extending our understanding of consumers' ewom ...

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Dec 10, 2016 - (M =3.38). 0.587 0.739 0.734 I like listening advices before shopping. 3.51 1.11 0.704 .... These findings reveal that eWOM is a much more effective tool in reaching consumers ... as Facebook or Instagram. Also, a comparison ...
18th International Scientific Conference on Economic and Social Development – “Building Resilient Society” – Zagreb, Croatia, 9-10 December 2016

EXTENDING OUR UNDERSTANDING OF CONSUMERS’ EWOM BEHAVIOR: GENDER AND GENERATION DIFFERENCES Ali Naci Karabulut Fethiye Faculty of Management Mugla Sitki Kocman University, Mugla, Turkey [email protected] Zeki Atil Bulut Department of Marketing Dokuz Eylul University, Izmir, Turkey [email protected] 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. 173-174). 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 behavioural. 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 1964. Demographers termed this generation because of the population-boom period following the end of World War II (in 1945) (Shimp, 2007, p. 99-106). 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 154

18th International Scientific Conference on Economic and Social Development – “Building Resilient Society” – Zagreb, Croatia, 9-10 December 2016

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 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 behaviour 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 behaviours that are related to or define eWOM in the literature. Some of those behaviours, 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. 75-76; Maria et al., 2016, p. 1088; Akyüz, 2013, p. 106-161), 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 email. 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 behaviours. Thus, we hypothesised that: H1: Generation Yers show a higher level of eWOM behaviour 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. 401–404) men’s use of online reviews depended on their level of expertise while expertise isn’t a factor for females. In 155

18th International Scientific Conference on Economic and Social Development – “Building Resilient Society” – Zagreb, Croatia, 9-10 December 2016

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 purposes of convenience and quality and for risk reduction. In accordance with this, Abubakar et al. (2016, p. 702) emphasised 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 hypothesised that: H2: There is a significant difference between males and females in terms of eWOM behaviours Last, according to the main hypotheses and the six identified eWOM behaviours, 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. 75-76). 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 29.51 hours. They shop 2.78 times average in the last month on Internet shopping and spend approximately €70 on online shopping in a month. 156

18th International Scientific Conference on Economic and Social Development – “Building Resilient Society” – Zagreb, Croatia, 9-10 December 2016

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 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

Using 0.598 0.880 experience of eWOM (UEeW) (M=3.22)

Item

0.877

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

3.39 3.03 3.14 3.52

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

3.06 1.07 0.851 3.04 1.05 0.910 3.13 1.12 0.837

Perceived eWOM credibility (PeWC) (M =3.02)

0.648 0.902

CSII (M =3.38)

0.587 0.739

0.734

eWOM effect 0.751 0.900 (eWE) (M =3.08)

0.898

Positive valence eWOM (PVeW) (M =3.41)

0.895

Negative valence eWOM (NVeW) (M =3.10)

0.572 0.889

0.566

0.722

Item Factor SD mean loading

Α

0.907

0.830

1.16 1.19 1.17 1.09

0.652 0.886 0.828 0.679

3.03 1.18 0.795 2.97 1.17 0.756 2.82 1.14 0.786 3.14 1.10 0.841 3.04 1.06 0.873 3.08 1.07 0.764 3.51 1.11 0.704 3.25 1.08 0.824

3.49 0.99 0.793 3.50 0.96 0.807 3.28 1.09 0.790 3.07 1.12 0.725 3.59 0.97 0.706 3.52 0.95 0.709 3.47 1.11 0.791 2.74 1.18 0.711

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