EXTENDING OUR UNDERSTANDING OF CONSUMERS ewom BEHAVIOR: GENDER AND GENERATION DIFFERENCES

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1 EXTENDING OUR UNDERSTANDING OF CONSUMERS ewom BEHAVIOR: GENDER AND GENERATION DIFFERENCES Ali Naci KARABULUT Fethiye Faculty of Management, Mugla Sitki Kocman University, Mugla, Turkey Zeki Atıl BULUT Department of Marketing, Dokuz Eylul University, Izmir, Turkey ABSTRACT Electronic word-of-mouth (ewom) has become one of the primary and preferred information sources for consumers in the process of evaluating alternatives and purchasing online. However, not all consumers have the same behaviors in ewom. By using a sample of 524 consumers, this study compares the ewom behavior of Generation X and Y together with gender differences. Results show that, although males and females behaviors are strikingly similar, there are significant differences in ewom behaviors between Generation X and Y consumers. Few differences were found between Gen X and Y in ewom experience, credibility of ewom, consumer susceptibility to interpersonal influence (CSII), ewom effect and positive valence ewom behavior. However, no differences were observed in negative valence ewom behaviors of Gen X and Y. In closing, theoretical and managerial implications for marketing theory and managers are discussed while important limitations are recognized. Keywords: ewom, ewom behavior, Gender, Gen X, Gen Y 1. INTRODUCTION Most of the time it is not possible to satisfy the entire consumer market with one product or service because there are many different groups of potential buyers with similar needs or characteristics or that display similar behaviour. These groups are known as market segments and each segment seeks a unique set of benefits from the product or service purchased (Stone & Desmond, 2007, p ). That s why companies identify smaller segments in large and heterogeneous markets to offer better products and services that match their unique needs efficiently and effectively (Kotler & Armstrong, 2008, p. 185). The main segmentation variables for consumer markets can be grouped under four headings; geographic, demographic, psychographic and behavioral. Also, age and gender, which are included in demographic variables, are two of the main segmentation criteria (Kotler & Armstrong, 2008, p. 186; Stone & Desmond, 2007, p. 175). Marketers are interested to define and classify the age structure of consumers globally and use three main and successive generations for this aim. Baby Boomers are the first of these generations whose dates of birth are between 1946 and Demographers termed this generation because of the population-boom period following the end of World War II (in 1945) (Shimp, 2007, p ). The two generations that followed are accepted as X generation for those who were born between 1965 and 1979, and Y generation for those who were born between 1980 and 1999 (Crampton, Hodge, 2009, p. 1). Nowadays, when considering that members of the baby boomer generation are retiring and that members of Generation Y have been entering the workforce, determining the characteristic differences between those two generations (X and Y) in particular is important for marketers (Reisenwitz & Iyer, 2009, p. 91). At the same time, it is possible to see from numerous academic research

2 results that some perceptional and behavioural differences exist between female and male consumers. For example, Bakewell and Mitchell (2006, p. 1299) found that men are likely to have different decision-making styles to women while the Garbarino and Strahilevitz study (2004, p. 773) reported women s higher risk perceptions in online shopping as against men s. Contributing to this research field, this study focuses on the impact of gender and generation differences in how people use and impress with ewom. We examine gender and generational differences in terms of ewom behaviours. 2. LITERATURE REVIEW For marketers, the purpose is to predict and direct the behavior of the individuals involved in those groups by means of determining the differences between demographic groups like age or gender. In fact, marketers want to direct the individuals in the target group to buy or indulge in behaviours like sharing messages that promote other individuals to buy. Consumer reports of unqualified opinions about brands and products offer a strong advantage in competition, so much so that there exists a marketing approach called Word-of-Mouth (WOM) marketing that provides and encourages those shares. In this marketing approach, companies seek to identify influential individuals who are early adopters, vocal and curious and with a large network of acquaintances, and try to bring their new products to the attention of these influentials. In this manner, these influentials act as unpaid salespeople who are much more persuasive than any advertisement or salesperson (Kotler, 2003, p. 185). The Internet, which provides consumers with access to all kinds of information easily and allows them to share that information with large numbers, brings power to classical WOM and causes the emergence of the new term electronic word-of-mouth (ewom). ewom, which is more accessible and more powerful than the classical WOM (Akyüz, 2013, p. 159), possesses unprecedented speed of diffusion and enables multi-directional exchanges of information between communicators and receivers (Cheung & Thadani, 2012, p. 468). There are various behaviors that are related to or define ewom in the literature. Some of those behaviors, which underlie the scope of this research, are; using experience of ewom, perceived ewom credibility, consumer susceptibility to interpersonal influence (CSII), ewom effect (Park et al., 2011, p ; Maria et al., 2016, p. 1088; Akyüz, 2013, p ), positive valence ewom and negative valence ewom (Goyette et al., 2010, 11). Many other marketing scholars conducted studies aspiring to contribute to the understanding of this new term and behaviour from different viewpoints, and some of those studies were related to gender and age variables. Strutton et al. (2011, pp. 559) claimed there are structural differences between the X and Y Generations regarding the media that are used to spread e- WOM messages. They suggested that Generation Y is more heavily engaged with social networking media while Generation X is more reliant on . Besides, San-Martin et al. (2015, p. 1) suggested that there are some significant differences between age groups while Strutton et al. (2011, p. 582) highlighted that generational differences are blurred in e-wom behaviors. Thus, we hypothesized that: H 1 : Generation Yers show a higher level of ewom behavior than Gen Xers. Also, there are many studies in the literature that found distinct differences between genders in the matter of evaluating ewom messages. According to Kim et al. (2011, p ) men s use of online reviews depended on their level of expertise while expertise isn t a factor for females. In spite of this finding, Fan and Miao (2012, p. 178) claimed that involvement is the only factor on perceived ewom credibility for male customers, while expertise, involvement, and rapport have significant effects on females perceived ewom credibility. Again, according to Kim et al. (2011, p. 399), women are more likely to read reviews for the

3 purposes of convenience and quality and for risk reduction. In accordance with this, Abubakar et al. (2016, p. 702) emphasized that the risk perception of females in online shopping is higher than males, and thinks that this may be one reason why females are more affected by the brand image of ewom than males. Furthermore, there are controversial findings in previous studies concerning gender and generational differences in ewom communication. For instance, Maceli et al. (2015, p. 288) revealed that females are more likely to tell others about their buying processes, while Cataluna et al. (2014, p.23) found that males give more importance than females to recommendations in case of analyzing and purchasing the search goods on the Internet. Regarding the relevant literature and objectives of the present research, we hypothesized that: H 2 : There is a significant difference between males and females in terms of ewom behaviors Last, according to the main hypotheses and the six identified ewom behaviors, we developed six sub-hypotheses for each main hypothesis. 3. METHOD We use a cross-sectional survey to determine the ewom behaviour of consumers from Gen X and Y and whether there are significant differences in term of their gender. Items used in this study to measure research variables have been compiled from different studies. Using experience of ewom, perceived ewom credibility, consumer susceptibility to interpersonal influence (CSII), and ewom effect were measured with items adapted from Park et al. (2011, p ). However one item for CSII was deleted because its factor loading is less than 0.5. We measured positive valence ewom using six items and negative valence ewom with two items which were adapted from Goyette et al. (2010, p.11). All construct were measured using a five-point Likert scale, ranging from 1=strongly disagree to 5=strongly agree. In the last section of the questionnaire, socio-demographic variables such as gender, age, income, weekly time spend on Internet and online shopping and frequency of online shopping were measured. Data for this study were collected using face-to-face questionnaire. We collected data from online consumers who are in Gen X and Gen Y who had experience with online purchase in last three month. A total of 600 questionnaires presented to the potential respondents by using convenience sampling technique in Izmir, the third biggest city in turkey. 578 of them were completed, resulting in a 96.3 percent response rate; 510 usable responses were used for analysis, due to missing data or non-qualified responses. A confirmatory factor analysis (CFA) was then performed to verify the validity and reliability of constructs. After, we run Kolmogorov-Smirnov and Shapiro-Wilk tests to check normality of data. In order to test hypotheses and sub-hypotheses we used Mann-Whitney U tests. The proportions of male and females were very similar with 51.6 percent of females. Our sample consists of 51.6 percent of Gen Y and 48.4 percent of Gen X. Of the participants, most of them (76.4%) had annual personal income levels of less than 10,000. Respondents Internet usage ranged from 1 to 100 hours in a week with an average weekly Internet usage of hours. They shop 2.78 times average in the last month on Internet shopping and spend approximately 70 on online shopping in a month. 4. RESULTS 4.1 Reliability and Validity Regarding reliability of variables, Cronbach s α coefficients and composite reliability (CR) values were measured. Items used to measure variables, item and variable means, standard

4 deviations, factor loadings, average variance extracted (AVE) and reliability values are shown in Table 1. Table 1: AVE, CR, CA, item mean, standard deviation and factor loading values of the variables Variable AVE CR Α Item Item mean SD Factor loading Using experience of ewom (UEeW) (M=3.22) Perceived ewom credibility (PeWC) (M =3.02) CSII (M =3.38) ewom effect (ewe) (M =3.08) Positive valence ewom (PVeW) (M =3.41) Negative valence ewom (NVeW) (M =3.10) I always read online reviews written by others I always write down online review by myself I always share my knowledge and information I always read online consumer reviews when I was shopping I always write down online consumer reviews when I was finishing my shopping I believe the online review which been read a lot I believe the online review which is believed by others I believe online review is important I believe online review is credible information I believe online review is written with responsibility I like listening advices before shopping Others advices are important for my shopping I will buy things because online review is positive I rely on online reviews when I purchase Online review affects my purchase decision crucially I recommended this company I speak of this company s good sides I am proud to say to others that I am this company s customer I strongly recommend people buy products online from this company I mostly say positive things to others I have spoken favourably of this company to others I mostly say negative things to others I have spoken unflatteringly of this company to others AVE: Average variance extracted; CR: Composite reliability; α: Cronbach s alpha; SD: Standard deviation; M: Mean As shown on Table 1, all item loadings were significant (p<0.001) and both Cronbach s alpha and composite reliability values ranged from 0.72 to 0.91, greater than the recommended value Thus, the reliability analyses indicated a good reliability of the measures used in the research. Validity of the measures was assessed using a confirmatory factor analysis (CFA). CFA provided evidence for good fit between the measurement model and the data (χ2=715.12; df=207; χ2 /df=3.45; GFI= 0.89, AGFI=0.86, IFI=0.93, NFI=0.91, CFI=0.93, RMSEA=0.067). Convergent validity was examined by using CR and AVE values. As it can be seen from Table 1, all CR values were higher than 0.72, AVE values were higher than

5 0.56, item loadings investigated significant, and exceeded 0.5 which means the convergent validity is established. For discriminant validity, we compared the square root of the AVE score of each construct with its cross-correlation with other constructs. As presented in Table 2, all AVE scores surpassed the squared correlations which indicate the discriminant validity was achieved. Table 2: Discriminant validity Variable NVeW (1) UEeW (2) PeWC (3) CSII (4) ewe (5) PVeW (6) Notes: The diagonal elements in bold represent the square root of AVE NVeW: negative valence ewom; UEeW: using experience of ewom; PeWC: perceived ewom credibility; CSII: consumer susceptibility to interpersonal influence; ewe: ewom effect; PVeW: positive valence ewom 4.2 Hypothesis Testing Before testing hypotheses, a test of basic assumption is usually performed to choose the appropriate statistical test. We run Shapiro-Wilk and Kolmogorov-Smirnov tests to check the normality of data. We found that all significance levels are lower than It means all variables in research are shown non-normally distribution. Therefore, we use Mann-Whitney U test which is a kind of nonparametric test to compare males and females, Gen Xers and Yers ewom behaviors. The statistical analysis is carried out by using SPSS version The significant (p) values <0.05 are considered as statistically significant. The results of tested hypotheses are presented in Table 3. All hypothesized relationships between gender and ewom behaviors of Xers and Yers were found statistically nonsignificant. Although, males using experience of ewom and intent to share negative ewom messages were found higher than females and females perceived ewom credibility, CSII, ewom effect and intent to share positive ewom messages were higher than males, these differences were not significant. Therefore, H 1 was not supported. However we found some significant differences in ewom behaviors of people in terms of their generation. More specifically, one of our sub-hypothesis of H 2, H 2a which predicts the significant differences between Gen Xers and Gen Yers using experience of ewom, was supported. Gen Yers have more using experience of ewom than Gen Xers. With respect to H 2b, perceived ewom credibility of Gen Yers is higher than Gen Xers. Sub-hypothesis H 2c examined the difference between Xers and Yers in CSII, which was also found to be higher in Yers than Xers. Similarly H 2d, Yers was found to be more impressed by ewom than Xers. Finally, H 2e and H 2f, which predict the differences between generations in spreading both positive and negative valenced ewom were supported. Gen Yers intent on spreading and talking about their experienced by using ewom more than Xers. Variables Table 3: ewom behavior in terms of gender and generational differences Mean Rank Mean Rank p U Male Female p U Gen X Gen Y Using experience of ewom (H 1a -H 2a ) * Perceived ewom credibility (H 1b -H 2b ) * Consumer susceptibility to interpersonal *

6 influence (H 1c -H 2c ) ewom effect (H 1d -H 2d ) * Positive valence ewom (H 2e -H 2e ) * Negative valence ewom (H 1f -H 2f ) * : Statistically significant differences in p<0.05 (Mann-Whitney Test) Additionally, we analyzed the differences between males and females ewom behaviors separately in two generations in order to investigate gender differences thoroughly. We found significant differences only in Gen Yers two ewom behaviors. CSII (U=7191.5; p=0.018) and intent to spread positive valence ewom (U=7313.0; p=0.032) were found to be higher in females than males. 5. DISCUSSION AND CONCLUSION Studies in the literature, which explain the ewom behaviors of consumers, reveal that behaviors of ewom vary according to the various features of consumers. There is no doubt that examining the ewom behaviors of consumers in terms of demographic features like gender and generation will bring a detailed point of view to the topic. In this study, behaviors that are accepted as ewom behaviors like using experience of ewom, perceived ewom credibility, consumer susceptibility to interpersonal influence (CSII), ewom effect, positive valence ewom and negative valence ewom are examined in terms of whether they differentiate according to the gender and generation of consumers or not. There are three remarkable fields of interest in the results. First, when the frequency of the exhibiting behaviors is examined, it is determined that the behavior of positive valence ewom spreading is exhibited more commonly than negative valence ewom spreading. In addition, it is determined that consumers response to ewom and their trust in ewom is at the middle level, and even if the CSII level is higher, transforming the ewom effect is less frequent. Second, our results showed that male consumers using experience of ewom is more than that of females. This finding is consistent with Kim et al. (2011, p. 403), and reveals the fact that males generally make and spread more ewom reviews. Thus, one can deduce that businesses choose males as the primary target group in studies about ewom. In any case, while negative valence ewom spreading is higher in males, positive valence ewom spreading is higher in females. This finding is partially parallel to the opinion of Maceli et al. (2015, p. 288) that female intentions of talking about the shopping process is more than those of males. This is because in an acquired experience of buying, satisfying the expectations when one has bought before is a situation that can be called routine. In those kinds of routine situations, the lower sharing of males in comparison to females can be interpreted as they are less willing to have those types of shares, and they only do it when they have to. Results show that females not only have positive share intentions but also perceived ewom credibility, levels of CSII and ewom effect are higher than those of males. The finding of ewom effect seems inconsistent with the finding of Cataluna et al. (2014, p. 23) that males attach more importance to recommendations in the Internet environment. When these two findings are evaluated, it is revealed that although males attach more importance to ewom than females, they might be behind females in terms of transforming the information they got in the ewom process to buying behavior. Therefore, businesses choosing females as the primary target group while conducting ewom activities considering that they are more effective in transforming the ewom effect into buying and thus may cause more successful ewom results. However, that the differences between genders are not at a level that can be scientifically accepted necessitates being cautious while making such deductions.

7 Also, when the ewom behaviors between X and Y generations are compared, significant differences are detected. Results reflecting ewom experiences, perceived ewom credibility, CSII positive valence ewoms and influenced by ewoms of consumers in the Y generation come out at significantly higher levels in comparison to Generation X. Our results support the age differences and provide strong evidence to crystallize the uncertainty stated by Strutton et al. (2011, p. 582). However, we did not find significant differences in negative valence ewom behavior between Generation Xers and Yers. These findings reveal that ewom is a much more effective tool in reaching consumers from the Y generation, and show that a business that targets the Y generation should take the ewom topic more seriously. In conclusion, when the gender differences between X and Y generations are examined, it is determined that in the X generation there is no ewom behavior differentiation according to gender while in the Y generation, CSII and positive valence ewom spreading behaviors are higher in females. This finding reveals that in the Y generation, when ewom messages are more effective, both males and females are more sensitive to ewom. There are several limitations to the study that should be mentioned. The participants were chosen only in one city and country because of convenience. Therefore, the findings should be validated in multiple cities or countries to define possible cultural differences. Another limitation of the study is considering all types of products and social media websites, instead of specifically focusing on one product such as electronic devices or only ewom messages in one social media website such as Facebook or Instagram. Also, a comparison between different products or social media websites could provide valuable and interesting insights both in theoretical and managerial perspectives. Although limited in scope, the findings of the current study enhance our understanding of several ewom behaviors of consumers from Gen X and Gen Y. The findings also provide managers with strategic directions for rethinking their communication strategies in both media choice and consumer targeting. 6. REFERENCES Abubakar, A. M., Ilkan, M., Sahin, P. (2016). ewom, ereferral and gender in the virtual community, Marketing Intelligence & Planning, 34 (5), Akyüz, A. (2013) Determinant Factors Influencing E-WOM, Mediterannean Journal of Social Sciences, 4 (11), Bakewell, C., & Mitchell, V-W. (2006) Male versus female consumer decision making styles, Journal of Business Research, 59, pp Cataluna, F.J.R., Gaitan, J.A. & Correa, P.E.R. (2014). Exploring the influence of ewom in buying behavior. The International Journal of Management Science and Information Technology, 14, Cheung, C. M. K., Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), Crampton, S., M., & Hodge, J., W. (2009). Generation Y: Unchartered Territory, Journal of Business & Economics Research, 7(4), pp Fan, Y-W. & Miao, Y-F (2012). Effect Of Electronic Word-Of-Mouth On Consumer Purchase Intention: The Perspective Of Gender Differences, International Journal Of Electronic Business Management, 10(3), pp Garbarino, E., & Strahilevitz, M. (2004) Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation, Journal of Business Research, 57, pp

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