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Impact of Consumers' Online Motivations on the Online Purchase Intentions: Mediating Role of Consumers' Attitudes towards Social Media Marketing.

1. Introduction

Social media which comprises of Facebook, Twitter, YouTube, Instagram and Pinterest etc. has gained an unprecedented acceptance in the lives of about 3.196 billion people around the world (Kemp, 2018). Today's consumers devote considerable amount of their time to use social media (Windels et al., 2018). The average time spent by people on social media is 135 minutes per day (Statista, 2017). Hence, it can be said that social media has deeply penetrated in to our daily routine. The increased popularity of social media has also captured the attention of marketer since people spend considerable amount of their time on social media. Recent statistics reveal that 91% of retail brands use two or more social media channels and 81% of all small and medium businesses use some kind of social media platform for marketing purposes (Brandwatch, 2018).

Consumers are exposed to different types of advertising on social media which include banner ads, brand pages and sponsored stories etc. (Luna-Nevarez & Torres, 2015). Besides these forms of promotion, social media is also used as a platform for electronic commerce activities (Han, Xu, & Chen, 2018). It has been acknowledged that social media marketing is gaining momentum not just in advanced countries of the world like US and Canada (Zhang & Mao, 2016) but also in Asian developing countries like Pakistan (SBP, 2018).

Despite the popularity of social media marketing, some research gaps are reported in the literature. Firstly, the research on social media marketing is still in its formative years with many inconclusive and divergent findings (Duffett, 2015; Hamouda, 2018). Marketers still lack clarity about the effectiveness of social media marketing strategies in terms of influencing consumers' attitudes and purchase intentions with respect to social media marketing (Irshad, 2018; Zhang & Mao, 2016). These attitudes refer to consumers' attitudes towards all forms of explicit social media advertising or marketing like banner ads and commercial videos as well as implicit social media marketing like fan brand pages and company related tweets that are delivered through social media (Irshad, 2018; Taylor, Lewin, & Strutton, 2011), whereas, consumers' online purchase intentions refer to consumers' intentions to purchase the product that they see through social media advertisements or brand pages (Irshad, 2018). Purchase intention is an important criterion that anticipates a response behavior (Martins, Costa, Oliveira, Goncalves, & Branco, 2019). It is an important dependent variable that measures the effectiveness of advertising at different levels (Lee, Lee, & Yang, 2017). However, there is limited understanding about factors that influence consumers' online purchase intentions in social mediated marketing environment. (Bebber, Milan, De Toni, Eberle, & Slongo, 2017).

The second research gap is that researchers are of the view that one reason behind the lack of understanding about the effectiveness of social media marketing strategies in affecting consumers' attitudes towards social media marketing and purchase intentions is the lack of understanding about underlying consumers' needs and motivations (Zhang & Mao, 2016; Zhu & Chen, 2015). Consumers' motivations are defined as preceding conditions that trigger human behavior and impact how consumers exert effort in order to complete a task (Osei-Frimpong, 2019; Roberts, Hughes, & Kertbo, 2014). These online consumers' motivations cover consumers' needs and preferences and play a significant role in consumers' decision making processes including their purchase intentions (Zhang & Mao, 2016). The limited understanding about consumers' motivations presents major obstacle in designing any effective marketing strategy (Parker & Wenyu, 2019). The same holds true in case of social media marketing (Irshad, 2018; Mikalef, Giannakos, & Pateli, 2013; Zhu & Chen, 2015). However, the existing studies on consumers' motivations are scanty in the context of social media marketing (Irshad, 2018; Muralidharan & Men, 2015; Zhu & Chen, 2015).

The third research gaps deals with understanding the mediating role of consumers' attitude in affecting the relationships between consumers' motivations and behavioral intentions in the context of social media advertising (Muk, Chung, & Kim, 2014). Besides this, another research reported in the literature is that most of the previous studies on social media marketing lack explicit theories and testable hypotheses (Knoll, 2015; Zhang & Mao, 2016). The present study addresses all the above mentioned research gaps and presents and tests a conceptual model of consumer behavior in social media marketing setting by focusing on the fashion industry.

The research questions of the study are:

1. What is the impact of consumers' motivations on consumers' attitudes towards social media marketing?

2. What is the impact of consumers' motivations on consumers' online purchase intentions?

3. Does consumers' attitude towards social media marketing mediate the relationship between consumers' motivation and online purchase intentions?

Hence, the research objectives of the study are to determine the impact of consumers' motivations on their attitude towards social media marketing and online purchase intentions, and to determine the mediating role of consumers' attitude towards social media marketing in affecting the relationship between consumers' motivation and online purchase intentions. The existing research would fill these research gaps by developing a model based on Uses and Gratifications Theory incorporating three types of consumers' motivations (i.e. utilitarian, hedonic and content personalization), consumers' attitudes towards social media marketing and online purchase intentions.

2. Literature Review

Social media is defined as ''a group of Internet-based applications that are built on the ideological and technological foundations of Web 2.0, which allow the creation and exchange of user-generated content'' (Kaplan & Haenlein, 2010, p.61). Social media is a huge platform which covers number of different channels or vehicles like collaborative writing, content sharing communities, social networking sites, micro blogging sites, social bookmarking sites and commerce communities etc. (Biswas & Roy, 2014; Mangold & Faulds, 2009). Among different channels of social media, Facebook occupies the top most position in terms of its audience which is equal to one third of total world's population (Kohli, Suri, & Kapoor, 2015).

Internet in conjunction with the social networks has developed new approaches for marketing in the recent years such as the social media marketing, where the customers are influenced by the opinion and information introduced by other customers (Jara, Parra, & Skarmeta, 2014). An important point to note is that social media advertising is a general term that captures all forms of implicit advertising like banner ads and commercial videos as well as explicit advertising like brand pages and company related tweets (Taylor et al., 2011). Chi (2011) also stated that there are two types of marketing communications in social media which include interactive digital advertising and brand communities or pages. Therefore, in this research the term '' social media marketing'' is synonymously used with social media advertising.

Due to the increased popularity of social media among people, companies devote considerable amount of their marketing budgets towards the use of social media like spending on social media advertising in Asia-Pacific will reach $5.8 billion by 2019 (Forrester, 2015). Therefore, it becomes vital to understand consumer behavior in social media marketing environment (Hew, Lee, Ooi, & Lin, 2016). As already discussed in the introduction section that existing literature on social media marketing lacks sound theoretical base and testable hypotheses (Knoll, 2015), therefore the current study is based on Uses and Gratification Theory to fill the existing gap in the literature.

The primary purpose of Uses and Gratifications Theory UGT) is to explain the reasons that prompt individuals to pick a particular medium over other alternative media and to elaborate the underlying needs that inspire individuals to utilize a specific medium (Heravi, Mubarak, & Choo, 2018). Uses and Gratifications Theory (UGT) is considered as one of the most popular theories to understand consumers' motivations and their impact on behavioral intentions (Plume & Slade, 2018). It is considered as an axiomatic theory because its principles are generally accepted and applicable in various situations (Plume & Slade, 2018).

It is equally important for both physical and online stores to identify consumers' motivations with respect to their purchasing activities (Mikalef et al., 2013; Zhu & Chen, 2015). Motivations can be defined as desires to achieve goals (Chiang & Hsiao, 2015). Online purchases are the third most common activities after email and web surfing in the context of digital commerce (Jamali, Samadi, & Marthandan, 2018). It is vital to understand consumers' online purchase intentions because it forecasts consumer behavior and predicts their actual buying activities (Ariffin, Mohan, & Goh, 2018). Thus, online purchase intention is taken as the final outcome variable in the current study. The current research focuses on utilitarian motivation, hedonic motivation and content personalization motivation as potential predictors of consumers' attitudes towards social media marketing and online purchase intentions as discussed in sub sequent sections.

2.1 Utilitarian motivation

Utilitarian value emphasizes on the product-centric thinking that assists the consumers in their decision making processes (Kumar & Kashyap, 2018). It is synonymously used for information or cognitive needs (Kakar, 2017). Information holds its significance in all types of marketing programs communicated through different types of media (Lwin & Phau, 2013). In today's competitive world, marketers need to provide necessary information about a product or service to consumers in order to fulfill informational needs of the consumers (Swani, Brown, & Milne, 2014). Consumers are found to be very much concerned about the detailed product information, attributes and specifications of products, prices and updated information (Chiu, Wang, Fang, & Huang, 2014).

Keeping in view the above fact, retailers on social media through their online communities can help the individuals in finding different products by presenting the full range of products or their advertisements can direct them towards their online stores where they can find the full range of products with all the details as suggested by Liang, Ho, Li and Turban (2011). Based on the above facts, it is assumed that easy and convenient access to product information through social media marketing can help the consumers in satisfying their informative needs as they can find detailed information about products, product variety and product prices etc. The information might be present either on social media brand pages or the social media advertisements which can also facilitate the consumers to find detailed information about the products by directing them to the brands' websites as already mentioned above. Hence the above mentioned facts lead to the development of the following two hypotheses given below:

H1a: There is a significant positive impact of utilitarian motivation (information) on attitude of the consumers towards social media marketing.

H1b: There is a significant positive impact of utilitarian motivation (information) on the online purchase intentions of consumers.

2.2 Hedonic motivation

Hedonic motivation is known by different names like intrinsic motivation and entertainment motivation (Fuller, 2006; Muntinga, Moorman, & Smit, 2011). Three Fs represent the hedonic aspects of motivation namely fantasies, feelings and fun (Kakar, 2017). Psychologically, consumers love the visual depiction/symbolism of things like different products and seeing outwardly engaging and appealing things create positive feelings (Zhu & Chen, 2015). The majority of the general population i.e. somewhere around 65 and 85 percent portray themselves as 'visual learners,' framing meaning and sorting out contemplations in view of what they see as compared to what they read (Vong, 2015).

Hedonic benefits make users feel relaxed and rewarded, lighten their mood and capture their attention, surprise them and provide fun and help in engaging them (Alnawas & Aburub, 2016). Entertaining content also plays an important role in strengthening the purchase intentions of consumers (Hsu & Lin, 2016). In the context of social media marketing, variety of posts can be given to consumers in the form of aesthetically appealing product pictures and interesting stories that can target the hedonic needs of consumers and help them divert their attention from the problems of routine life. Hence it is assumed that if companies succeed in providing entertainment to the consumers by fulfilling their needs of enjoyment then it would help is developing positive attitude of the consumers towards social media marketing and would affect the online purchase intentions of consumers as well.

H2a: Hedonic motivation has a significant positive influence on attitudes of the consumers towards social media marketing.

H2b: Hedonic motivation has a significant positive influence on the online purchase intentions of consumers.

2.3 Content personalization motivation

Personalization of advertising means the extent to which the advertising message is tailored according to consumers' needs and preferences, mindset and lifestyle (Baek & Morimoto, 2012; Bleier & Eisenbeiss, 2015). From a theoretical point of view, users are better able to remember the content of customized banners as it leads to better processing (Koster, Ruth, Hamborg, & Kasper, 2015). Consumers are more responsive and get attracted towards ads that are personalized and avoid to pay attentions to ads that are not personalized (Liu, Li, Mizerski, & Soh, 2012). However, there are few researchers who consider personalized advertising as a threat to ones' privacy and consider personalization to be intrusive (Li, Edwards, & Lee, 2002). This intrusiveness causes the internet users to use different blocking and filtering tools and subscribing to do-not mail registers in order to refrain themselves from getting the ads (Johnson, 2013).

Social media provides the marketers with opportunity to target consumers on the basis of their interests and preferences (Zhu & Chen, 2015), however inconclusive results based on previous studies on digital marketing present a need to test the impact of customized ads on consumers' attitudes. Hence the next hypotheses are formulated as:

H3a: Content personalization motivation significantly influences attitudes of the consumers towards social media marketing.

H3b: Content personalization motivation significantly influences the online purchase intentions of consumers.

2.4 Attitude towards social media marketing and online purchase intention

According to American Marketing Association (2016), attitude is defined as persons' overall assessment of a concept, encompassing general feelings of likeability and favorability. When consumers feel one way or another about anything like product, service, person or any other entity then it is considered as a generalized consumer attitude that can exert an influence on the marketing of fore mentioned things either in a positive or a negative way (Hossain, Islam, & Himel, 2014). Attitude is influenced by variety of motivations, values and beliefs (Nwagwu & Famiyesin, 2016).

Attitudes develop over a period of time and are slow to change (Lien & Cao, 2014). Nevertheless, marketers consider that marketing communications like advertisements can influence attitudes of consumers either in a positive way or a negative way by gratifying different types of motivations of consumers (Kim, Sohn, & Choi, 2011). Ashraf, Thongpapanl and Auh (2014) carried out a study in order to determine online shopping intentions of Pakistani and US consumers and concluded that attitude significantly impacted that online purchase intentions of both Pakistani and US consumers. Thus the next hypothesis becomes:

H4: Attitude towards social media marketing positively influences the online purchase intentions of consumers.

2.5 Mediating role of attitude towards social media marketing

Attitude has been found as a mediating variable in different studies. Luo (2010) in his study based on the theoretical underpinning of uses and gratifications theory concluded that attitudes play mediating role between consumers' motivations and behavioral responses like website usage and satisfaction. Attitude also mediates the relationship between consumers' motives (perceived ease of use and usefulness) and intentions to shop online through online shopping stores (Ashraf et al., 2014). Attitude also mediates the relationship between consumers' motivations and intentions to use social networking sites in general (Chiang, 2013). Moreover, researchers like Muk and Chung (2014) have expressed a strong need to identify the mediating impact of consumers' attitudes with respect to motives and behavioral intentions in social media marketing setting in different countries. Based on the above discussion, the next hypotheses becomes:

H5 a,b,c: Attitude towards social media marketing mediates the relationship between utilitarian, hedonic and content personalization motivation and the online purchase intentions of consumers with respect to social media marketing.

3. Methodology

Social media is used by many fashion brands (Godey et al., 2016). However, since the consumers have become more information savvy with the emergence of new communication channels, therefore, it becomes challenging for fashion retailers to predict consumers behavior in these new settings including social media (PAS, 2015).

Therefore this research focuses on the fashion industry of Pakistan.

The present study has five constructs: Utilitarian motivation, hedonic motivation, content personalization motivation, consumers' attitude towards social media marketing and online purchase intentions. A questionnaire was developed to measure the constructs. The research used a seven point Likert-scale from 1 representing strongly disagree to 7 representing strongly agree. The items of utilitarian motivation (information) and hedonic motivation (entertainment) were adapted from Cheng, Blankson, Wang and Chen (2009). Items measuring content personalization were adapted from Mikalef et al. (2013). Items of attitude towards social media marketing were adapted from Akar and Topcu (2011). Items measuring purchase intentions were adapted from Duffett (2015). In the preamble respondents were asked to fill out the questionnaire if they use social media for commercial purposes and those who did not use it for commercial purposes were asked not to fill it. They were told in the preamble about what is social media marketing and were asked to fill the questionnaires keeping fashion retailers on social media in mind. The first section of the questionnaire survey pertained to the demographic profiles of the respondents like gender, age, income, occupation and time spent on social media; whereas the second section comprised of items pertaining to measure the constructs in the model i.e. utilitarian motivation, hedonic motivation, content personalization motivation, consumers' attitude towards social media marketing and online purchase intentions. The content validity of the instrument was tested by getting feedback from four marketing experts since the wording of many items in the questionnaire was modified in order to fit in the context of social media marketing. The experts suggested minor changes that were incorporated in the study and then final questionnaire was made by obtaining the consensus from all the experts.

Total number of social media users in Pakistan is about 31 million (PAS, 2017). We applied Krejcie and Morgan (1970) formula to determine the sample size for this population which came out to be 384.

s = X2 NP (1 - P) / d2 (N -1) + X2 P(1 - P)

where X2=(1.96)2=3.841, p=0.50, d=0.05)

s=(3.841) (31000000)(0.50)(1-0.50) /(0.05)2(31000000-1)+(3.841)(0. 5)(1-0.5)

s=29,767,750/(0.05)2(31000000-1)+(77499.9975)+0.96025

s=29,767.750/77500.95775

s=384.1

The calculated sample size of 384 means that our sample size must be at least 384. However, for better representation of data, we chose a greater sample size i.e. 800. Data were collected from Karachi, Lahore and Islamabad through convenience sampling technique as these cities are characterized by high literacy rate and employment and presence of outlets of national and international brands (PBS, 2013). Questionnaires were distributed in different universities, shopping malls, banks, telecom offices and universities. An online version of questionnaire was also developed to collect data from people residing in these cities. In order to make sure that the no one respondent answered the questionnaire twice, the online respondents were different. The online respondents were those who could not be contacted in the offline setting i.e. universities, banks and shopping malls etc. due to their availability issues at the time when questionnaires were distributed in the offline setting. Besides this, in order to ensure more certainty, we asked a preliminary question from the online sample that either they had filled the same questionnaire in the offline setting. If they had filled the same questionnaire in the offline setting then they had the option to quit the online survey. Hence, there was no duplication of respondents across our online and offline sources of data collection Respondents were informed in the preamble to fill out the questionnaire if they use social media for commercial purposes and those who did not use it for commercial purposes were asked not to fill it. They were also introduced by the term of social media marketing and were asked to fill the questionnaires keeping fashion retailers on social media in mind. Out of 800 questionnaires, 605 questionnaires were returned back. Hence the response rate was 76%.

The present study follows positivist epistemological research paradigm as hypothetical deductive approach and empirical testable theories are used to examine the influence of consumers' motivations and trust on the attitudinal and behavioural outcomes. The present study is based on quantitative research method as the study focused on testing the hypotheses and establishing the reliabilities and validities of measures. The study was correlational in nature as the study examined the salient relationships among consumers' motivations, attitude and purchase intentions. Questionnaires were used as a tool of data collection. Convenience sampling was used to collect data from the respondents as it is a popular and viable sampling technique due to the constraints of time, speed and cost to obtain enough responses (Alam & Mohamed Sayuti, 2011).

4. Results

4.1 Preliminary analysis

Prior to pursue the actual analysis, we checked the data for missing values and outliers. 72 questionnaires were filled online by the respondents and there was no issue of missing data in the online version of questionnaire. Thus, all 72 online questionnaires were usable as they were completely filled by the respondents. As far as the offline version of questionnaire is concerned, 31 questionnaires were dropped from the analysis due to large number of missing responses. There were no outliers in our data set. So after discarding 31 questionnaires, total number of usable offline sample was 502. Hence adding the online and offline sample, total usable questionnaires were 574. In the next step, we checked the normality of our data by calculating skewness and kurtosis. The cut-off criteria of +2 and -2 for skewness and kurtosis was used to determine the normality of the data (George & Mallery, 2010). The skewness and kurtosis values for all scale items were between -2 and +2, indicating a reasonably normal distribution. We then checked the multi-collinearity of the data by calculating tolerance level and variance inflation factor for our independent variables (VIF). Value of tolerance level should be greater than 0.2 (Grewal, Cote, & Baumgartner, 2004), whereas, the cut-off value for VIF is that it should be less than 10 (Hair, Black, Babin, & Anderson, 2010). The values of tolerance level for each independent variable was greater than 0.2 and the values of VIF for each independent variable was less than 10 indicating the absence of multi-collinearity in the data.

4.2 Demographics

The results of demographic analysis showed that the percentage of female respondents was 51 %, while the percentage pf male respondents was 49%. Highest percentage of respondents (28%) fell in the age bracket of 26-30 Years. With respect to qualification, majority of the respondents (38%) had Masters' degree. 56% of the respondents were employed (i.e. 56%), while 44 % were unemployed. Highest percentage of respondents i.e. 32% had earning between 50 thousand to 1 lac per month. Average amount of time spent by the highest percentage of respondents (i.e. 47%) was 1-4 hours per day.

4.3 Confirmatory factor analysis

We followed two step approach of Anderson and Gerbing (1988). Confirmatory Factor Analysis was conducted in order test the measurement model. Amos 21 was used to test the measurement model and the structural model. Different fit indices are reported for model fitness (Kline, 2016). The results show that the measurement model had reasonable fit indices with CFI: 0.931, CMIN/DF: 2.886, RMSEA: 0.057 and SRMR: 0.0491. One criteria to estimate convergent validity is that each factor loading should be at least 0.5 (Anderson & Gerbing, 1988). However, the results showed that all indicator loadings were above 0.5 except one item of utilitarian motivation that showed 0.46 loading which also decreased the AVE of this construct. So this item was eliminated. After eliminating this item, the measurement model was run again and the results are presented in Table 1. Convergent validity is demonstrated by the measurement model; firstly, because the factor loadings are significant and greater than 0.5 and secondly because the average variance extracted [AVE] for each of the factors is greater than 0.5 (Fornell & Larcker, 1981). Scale reliability is verified since the values of composite reliability indices for all the factors are larger than 0.6 (Hair et al., 2010).

Discriminant validity is assessed by comparing the square root of AVE with the correlations between factors. The square root of AVE should be greater than the correlations between factors (Fornell & Larcker, 1981). So our model satisfied this criteria of discriminant validity as well as the AVE. All constructs shared more variance with their respective indicators than with other constructs of the model.

4.4 Structural model testing

In the next step, we tested our structural model. The structural model also revealed good fit indices i.e. CFI= 0.931, CMIN/DF=2.996, RMSEA= 0.057 and SRMR= 0.0426. The results revealed that there is a significant positive impact of utilitarian motivation on consumers' attitudes towards social media marketing [[beta]=0.440, p<0.001], online purchase intentions [[beta]=0.343, p<0.001] which lead us to accept H1a and H1b. Hedonic motivation has a significant influence on consumers' attitudes towards social media marketing [[beta]=-0.400, p<0.001] and an insignificant influence on the online purchase intentions [[beta]=0.035, p=0.408] which lead us to accept H2a and reject H2b. Content personalization motivation has a significant influence on consumers' attitudes towards social media marketing [[beta]= 0.140, p<0.001] and an insignificant influence on the online purchase intentions [[beta]=0.044, p=0.408] which lead us to accept H3a and reject H3b. Attitude towards social media marketing has a significant positive influence on the online purchase intentions of consumers [[beta]=0.4435, p<0.001] which leads us to accept H4.

In order to check the mediating impact of consumers' attitude towards social media marketing, we used bootstrapping technique in AMOS by using the bootstrap sample of 2000 using biased corrected confidence interval of 95. As shown in table 4 attitude towards social media marketing mediates the relationship between all motives and purchase intentions. Attitude towards social media marketing partially mediates the relationship between utilitarian motivation and online purchase intentions and fully mediates the relationship between hedonic motivation and online purchase intentions and content personalization motivation and online purchase intentions. Therefore, H5a, H5b and H5c were all accepted.

5. Discussion

The results of the study showed that utilitarian motivation has a strong impact on consumers' attitude towards social media marketing as well as the online purchase intentions. This finding suggests that social media advertisements and brand pages having good informative content play an important role for fashion retailers as these positively influence consumers' attitudes towards social media marketing as well as online purchase intentions. This finding is supported by previous studies on e-commerce websites and purchases of in-app mobile applications (Gao & Koufaris,2006; Hsu & Lin ,2016).

The results of the study revealed that hedonic motivation has a strong impact on consumers' attitude towards social media marketing which is consistent with previous studies on advertising in general (Kotler & Armstrong, 2014). The insignificant impact of hedonic motivation on the online purchase intentions is supported by previous studies (Anderson, Knight, Pookulangara, & Josiam, 2014). The results also showed that perceived personalization has a positive impact on consumers' attitude towards social media marketing which is consistent with previous studies in terms of advertising in general (Mulhern, 2009). However, perceived personalization does not impact purchase intentions of the consumers which is inconsistent with previous studies on web advertising (Li, 2016). The results also proved that attitude towards social media marketing has a positive impact on the online purchase intentions of consumers which is also in congruence with previous studies in the context of online shopping (Ashraf et al., 2014).

The results of mediation analysis revealed that attitude towards social media marketing plays an important role of as a mediating variable in affecting the relationship between all of the above mentioned three motives and online purchase intentions. This is consistent with previous studies on organic food consumption (Teng & Wang, 2015).

6. Implications

The objective of the present study was to evaluate factors that have an effect on consumers' attitude towards social media marketing and online purchase intentions. Social media has an important role in explaining the technological revolution around the world. The technological advancements brought by social media have removed the geographical and time constraints and people can communicate with each other at any time of the day. Marketers have also started to promote their products through social media marketing due to the high reach of social media but a key challenge faced by the marketers is determining consumers' attitudinal and behavioral responses towards social media marketing. Thus, this study is helpful in evaluating the different factors that affect consumers' attitudinal and behavioral outcomes with respect to social media marketing in the fashion industry. Theoretically, the study has filled the existing gaps in literature by establishing a research model based on sound theory i.e. Uses and Gratifications Theory which means that this theory can be used as theoretical framework to study consumer behavior in the context of social media marketing focusing on the fashion industry. Secondly, the study established the direct link of consumers' motivations with online purchase intentions. The study has not just focused on utilitarian and hedonic motivations but it has also added another motivations i.e. content personalization motive and established the linkage between content personalization motivations and online purchase intentions. Besides this, it also tested the mediating role of consumers' attitudes towards social media marketing and online purchase intentions.

The results of the study show that our research model is generally plausible to explain the role of consumers' motivations with respect to consumers' attitudes towards social media marketing and online purchase intentions. As attitude is the main driver of acceptance of social media marketing, this research makes a number of academic and managerial contributions to improve consumers' attitude toward social media marketing. Findings suggest social media marketing that encompasses elements of utilitarianism, hedonism and personalization lead towards favorable consumers' attitude towards social media marketing. Among the three independent variables, utilitarian motivation has the strongest positive impact on consumers' attitudes towards social media marketing and online purchase intentions. Therefore, marketers need to provide timely and relevant information to consumers in order to gratify the information seeking motive of consumers. The updated and detailed information would help the marketers in attracting the large chunk of consumers towards social media brand pages and advertisements. Marketers also need to take into consideration the hedonic needs of consumers as hedonic motivations also plays an important role in influencing consumers' attitude towards social media marketing. This can be done by providing entertaining content to the consumers by giving visually appealing pictures of products. Appearance related features should be given due attention by using high quality graphics, audio and visual elements, fonts and background music etc.

An interesting finding of the current study suggests that perceived personalization has a positive impact on consumers' attitude towards social media marketing. This implies that marketers should focus on behavioral targeting of consumers i.e. targeting them by keeping in view their tastes and preferences. This would not just save consumers' time and effort in finding the desired products but would also help the companies in targeting the desired consumer segment.

Finally, since consumers' attitude towards social media marketing acts as an important variable in affecting the relationship between motivations and online purchase intentions, therefore, marketers should always try to improve their efforts in positively influencing consumers' attitudes towards social media marketing.

7. Limitations and Future Research Directions

Our study has few limitations that might be addressed in near future. The study is based on cross-sectional design which is unable to capture changes in consumers' attitude with the passage of time. Therefore, future studies can be carried out using longitudinal design to add more rigor to the results. Secondly, the study focuses only on the fashion industry, whereas consumers' behavior might be different in the context of other industries. Thus, marketers can carry out future studies in different industries like electronics, airlines and tourism etc. The current study has incorporated only three motivations in the model. Future studies can extend the list of other motives. Future researchers can also carry out qualitative studies and gain in-depth knowledge about the underlying needs and motives of consumers with respect to social media marketing.

References

Akar, E., & Topcu, B. (2011). An examination of the factors influencing consumers' attitudes toward social media marketing. Journal of Internet Commerce, 10(1), 35-67.

Alam, S. S., & Mohamed Sayuti, N. (2011). Applying the Theory of Planned Behavior (TPB) in halal food purchasing. International Journal of Commerce and Management, 21(1), 8-20.

Alnawas, I., & Aburub, F. (2016). The effect of benefits generated from interacting with branded mobile apps on consumer satisfaction and purchase intentions. Journal of Retailing and Consumer Services, 31(1), 313-322.

American Marketing Association. (2016). Dictionary. Retrieved from https://www.ama.org/resources/Pages/Dictionary.aspx?dLetter=A#attitude

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 10 3(3), 411-423.

Anderson, K. C., Knight, D. K., Pookulangara, S., & Josiam, B. (2014). Influence of hedonic and utilitarian motivations on retailer loyalty and purchase intention: A Facebook perspective. Journal of Retailing and Consumer Services, 21(5), 773-779.

Ariffin, S. K, Mohan, T., & Goh, Y. N. (2018). Influence of consumers' perceived risk on consumers' online purchase intention. Journal of Research in Interactive Marketing, 12(3), 309-327.

Ashraf, A. R., Thongpapanl, N. (Tek), & Auh, S. (2014). The application of the technology acceptance model under different cultural contexts: The case of online shopping adoption. Journal of International Marketing, 22(3), 68-93.

Baek, T. H., & Morimoto, M. (2012). Stay away from me. Journal of Advertising, 41(1), 59-76.

Bebber, S., Milan, G. S., De Toni, D., Eberle, L., & Slongo, L. A. (2017). Antecedents of purchase intention in the online context. Journal of Relationship Marketing, 16(1), 82-98.

Biswas, A., & Roy, M. (2014). Impact of social medium on green choice behavior. Journal of Marketing Management, 2(2), 95-111.

Bleier, A., & Eisenbeiss, M. (2015). The importance of trust for personalized online advertising. Journal of Retailing, 91(3), 390-409.

Brandwatch. (2018). 121 amazing social media statistics and facts. Retrieved from https://www.brand-watch.com/blog/amazing-social-media-statistics-and-facts/.

Cheng, J. M. S., Blankson, C., Wang, E. S. T., & Chen, L. S. L. (2009). Consumer attitudes and interactive digital advertising. International Journal of Advertising, 28(3), 501-525.

Chi, H. (2011). Interactive digital advertising vs. virtual brand community: Exploratory study of user motivation and social media marketing responses in taiwan. Journal of Interactive Advertising, 12(1), 44-61.

Chiang, H.-S. (2013). Continuous usage of social networking sites: The effect of innovation and gratification attributes. Online Information Review, 37(6), 851-871.

Chiang, H.-S., & Hsiao, K.-L. (2015). YouTube stickiness: The needs, personal, and environmental perspective. Internet Research, 25(1), 85-106.

Chiu, C. M., Wang, E. T. G., Fang, Y. H., & Huang, H. Y. (2014). Understanding customers' repeat purchase intentions in B2C e-commerce: The roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal, 24(1), 85-114.

Duffett, R. (2015). The influence of Facebook advertising on cognitive attitudes amid Generation Y. Electronic Commerce Research, 15(2), 243-267.

Duffett, R. G. (2015). Facebook advertising's influence on intention-to-purchase and purchase amongst Millennials. Internet Research, 25(4), 498-526.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurements error. Journal of Marketing Research, 18(1), 39-50.

Forrester. (2015). Benchmarking social marketing efforts in Asia Pacific In 2015. Retrieved from https://www.forrester.com/report/Benchmarking+Social+Marketing+Efforts+In+Asia+Pacific+In+2015/-/E-RES120917#figure2

Fuller, J. (2006). Why consumers engage in virtual new product developments initiated by producers. Advances in Consumer Research, 33(1), 639-646.

Gao, Y., & Koufaris, M. (2006). Perceptual antecedents of user attitude in electronic commerce. The Data Base For Advances in Information Systems, 37(2), 42-50.

George, D., & Mallery, M. (2010). SPSS for windows step by step: A simple guide and reference, 17.0 update (10th ed.). Boston: Pearson.

Godey, B., Manthiou, A., Pederzoli, D., Rokka, J., Aiello, G., Donvito, R., & Singh, R. (2016). Social media marketing efforts of luxury brands: Influence on brand equity and consumer behavior. Journal of Business Research, 69(12), 5833-5841.

Grewal, R., Cote, J. A., & Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing Science, 23(4), 519-529.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.

Hamouda, M. (2018). Understanding social media advertising effect on consumers' responses: an empirical investigation of tourism advertising on Facebook. Journal of Enterprise Information Management, 31(3), 426-445.

Han, H., Xu, H., & Chen, H. (2018). Social commerce: A systematic review and data synthesis. Electronic Commerce Research and Applications, 30(1), 38-50.

Heravi, A., Mubarak, S., & Choo, K.-K. R. (2018). Information privacy in online social networks: Uses and gratification perspective. Computers in Human Behavior, 84(1), 441-459.

Hew, J.-J., Lee, V.-H., Ooi, K.-B., & Lin, B. (2016). Mobile social commerce: The booster for brand loyalty? Computers in Human Behavior, 59(1), 142-154.

Hossain, M. A., Islam, S., & Himel, S. H. (2014). Customers' attitude determinants and positioning of different boutique houses: A study on some selected boutique houses in Dhaka city of Bangladesh. European Journal of Business and Management, 6(29), 28-35.

Hsu, C.-L., & Lin, J. C.-C. (2016). Effect of perceived value and social influences on mobile app stickiness and in-app purchase intention. Technological Forecasting and Social Change, 108(1), 42-53.

Irshad, M. (2018). Impact of consumers' motivations and trust on attitudes towards social media marketing and purchase intentions. 2018 Summer AMA Conference Proceeedings (p. 523). Boston, USA.

Jamali, S. K., Samadi, B., & Marthandan, G. (2018). Prioritizing electronic commerce technologies in Iranian family SMEs. Interdisciplinary Journal of Contemporary Research in Business, 6(2), 148-180.

Jara, A. J., Parra, M. C., & Skarmeta, A. F. (2014). Participative marketing: Extending social media marketing through the identification and interaction capabilities from the Internet of things. Personal and Ubiquitous Computing, 18(4), 997-1011.

Johnson, J. P. (2013). Targeted advertising and advertising avoidance. RAND Journal of Economics, 44(1), 128-144.

Kakar, A. kumar S. (2017). Why do users prefer the hedonic but choose the utilitarian? Investigating user dilemma of hedonic--utilitarian choice. International Journal of Human Computer Studies, 10 8(1), 50-61.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59-68.

Kemp. (2018). Digital in 2018. Retrieved from https://wearesocial.com/uk/blog/2018/01/global-digital-report-2018

Kim, Y., Sohn, D., & Choi, S. M. (2011). Cultural difference in motivations for using social network sites: A comparative study of American and Korean college students. Computers in Human Behavior, 27(1), 365-372.

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York, NY: The Guilford Press.

Knoll, J. (2015). Advertising in social media: a review of empirical evidence. International Journal of Advertising, 35(2), 266-300.

Kohli, C., Suri, R., & Kapoor, A. (2015). Will social media kill branding? Business Horizons, 58(1), 35-44.

Koster, M., Ruth, M., Hamborg, K.-C., & Kasper, K. (2015). Effects of personalized banner ads on visual attention and recognition memory. Applied Cognitive Psychology, 29(2), 181-192.

Kotler, P. T., & Armstrong, G. (2014). Principles of Marketing (15th ed.). Upper Saddle River, NJ: Pearson.

Krejcie, R. V, & Morgan, D. W. (1970). Determining sample size for research activities. Education and Psychological Measurement, 30(3), 607-610.

Kumar, A., & Kashyap, A. K. (2018). Leveraging utilitarian perspective of online shopping to motivate online shoppers. International Journal of Retail and Distribution Management, 46(3), 247-263.

Lee, E.-B., Lee, S.-G., & Yang, C.-G. (2017). The influences of advertisement attitude and brand attitude on purchase intention of smartphone advertising. Industrial Management & Data Systems, 117(6), 1011-1036.

Li, C. (2016). When does web-based personalization really work ? The distinction between actual personalization and perceived personalization. Computers in Human Behavior, 54(1), 25-33.

Li, H., Edwards, S. M., & Lee, J.-H. (2002). Measuring the intrusiveness of advertisements: Scale development and validation. Journal of Advertising, 31(2), 37-47.

Liang, T.-P., Ho, Y.-T., Li, Y.-W., & Turban, E. (2011). What drives social commerce: The role of social support and relationship quality. International Journal of Electronic Commerce, 16(2), 69-90.

Lien, C. H., & Cao, Y. (2014). Examining WeChat users' motivations, trust, attitudes, and positive word-of-mouth: Evidence from China. Computers in Human Behavior, 41(1), 104-111.

Liu, F., Li, J., Mizerski, D., & Soh, H. (2012). Self congruity, brand attitude, and brand loyalty: A study on luxury brands. European Journal of Marketing, 46(7/8), 922-937.

Liu, J., North, M., & Li, C. (2017). Relationship building through reputation and tribalism on company Facebook pages: A uses and gratifications approach. Internet Research, 27(5), 1149-1169.

Luna-Nevarez, C., & Torres, I. M. (2015). Consumer attitudes toward social network advertising. Journal of Current Issues & Research in Advertising, 36(1), 1-19.

Luo, X. (2010). Uses and gratifications theory and e-consumer behaviors. Journal of Interactive Advertising, 2(2), 34-41.

Lwin, M., & Phau, I. (2013). Effective advertising appeals for websites of small boutique hotels. Journal of Research in Interactive Marketing, 7(1), 18-32.

Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business Horizons, 52(4), 357-365.

Martins, J., Costa, C., Oliveira, T., Goncalves, R., & Branco, F. (2019). How smartphone advertising influences consumers' purchase intention. Journal of Business Research, 94(1), 378-387.

Mikalef, P., Giannakos, M., & Pateli, A. (2013). Shopping and word-of-mouth intentions on social media. Journal of Theoretical and Applied Electronic Commerce Research, 8(1), 17-34.

Muk, A., & Chung, C. (2014). Driving consumers to become fans of brand pages: A theoretical framework. Journal of Interactive Advertising, 14(1), 1-10.

Muk, A., Chung, C., & Kim, J. (2014). Korean consumer perspectives on social media advertising and intention to join brand pages. Journal of Global Scholars of Marketing Science, 24(4), 384-394.

Mulhern, F. (2009). Integrated marketing communications: From media channels to digital connectivity. Journal of Marketing Communications, 15(2-3), 85-101.

Muntinga, D. G., Moorman, M., & Smit, E. G. (2011). Introducting COBRAs: Exploring motivations for brand-related social media use. International Journal of Advertising, 30(1), 13-46.

Muralidharan, S., & Men, L. R. (2015). How peer communication and engagement motivations influence social media shopping behavior: Evidence from China and the United States. Cyberpsychology, Behavior, and Social Networking, 18(10), 595-601.

Nwagwu, W. E., & Famiyesin, B. (2016). Acceptance of mobile advertising by consumers in public service institutions in Lagos, Nigeria. Electronic Library, 34(2), 265-288.

Osei-Frimpong, K. (2019). Understanding consumer motivations in online social brand engagement participation: Implications for retailers. International Journal of Retail and Distribution Management, 47(5), 511-529.

Parker, C. J., & Wenyu, L. (2019). What influences Chinese fashion retail? Shopping motivations, demographics and spending. Journal of Fashion Marketing and Management, 23(2), 158-175.

PAS. (2015). Digital, social & mobile in APAC in 2015. Retrieved from http://www.pas.org.pk/digital-social-mobile-in-apac-in-2015/

PAS. (2017). Digital in 2017: Global overview. Retrieved June 11, 2018, from http://pas.org.pk/digi-tal-in-2017-global-overview/

PBS. (2013). Pakistan social and living standards measurement. Retrieved from http://www.pbs.gov.pk/content/pakistan-social-and-living-standards-measurement-survey-pslm-2012-13-provincial-district

Plume, C. J., & Slade, E. L. (2018). Sharing of sponsored advertisements on social media : A uses and gratifications perspective. Information Systems Frontiers, 20(3), 471-483.

Roberts, D., Hughes, M., & Kertbo, K. (2014). Exploring consumers' motivations to engage in innovation through co-creation activities. European Journal of Marketing, 48(1), 147-169.

SBP. (2018). Online payment platforms in Pakistan--A case of B2C e commerce. Retrieved from http://www.sbp.org.pk/reports/quarterly/fy18/First/Special-Section-2.pdf

Statista. (2017). Daily time spent on social networking by internet users worldwide from 2012 to 2017 (in minutes). Retrieved from https://www.statista.com/statistics/433871/daily-social-media-usage-worldwide/

Swani, K., Brown, B. P., & Milne, G. R. (2014). Should tweets differ for B2B and B2C? An analysis of Fortune 500 companies' Twitter communications. Industrial Marketing Management, 43(5), 873-881.

Taylor, D. G., Lewin, J. E., & Strutton, D. (2011). Friends , fans , and followers : Do ads work on social Nnetworks ? How gender and age shape receptivity. Journal of Advertising Research, 51(1), 258-276.

Teng, C. C., & Wang, Y. M. (2015). Decisional factors driving organic food consumption: Generation of consumer purchase intentions. British Food Journal, 117(3), 1066-1081.

Vong, K. (2015). Image is everything: Why people are hooked on image-based social media. Retrieved from http://www.trendreports.com/article/imagebased-social-media

Windels, K., Heo, J., Jeong, Y., Porter, L., Jung, A. R., & Wang, R. (2018). My friend likes this brand: Do ads with social context attract more attention on social networking sites? Computers in Human Behavior, 84(1), 420-429.

Zhang, J., & Mao, E. (2016). From online motivations to ad clicks and to behavioral intentions: An empirical study of consumer response to social media advertising. Psychology & Marketing, 33(3), 155-164.

Zhu, Y., & Chen, H. (2015). Social media and human need satisfaction : Implications for social media marketing. Business Horizons, 58(3), 335-345.

Madeeha Irshad (1), Mohamad Shakil Ahmad (2)

(1) PhD Scholar, Department of Management Sciences, COMSATS University Islamabad, Islamabad Campus, Pakistan. Email: madeeha_scorpion@hotmail.com

(2) Assistant Professor, Department of Management Sciences, COMSATS University Islamabad, Pakistan

ARTICLE HISTORY

05 Nov, 2018 Submission Received

25 Feb, 2019 Second Review

09 Aug, 2019 Accepted

30 Nov, 2018 First Review

16 July, 2019 Third Review

DOI: dx.doi.org/10.22547/BER/11.3.4
Table 1: Standardized Factor Loadings, Reliability and Convergent
Validity


Variable                 Item Code  Standardized  CR     AVE
                                    Factor
                                    Loading

Utilitarian Motivation   U1         0.751          0.837  0.507
                         U2         0.741
                         U3         0.686
                         U4         0.723
                         U5         0.656
Hedonic Motivation       HM1        0.817          0.921  0.663
                         HM2        0.791
                         HM3        0.814
                         HM4        0.849
                         HM5        0.899
                         HM6        0.699
Content Personaliza-     CPM1       0.767          0.845  0.645
tion Motivation          CPM2       0.809
                         CPM3       0.833
Attitude towards social  ATT1       0.652          0.884  0.523
media marketing          ATT2       0.725
                         ATT3       0.793
                         ATT4       0.747
                         ATT5       0.702
                         ATT6       0.699
                         ATT7       0.735
Online Purchase Inten-   PI1        0.656          0.920  0.589
tions                    PI2        0.752
                         PI3        0.842
                         PI4        0.820
                         PI5        0.769
                         PI6        0.782
                         PI7        0.784
                         PI8        0.721

Table 2: Discriminant Validity

   1                        1            2            3

1  Utilitarian Motivation   0.712
2  Hedonic Motivation       0.365 (***)  0.814
3  Attitude towards social  0.633 (***)  0.582 (***)  0.723
   media marketing
4  Content Personaliza-     0.326 (***)  0.145 (**)   0.340 (***)
   tion Motivation
5  Online Purchase Inten-   0.650 (***)  0.424 (***)  0.695 (***)
   tions

   1                        4            5

1  Utilitarian Motivation
2  Hedonic Motivation
3  Attitude towards social
   media marketing
4  Content Personaliza-     0.803
   tion Motivation
5  Online Purchase Inten-   0.312 (***)  0.768
   tions

Note: (***) p < 0.001

Table 3: Hypotheses Testing

Hypotheses  Hypothesized   [beta]  p      Decision
            Relationships

H1a         UM-ATTSMM      0.440   (***)  Accepted
H1b         HM-ATTSMM      0.400   (***)  Accepted
H1c         CPM-ATTSMM     0.140   (***)  Accepted
H2a         UM-PI          0.343   (***)  Accepted
H2b         HM-PI          0.035   0.408  Rejected
H3b         CPM-PI         0.044   0.243  Rejected
H4          ATTSMM-PI      0.443   (***)  Accepted

Note: UM=Utilitarian motivation, ATTSMM=Attitude toward social media
marketing, HM=Hedonic motivation, CPM=Content personalization
motivation, PI=Purchase intentions, (***) p <0.001

Table 4: Mediation Analysis

Hypothesis   Dependent  Inde-     Mediator  Standardized  Standardized
             Variable   pendent             total         Direct
                        Variable            Effect        Effect

H5a p value  PI         UM        ATTSMM     0.538         0.343
                                            (0.001)       (0.001)
H5b p value  PI         HM        ATTSMM     0.212         0.035
                                            (0.001)       (0.474)
H5c p value  PI         CPM       ATTSMM     0.106         0.044
                                            (0.025)       (0.316)

Hypothesis   Standardized
             Indirect
             Effect

H5a p value   0.195
             (0.001)
H5b p value   0.177
             (0.001)
H5c p value   0.062
             (0.007)

Note: UM=Utilitarian motivation, ATTSMM=Attitude toward social media
marketing, HM=Hedonic motivation, CPM=Content personalization
motivation, PI=Purchase intentions, (***) p <0.001
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Author:Irshad, Madeeha; Ahmad, Mohamad Shakil
Publication:Business & Economic Review
Geographic Code:9PAKI
Date:Sep 1, 2019
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