aspects of Social Media (Big) Data In quantitative analysis, a relatively large sample is recommended because as the sample size n grows indefinitely, the characteristics of samples are equal to the characteristics of the population. Correspondingly, the expected value of the sample means will itself be the population mean and the expected value of standard deviation of the sample means will equal the standard error of the population mean. This proposition is known in the literature as the central limit theorem (CLT) (Fischer, 2011; Johnson, 2004), which can be stated algebraically as Equations 1 & 2 (Appendix 2). This CLT is the aim of any marketing student in implementing quantitative analysis so that the research findings will represent the true picture of the population under investigation. Analogously, a sampling strategy for qualitative analysis is as essential as that for quantitative analysis because a precise sampling technique leads to unbiased and robust findings. However, applying this to social media (big) data, can research which employs social media (big) data be concluded similarly? Correspondingly, the next sections will explore sampling property aspects of qualitative analysis, such as convenience, purposive and other probabilistic sampling (Figure 2, Appendix 1), which are relevant to social media (big) data. 2.1 Convenience It has been noted in the mainstream literature (Charmaz, 2006; Glaser, 2004, 1998; Goulding, 2002; Strauss and Corbin, 1990; Straus, 1987) that qualitative analysis is related to non-probabilistic sampling (the interpretivist paradigm) while quantitative analysis is more positively linked with probabilistic sampling (the positivist paradigm) (Figure 2). Correspondingly, when collecting social media (big) data on the Internet, marketing students often employ a convenience sampling technique by partially selecting some respondents (say 50 participants) from the whole population of a particular Twitter thread (say 1,000 participants). Convenience sampling is simply defined here as a type of non-probability sampling technique that relies on data collection from the population members who are conveniently available to participate in research, and in which no inclusion criteria is identified prior to selection of the respondents (Saumure and Given, 2008). While, probability sampling can be defined as a random sampling technique used to ensure that every element in a sample frame has an equal chance of being incorporated into the sample (Sudman, 1976; Barnett, 1974). While nonprobability or non-random sampling can be defined as a sampling technique in which the samples (participants) are collected non-randomly; it does not give all the participants in the population an equal chance of being selected (Cochran, 1977). The practical reasons for employing convenience sampling for social media (big) data or Twitter threads and choosing only a fraction is due to practicality, time, and resource consideration because of the existence of a massive amount of Twitter threads; there are potentially thousands even millions in real life. Hence, it takes a considerable amount of time and resources to analyse and code the whole communicated texts of all the respondents who participated in particular

3 3 Twitter threads. Unfortunately, since the characteristics of the respondents who were active in the same Twitter threads are mostly homogenous (they share the same interests, are educated, and are relatively young), homogeneous respondents tend to be oversampled and sampling diversity may not be achieved by using a mere convenience sampling technique. diversity or heterogeneity is a prerequisite of qualitative analysis (Maykut and Morehouse, 2000) because the technique seeks a wide range of views or different subjects. This results in more robust findings (Cochran, 1977). In summary, although using a convenience sampling technique for big data is useful for undertaking pilot studies, hypothesis generation, and initial exploratory analysis (in which data collection can be facilitated in a relatively short time), it comes at higher costs, such as homogenous characteristics and a lack of sample diversity. These phenomena make the research highly vulnerable to selection bias and a high level of sampling errors. 2.2 Purposive A purposive sampling technique can be defined as a sampling technique in which the respondents were selected based on the knowledge of a population and the research objectives (Thompson, 2012; Tryfos, 1996). Thus, the respondents are selected because of some desired characteristics. There are several types of purposive sampling techniques (Figure 3, Appendix 1). This paper discusses only several types of purposive sampling techniques that are relevant to big data Extreme Case Extreme Case, also known as deviant case sampling, is a type of purposive sampling that is used to focus on cases that are extreme, such as deviant buying behaviour, notable product failure, crises, and others (Lewis-Beck et al, 2004; Marshall, 1996). These extreme cases are useful because they often provide significant insight into a particular marketing phenomenon, which can act as lessons (or cases of best practice) that guide future practice. In some cases, extreme (or deviant) case sampling is thought to reflect the purest form of insight into the phenomenon being studied. For example, if marketing students are interested in studying a consumer s addiction to plastic surgery or deviant purchasing behaviour, they need to sample extreme case sampling (Figure 3) of repeat plastic surgery customers, which do not follow regular patterns of buying behaviour. By studying these deviant cases, marketing students can gain a better understanding of deviant buying behaviour. Sample sizes in purposive sampling are normally fixed prior to data analysis (Binsardi and Green, 2012; Thompson, 2012; Tryfos, 1996) depending on time, resources, and the research objectives. Hence, this extreme case sampling is suitable for collecting social media (big) data by selecting particular Twitter threads on extreme cases of repeat plastic surgery Maximum Variation The opposite of homogeneous sampling is maximum variation sampling (MVS). Using MVS, marketing students need to select the participants who view that customer satisfaction is important as well as the ones who consider that customer satisfaction is not important. For example, Figure 4 (Appendix 1) contains participants, regardless of their age and income, who consider that customer satisfaction is important. There are 30 opinions via LinkedIn, 74 opinions on Twitter, and 30 opinions on Google, which all can be served as raw data to be coded and analysed by qualitative analysis. In addition, marketing students need to find a variety of participants in terms of gender, education, income, etc. (who view that customer satisfaction is important ) as indicated by Figure 5. Hence, instead of interviewing selected respondents of a particular ethnicity, a study can interview all ethnic groups within a city by including several stratifications or demographic classes, such as low income, middle income or higher income respondents, male and female respondents, and the geographical locations of the respondents. Unfortunately, it is not practical to vary the respondents in social media (big) data because of limited information. That is, marketing students know only a participant s Twitter address, such

4 4 However, they can trace an IP address from the participant s ISP provider to a specific region and country. This geographic differentiation (Figure 5, Appendix 1) can be used for diversifying samples based on a variety of locations to achieve MVS. Figure 5 reveals MVS can be applied by recruiting different respondents to maximize the variety of respondents opinions with regards to the issue of customer satisfaction. Relevant literature (Binsardi and Green, 2012; Binsardi and Mclean, 2008) indicates that MVS leads to optimal sampling diversity. However, the goal of MVS is not to build random and generalizable samples, but rather to represent an optimal range of opinions, attitudes, and experiences related to the subject under investigation. Traditionally, qualitative analysis uses non-probability sampling and quantitative analysis employs probability sampling (Lavrakas, 2008; Creswell, 2008). Hence, purposive sampling techniques are normally employed in qualitative analysis because it does not aim to produce a result that is statistically representative. However, purposive sampling is a common source of sampling error and bias (Binsardi 2008, Lewis-Beck et al 2004, Rao 2000) as indicated by a measure of sampling error. Correspondingly, If Mean Square Error (MSE) can be used as a method of sampling error (Equation 1, Appendix 2), the use of narrowly defined purposive sampling, leads to a higher instance of sample error. This is due to increased variance (sampling error variability) and bias (systematic sampling error). Accordingly, applying narrowly defined purposive sampling to big data such as Twitter threads or Facebook posts, can lead to data inadequacy due to bias, error and lack of diversity. This reduces the possibility of fresh insights into the data. In addition, although predictions from these types of purposive (non-probabilistic) sampling procedures can sometimes be close to the population values, their success cannot be guaranteed (Lewis-Beck et al 2004, Rao 2000, Scheaffer and Mendenhall 1990, Stuart 1984, Sudman 1976) since the errors in these predictions cannot be estimated from this method. This is not the case with probability sampling procedures such as random stratification (Equation 4, Appendix 2). Enhanced purposive sampling methods such as theoretical sampling and maximum variation sampling are therefore recommended, because these accurately reflect the diversity and breadth of the population of Twitters, blogs, Facebook and other social media data Theoretical Theoretical sampling is a central tenet of the Grounded Theory (GT) methodology advocated by Glaser and Strauss (1967). This particular method of sampling differentiates GT from other qualitative research methodologies. In this paper, theoretical sampling can be loosely defined as a continuous gathering of samples for the purpose of developing theoretical ideas, as opposed to exploring findings, testing hypotheses or representing the population. Unfortunately, theoretical sampling is frequently confused with purposive sampling in relevant empirical literature (Sandelowski, 1995; Breckenridge and Jones, 2009; Binsardi and Green, 2012). In the case of purposive sampling, samples or respondents are purposefully determined in advance as a part of the research process (Figure 6). For example, marketing students may decide to collect a deliberately fixed number of respondents as data to be processed by Qualitative Analysis. On the contrary, in GT methodology only the first respondent or sample is determined purposefully, with the second, third and other respondents being decided as part of an ongoing process. This type of sampling can be effectively applied in the collecting and analysing of big data, such as Twitter threads. Hence, in the data collection and coding process of such media, marketing students are required to add to the samples by applying the concept of theoretical sampling (the process of adding more respondents according to the needs of a particular marketing research project). Additional participants for theoretical sampling may be required according to the needs that arise in the analysing of the initial samples. The first respondent, however, can be selected purposefully from the owner opinion of a particular Twitter thread. However, according to relevant literature (Cohen and Crabtree 2006; Binsardi, 2008), theoretical saturation can be reached prematurely if one's sampling frame is too narrow and the

5 5 method employed does not produce rich, in-depth information. diversity in Twitter threads, therefore, is prerequisite in GT methodology. This ongoing enhancement of sampling diversity aims to employ as much data variation as possible. diversity enables students to enhance the scope and richness of big data, while gaining fresh insights from Twitter threads due to the emergence of new categories (Glaser 2004, 1998, Strauss and Corbin 1990) Total Population There is a total population sampling technique commonly known as census, in which researchers study an entire population with a particular set of characteristics, experiences, specific knowledge, or exposure. This technique can be applied to big data. Marketing students may research whole Twitter threads, Facebook posts, and blogs in order to gain a more comprehensive analysis. In some cases, the size of the population (twitter threads, Facebook comments etc) is relatively small, so that marketing students can include all the respondents as census. This is because, if a small number of respondents were not included comprehensively, it may be felt that a significant piece of the puzzle was missing. However, sometimes the population of a particular blog or Twitter thread is very large. Certain Twitter topics have thousands or even millions of followers. Accordingly, it becomes time-consuming, demanding of resources, and generally unmanageable to take a census of big data to be analysed. 2.3 Stratified Random Samples used in qualitative research are usually non-random (non-probability) samples because qualitative research is more concerned with generalising findings to theory development ( interpretivist ) rather than to populations ( positivist ) (Thompson, 2012; Lewis-Beck et al., 2004; Tryfos, 1996). Because of this 'traditional divergence in the positivist versus interpretivist debate, most scholars have been reluctant to apply a more robust sampling strategy in qualitative research or to use a more innovative sampling technique. This, in turn, has blocked the development of methodological creativity in qualitative research (Binsardi and Mclean, 2008). This paper also proposes random stratification for collecting big data such as Twitter threads instead of purposive sampling. Here, stratified random sampling is defined as the division of the target population into strata or groups that are linked by some demographic characteristic, such as age or income, to minimise bias. The main purpose of random stratification is to estimate population characteristics. The units within each stratum should be close to each other, but the means of the strata should differ as much as possible to enhance the sample diversity. In reality, since most respondents in Twitter threads are heterogeneous and knowledge of a particular topic is randomly distributed in the population, the use of random stratification leads positively to sampling efficiency, i.e., higher precision, leading to increased data diversity (Equation 3). According to the literature on sampling and random stratification (Rao 2000, Stuart 1984, Cochran 1946, 1977, Raj 1968), when a theoretical population consists of G strata of sizes N, the theoretical observation of the strata can be represented by xgi, g = 1, 2, 3..G and i = 1, 2, 3, Ng. The total and mean and the variance of the observations of the gth stratum can be obtained using Equations 4 and 5 (Appendix 2). From the equations, the advantages of random stratification in GT are estimates for each stratum that can be obtained individually, and the total and mean of the population can be estimated efficiently with higher precision. This leads to greater data diversity than that derived from using a non-probabilistic sampling procedure. 2.5 Systematic Random A systematic random sampling technique can be defined as probabilistic sampling, which selects random samples from a larger population. The recruitment process starts by randomly selecting the first respondent as a starting point, and then additional respondents can be obtained by employing a constant interval between the samples taken to achieve the desired number of observations. That is, in systematic random sampling, only the first respondent is selected at random. The rest of the sample is selected according to a predetermined pattern. This type of sampling can be employed for collecting and analysing big data. For example, if the total population of Twitter

6 6 threads is 500 (=n) and 5% of the population is required as a sample, the number of respondents needed is (5%)*(500)=25 Twitter users. In this case, the required sample size (n) is n=25. Accordingly, the interval or k is N/n=k, which equals 500/25=20. Marketing students are required to select the first respondents randomly from respondent number 1 to respondent number 500 using a random number generator (RNG). Say that the first respondent selected by RNG is respondent number 5. The second respondents is respondent (=5+20) number 25. The third respondent is number 45. The numbers of the rest of the respondents are listed in Table 1. Accordingly, marketing students must initially find the size of the population (Twitter threads) as N. Then, they must calculate the number of required respondents. Normally, 5% of the population is required as the minimum sample size in quantitative research ((Creswell, 2008; Cochran, 1977). The sample will be selected from the population with serial indexes or numbers as indicated by Equation 6 (Appendix 2). In practice, systematic sampling has many attractive features for marketing students because it provides a better random distribution than stratified random sampling and is simple to implement. Marketing students may start without a complete listing frame of particular Twitter threads (perhaps a listing of every tenth participant responding to particular Twitter threads). In addition, with an ordered list, the variance of systematic random sampling may be smaller than that of stratified random sampling (Binsardi and Green, 2012). 3. Summary and Conclusion This paper advocates the use of non-probabilistic sampling such as enhanced purposive sampling, maximum variation sampling (MVS), theoretical sampling, and probabilistic sampling, such as stratified and systematic random sampling techniques, to attain sampling diversity in analysing big data. Although demographic variables such as gender, income, and age group are commonly used strata, along with other time and contextual variables, such stratification frames are not available in big data. That is, respondents can be identified only by their addresses or Twitter usernames. Accordingly, stratification can be based on the respondents Internet service providers (ISPs) and geographical location, such as the UK, the USA, Malaysia, Brazil, and others since the ISP of a Twitter username can be determined using the respondent s Internet Protocol (IP) address. A non-probabilistic sample technique such as simple convenience sampling may be suitably used when the population is homogeneous. The respondents or opinions used in big data, such as Twitter threads, varies so widely (children, teenagers, older people, people with different professions, ethnic origin, age, income, gender, etc.) that the population can be considered heterogeneous. When the population is heterogeneous, the use of simple convenience sampling may yield misleading results because sampling diversity cannot be achieved. It is therefore recommended to use enhanced purposive sampling or stratified and systematic random sampling. This proposal is intended to enhance both qualitative and quantitative research findings. In addition, it provides support to generalising a new theory interpretation to larger groups (populations). Findings of new insights that are more robust or revived theories will accelerate because of the increased diversity of the collected data. The sampling recommendations adopted in this paper linking big data with the two types of research paradigms (positivist and interpretivist) are consistent with the views of scholars who have aimed at conducting more robust and consistent research on the prospects of complementary integration (Pidgeon and Henwood 2006, Bryman and Hardy 2006, Denzin and Lincoln 2004, Gray et al. 2003, Bryman 1988, 1992) instead of collecting and analysing big data separately in qualitative or qualitative research. This paper pedagogically offers an initial conceptual and empirical discussion that is useful for marketing students and marketing researchers by providing direction for analysing big data.

7 7 Appendix 1 Figure 1 Big Data from Social Media Landscapes Figure 2 Techniques Convenience Non probabilistic or non random sampling techniques Purposive Quota Techniques Snowball Simple Random Probabilistic or random sampling techniques Stratified Random Systematic Random Cluster Random

8 8 Figure 3 Types of purposive sampling techniques Purposive Maximum Variation Extreme Case Theoretical Total Population Figure 4 Twitter threads ( customer satisfaction is important )

9 9 Figure 5 Maximum Variation Respondents MVS Income Gender Education Geography Lower Income Respondents Male Respondents High School Graduates Urban Respondents Middle Income Respondents Female Respondents UG Degrees City Respondents Higher Income Respondents PG Degrees Village Respondents Higher Degrees Figure 6 in Input-Process-Output (IPO) INPUT Samples PROCESS Qualitative Analysis Samples (Grounded Theory) OUTPUT Research Findings Reports Table 1 Systematic Random (N=500; n=25 and k=25) Respondent No. Respondent No. Respondent No. Respondent No. Respondent No

10 10 Appendix 2 Equation 1 E( x) ( x) where E stands for expected values, x for sample means, µ for population means, for standard deviation of the population, s for standard deviation of the samples, n for number of observations or samples, and superscript bar for average values. Equation 2 E( s ) ( x) n where E stands for expected values, x for sample means, µ for population means, for standard deviation of the population, s for standard deviation of the samples, n for number of observations or samples, and superscript bar for average values. Equation MSE( x) E[ x E( x) ] E[ x X ] Var( x) Bias ( x) Where MSE stands for Mean Square Errors, E for expected values, x for sample values, X for population values and Var for variance. Equation 4 The total and mean and the variance of the observations of the gth stratum can be given by: N g xg X g xgi and xg N 1 g Where G stands for strata of the population, N for the number of strata, the theoretical observation of the strata can be represented by x gi, g = 1, 2, 3..G and i = 1, 2, 3,..N g. Equation 5 S N g 2 1 g ( x X ) gi N g 1 g 2 Where G stands for strata of the population, N for the number of strata, the theoretical observation of the strata can be represented by x gi, g = 1, 2, 3..G and i = 1, 2, 3,..N g. Equation 6 r, r k, r 2 k, r 3 k, r 4 k, r 5 k,..., r ( n 1) k where r stands for the first respondent selected by RNG, N for the population size, n for the number of respondents required, and k for the sampling interval. k must be an integer. If k is not an integer, the solution is to round k to an integer to produce n respondents.

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