Keywords

1 Introduction

India witnesses a significant development in retailing with e-tailing (ET Retail 2017). Increased internet diffusion, smart phone usage, ease of buying & payments, choice of products & services are steering growth in e-tailing. Investments in e-commerce companies are growing up. Also, the government initiatives to promote Digital India and inter-operability systems are facilitating the e-tailing. These scenarios have provided ample opportunities for e-tailers to establish themselves.

With increased internet penetration and popularity of social networking site, e-tailers are bringing out technology steered practices such as Social Commerce.

It originated with Amazon introducing Purchase Circles (Amazon 1999). The term social commerce was introduced by Yahoo as Picklists and Shoposphere (Beach (2005) and Rubel (2006)). Yadav et al. (2013) defined social commerce as “exchange-related activities that occur in, or are influenced by, an individual’s social network in computer-mediated social environments, where the activities correspond to the need recognition, pre-purchase, purchase and post-purchase changes of a focal exchange.” It simple terms it is e-commerce happening in social networking sites.

Web 2.0 has brought new platforms Social commerce constructs such as Recommendations & referrals, Forums & communities, Ratings & reviews in social networking sites. Though technically slightly different, these are primarily content generators to facilitate interactions and promote word of mouth. The economic implication of these constructs is product sales (Forman et al. 2008a, b) in e-tailing which is the ultimate destination for every marketer. Trust is also a very noteworthy factor in e-commerce (Gefen and Straub 2004; Mutz 2005; Pavlou 2003) and also place a very significant role to facilitate social commerce (Hajli 2015). The economic implication of these constructs is product sales (Forman et al. 2008a, b) in e-tailing which is the ultimate destination for every marketer. Understanding the Trust & social commerce construct on consumer decision making is important for marketer to use appropriately and devise strategies accordingly. In this context, the study is carried out to research the impact of trust and social commerce construct across stages of consumer decision making.

2 Literature Review

Aljifri et al. (2003) identified Trust as the key barrier for adopting e-commerce. Senecal and Nantel (2004) studied on consumer choice based on product recommendation. Personal recommendation systems are highly significant among all the online recommendations. Hassanein and Head (2007) researched on impact of online consumer behaviour by individuals representing online environment. Trust has significant positive relationship with perceived social presence and impacts attitude. Park et al. (2007) identified online reviews are informants and recommenders for purchase decision making. Qualitative online reviews build consumers purchase intention. Also the purchase intention increases with increase in online reviews. DEI Worldwide (2008) reported 70% of consumers use social media among other online sources to seek information about a company and it influenced 67% consumers purchase decision. Swamynathan et al. (2008) studied the impact of social networks on e-commerce. Social networks have significant impact on e-commerce and satisfaction level of social network users was high. Lu et al. (2010) studied trust in social networks is high than C2C website. Also, the study revealed consumer intention to get information influences the purchase intention of the consumer. Personal recommendations & Online consumer opinions are the most trusted forms of advertisement among Internet Consumers according to “The Neilsen Global Online Consumer Survey”, Nielsen(2009). Hensel and Deis (2010) investigated social media to improve marketing & advertising. Social media facilitates conversations among consumers and also build brand value. Hsiao et al. (2010) studied building trust through product recommendations and relationship between trust and purchase intention for shopping online. The study revealed trust built from product recommendation is comparatively higher than trust built from the product website. Moreover, trust built through product recommendation has a direct impact on purchase intention and indirect impact on intention to buy the product from the website. Armelini (2011) study revealed direct correlation of sales and number of conversations in social media. Curty and Zhang (2011) traced the evolution of social commerce before Yahoo in 2005 with Amazon & Epinions in August 1999. Amazon used “purchased circles” similar to recommendations & consumer communities. Consumers & visitors of the website are provided with the facility for wishlist and email their friends about products. Epinions provided ratings & reviews, member forums - internal social network and referred to it as “Community of trust.” The study focus was on the technology perspective of social commerce. The findings of the study identified two categories of social commerce websites - Direct sales & Referrals.

Anderson et al. (2011) researched social media to be used as a commerce channel. The real- time data collected when customers search, purchase, give ratings, recommend and purchase products aids companies to build strategies influencing consumer behavior. Fijalkowski and Zatoka (2011) proposed a recommender system for e-commerce based on user profiles on Facebook. Rad and Benyoucef (2011) developed a model of social commerce with reference to consumer decision-making process. The model was built on social commerce components (Social shopping, Ratings & Reviews, Recommendations & Referrals, Forums & Communities, Social Media, Social Advertising) and included business. The study related the following social components across stages of consumer decision- making process: 1) Need Recognition Recommender systems. 2) Product brokerage - Trusted reviews. 3) Merchant brokerage - Synchronous shopping, 4) Purchase decision - Recommender system - Product bundling & Group purchase. 5) Purchase - Social Media to post status (Individual purchase/Group purchase) 6) Evaluation- Ratings & reviews. Hoffman (2013) studied social media and consumer behavior. The users review the user-generated content when they want to make a purchase intention or merely to spend time. The content generated by users directly influences the purchase intention. If the users are consuming user-generated information to spend time, it will influence users consequent attitudes and behavior. User-generated information online has several unique features such as popularity, longer carryover effects on consumer behavior especially with users of same group. Wang et al. (2012) analyzed the impact of peer communication on purchase intention. The peer communication about products has a direct (conformity with peers) and indirect (reinforcing product involvement) in purchase decision making. Based on past reviews and social support theory Haji (2013) developed a model for social commerce. The study identified social commerce conceptual elements with social commerce constructs (SCCs) - Ratings & Reviews, Forums & Communities, and Referrals & Recommendations. The social commerce constructs (SCCs) enhances trusts and results in purchase intention. Yadav et al. (2013) carried out a study to leverage social media and sell products. The authors stated the proposition & facilitating the role of social networks for the 4 different stages of decision making viz., Need Recognition, Pre-purchase activities, Purchase decision and post-purchase activities. Maity and Dass (2014) studied the impact of consumer decision making across modern and traditional channels. The findings revealed consumer prefer e-commerce channels for searching product information. Hajli (2014a) studied the impact of social media on consumers. Based on the technology acceptance model (TAM) a social commerce adoption model was developed. The study concludes consumer interactions through online forums, communities, ratings, reviews, and recommendations in social media have given rise to social commerce. Consumers are empowered as content generators which aid in sharing information & experiences in their networks and facilitate social interactions. Consequently, it builds trust and hence users intention to buy. Hajli et al. (2014d) build a trust model for new products & services in the context of social commerce. The study identified the social commerce constructs - Ratings & Reviews, Recommendations & Referrals, Forums & communities and the impact of trust in new products and services. Hajli (2015) built a model for social commerce consumer behaviour with social commerce constructs (Ratings & reviews, Recommendations & referrals, Forums & communities), Trust and Intention to buy. The social commerce constructs (SCCSs) has a direct impact on intention to buy and indirectly on intention to buy through Trust.

3 Conceptual Framework

Based on the above literature Trust and the social commerce constructs have significantly influenced the consumer. The study aims to establish the influence of Trust and social commerce constructs for fashion e-tailing. It also bring out the extent of influence by the social commerce constructs & Trust for fashion e-tailing across stages of consumer decision making viz., Need Recognition, Pre-Purchase, Purchase Decision and Post Purchase. The conceptual framework developed is represented below:

Fig. 1.
figure 1

Relationship of trust & social commerce constructs (SCCs) on consumer decision making stages

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4 Research Methodology

The study focussed to identify the impact of Trust & Social commerce construct (SCCs) across four stages of consumer decision making viz., Need Recognition, Pre-purchase, Purchase decision and Post-Purchase for fashion e-tailing. The following objective and the related hypothesis are laid for the study as follows:

Objective 1:

To study the impact of Trust on stages of consumer decision making for shopping fashion products in social networking sites.

  • Hypothesis 1 (H1): There is a significant relationship between trust across stages of consumer decision making for fashion e-tailing.

Objective 2:

To study the impact of Social commerce constructs (Recommendations & Referrals, Forums & Communities, Ratings & Reviews) on stages of consumer decision making for shopping fashion products in social networking sites.

  • Hypothesis 2 (H2): There is a significant relationship between social commerce constructs (Recommendations & referrals, Forums & Communities, Ratings& Reviews) across stages of consumer decision making for fashion e-tailing

The data was collected through structured questionnaire online & offline. The respondents were active social networking site user who purchased fashion products recently (Less than 6 months) based in Chennai. A pilot study was conducted among 42 respondents in Chennai. The reliability test score with Cronbach alpha was 0.81 i.e., 81% reliability. The data collection was carried out during the period Jan 2017 to June 2017.

Convenience sampling, a type of non-probability sampling was used for the study. 600 questionnaires were circulated for data collection. With 3.1% questionnaire rejected, 581 questionnaires were finally used for the study.

The questionnaire consists of statements related to social commerce constructs (Recommendations & referrals, Forums & communities, Ratings & reviews) and Trust. This was measured using the instrument developed initially by Nick Hajli (2015). It consists of 10 items relating to each social commerce construct and trust measured in a five-point Likert type scale. Equal importance was given to all the statements, and the opinion about every social commerce construct and trust was obtained for fashion e-tailing. Also, the questionnire with other set of statements related to the facilitative role of social commerce across stages of consumer decision making was included. This was measured using the instrument developed initially by M.S. Yadav et al. (2013). It comprises of 11 items relating to four stages of consumer decision making namely Need recognition, Pre-purchase, Purchase decision and Post purchase. These are measured in a five-point Likert type scale. All the items were given the same importance, and the respondent’s opinion about the facilitative role of social commerce for fashion e-tailing was obtained.

5 Results and Discussion

Regression analysis used to investigate the extent of trust impact and social commerce constructs impact (Recommendations & Referrals, Forums & Communities, Ratings & Reviews) on various stages of consumer decision making (Need Recognition, Pre-purchase, Purchase decision, Post- purchase).

From Table 1, based on the R square value for the dependent variable trust, recommendations & referrals, forums & communities, ratings & reviews establishes 4.8%, 10.1%, 4.8%, 10.9% variance respectively on the stages of consumer decision making. Also, statistically significant at 1% level i.e., Trust, recommendations & referrals, forums & communities, ratings & reviews is well related with various stages of consumer decision making. This leads to the determination of trust & social commerce constructs impact on each stage of consumer decision making - Need Recognition, Pre-purchase, Purchase decision, Post purchase.

Table 1. Mode Summary for Impact of Trust & Social commerce construct on stages of consumer decision making
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From Table 2, the multiple regression equation is

$$ Y\, = \,3.389\, - \,0.261X_{1} \, + \,0.054X_{2} \, - \,0.016X_{3} \, + \,0.196X_{4} $$
Table 2. Coefficient table for impact of trust on stages of consumer decision making
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Here the coefficient of X1 is −0.261 represents the partial effect with Need Recognition on Trust, holding other stages of consumer decision making constant. The estimated negative sign implies that such effect is negative and Trust score would decrease by 0.261 for every unit increase in need recognition. Also, the coefficient value is significant at the 1% level.

The coefficient of X2 is 0.054 represents the partial effect of Pre-purchase on Trust, holding other stages of consumer decision making constant. The estimated positive sign implies that such effect is positive and Trust score would increase by 0.054 for every unit increase in pre-purchase. Also, the coefficient value is not significant at the 5% level.

The coefficient of X3 −0.016 represents the partial effect with purchase decision on Trust, holding other stages of consumer decision making constant. The estimated negative sign implies that such effect is adverse and Trust score would decrease by 0.016 for every unit increase in a purchase decision. Also, the coefficient value is not significant at the 5% level.

The coefficient of X4 is 0.196 represents the partial effect of Post purchase on Trust, holding other stages of consumer decision making constant. The estimated positive sign implies that such effect is positive and Trust score would increase by 0.196 for every unit increase in a purchase decision. Also, the coefficient value is significant at the 1% level.

Hence, it is inferred that there is a considerable impact on the Trust in Need Recognition and Post-purchase stage. The impact of Trust in Need recognition stage is high and exhibits inverse relationship compared with Post-purchase stage exhibiting positive relationship

From Table 3, the multiple regression equation is

$$ Y\, = \,4.200\, - \,0.296X_{1} \, + \,0.238X_{2} \, + \,0.021X_{3} \, - \,0.313X_{4} $$
Table 3. Coefficient table for Impact of Recommendations & Referrals on stages of consumer decision making
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Here the coefficient of X1 is −0.296 represents the partial effect with Need Recognition on Recommendations & Referrals, holding other stages of consumer decision making constant. The estimated negative sign implies that such effect is adverse and Recommendations & Referrals score would decrease by 0.296 for every unit increase in need recognition. Also, the coefficient value is significant at the 1% level.

The coefficient of X2 is 0.238 represents the partial effect of Pre-purchase on Recommendations & Referrals, holding other stages of consumer decision making constant. The estimated positive sign implies that such effect is positive and Recommendations & Referrals score would increase by 0.238 for every unit increase in pre-purchase. Also, the coefficient value is significant at the 1% level.

The coefficient of X3 0.021 represents the partial effect with purchase decision on Recommendations & Referrals, holding other stages of consumer decision making constant. The estimated positive sign implies that such effect is positive and Recommendations & Referrals score would increase by 0.021 for every unit increase in a purchase decision. However, the coefficient value is not significant at the 5% level.

The coefficient of X4 is −0.313 represents the partial effect of Post purchase on Recommendations & Referrals, holding other stages of consumer decision making constant. The estimated negative sign implies that such effect is adverse and Recommendations & Referrals score would decrease by 0.313 for every unit increase in a purchase decision. Also, the coefficient value is significant at the 1% level.

Hence, it can be inferred that there is a significant impact of the Recommendations and referrals specficially with the Need recognition; Pre and Postpurchase stages of consumer decision making. The impact of Recommendations & referrals is high with Post purchase followed by Need Recognition and Prepurchase. The impact is negative with Post Purchase & Need Recognition and confidence with the Pre-purchase stage of consumer decision making.

Table 4. Coefficient table for Impact of Forums & Communities on Stages of consumer decision making
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The multiple regression equation is

$$ Y\, = \,2.983\, + \,0.027X_{1} \, + \,0.354X_{2} \, - \,0.449X_{3} \, + \,0.100X_{4} $$

Here the coefficient of X1 is 0.027 represents the partial effect with Need Recognition on Forums & Communities, holding other stages of consumer decision making constant. The estimated positive sign implies that such effect is positive and Forums & Communities score would increase by 0.027 for every unit increase in Need recognition. However, the coefficient value is not significant at the 5% level.

The coefficient of X2 is 0.354 represents the partial effect with Pre-purchase on Forums & Communities, holding other stages of consumer decision making constant. The estimated positive sign implies that such effect is positive and Forums & Communities score would increase by 0.354 for every unit increase in pre-purchase. Also, the coefficient value is significant at the 1% level.

The coefficient of X3 −0.449 represents the partial effect with purchase decision on Forums & Communities, holding other stages of consumer decision making constant. The estimated negative sign implies that such effect is negative and Forums & Communities score would decrease by 0.449 for every unit increase in a purchase decision. Also, the coefficient value is significant at the 1% level.

The coefficient of X4 is 0.100 represents the partial effect of Post purchase on Forums & Communities, holding other stages of consumer decision making constant. The estimated positive sign implies that such effect is positive and Forums & Communities score would increase by 0.100 for every unit increase in a purchase decision. However, Forums & Communities the coefficient value is not significant at the 5% level.

Hence, it can be inferred that there is a significant impact of Forums & Communities on Pre- and Purchase decision in the study region. The impact is elevated with Purchase decision compared with Pre-purchase. However, Purchase decision exhibits a negative impact and Pre- purchase exhibits a positive relationship with Forums & Communities.

From Table 5, the multiple regression equation is

$$ Y\, = \,3.175\, + \,0.221X_{1} \, + \,0.126X_{2} \, - \,0.484X_{3} \, + \,0.160X_{4} $$
Table 5. Coefficient table for Impact of Ratings & reviews on stages of consumer decision making
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Here the coefficient of X1 is 0.221 represents the partial effect with Need Recognition on Ratings & reviews holding other stages of consumer decision making constant. The estimated positive sign implies that such effect is positive and Ratings & reviews score would increase by 0.221 for every unit increase in need recognition. Also, the coefficient value is significant at the 1% level.

The coefficient of X2 is 0.126 represents the partial effect of Pre-purchase on Ratings & reviews, holding other stages of consumer decision making constant. The estimated positive sign implies that such effect is positive and Ratings & reviews score would increase by 0.126 for every unit increase in pre-purchase. Also, the coefficient value is significant at the 5% level.

The coefficient of X3 −0.484 represents the partial effect with purchase decision on Ratings & reviews, holding other stages of consumer decision making constant. The estimated negative sign implies that such effect is adverse and Ratings & reviews score would decrease by 0.484 for every unit increase in a purchase decision. Also, the coefficient value is significant at the 1% level.

The coefficient of X4 is 0.160 represents the partial effect of Post purchase on Ratings & reviews, holding other stages of consumer decision making constant. The estimated positive sign implies that such effect is positive and Ratings & reviews score would increase by 0.160 for every unit increase in the Post-purchase decision. Also, the coefficient value is significant at the 1% level.

Hence, it can be inferred that there is a significant impact of Ratings & reviews on all stages of consumer decision making The various stages of consumer decision making includes need Recognition, Pre-purchase, Purchases decision, and Post purchase. The impact on purchase decision is high with purchase decision followed by need recognition, Post-purchase and low with Pre-purchase. However, the impact of ratings & reviews is negative on purchase decision compared with a positive impact on need recognition stage, Pre-purchase and Post purchase.

From the above results, it is concluded that both Trust & Social commerce construct (SCCs) exhibit significant relationship across all the stages of consumer decision making namely Need Recognition, Pre-purchase, Purchase decision & Post-purchase. Hence, both the hypothesis statement, Hypothesis (H1): There is a significant relationship between trust across stages of consumer decision making for fashion e-tailing and Hypothesis 2 (H2): There is a significant relationship between social commerce constructs (Recommendations & referrals, Forums & Communities, Ratings& Reviews) across stages of consumer decision making for fashion e- tailing is established.

6 Implications of the Study

The study brings out the variations in the extent of impact of trust & social commerce constructs (SCCs) on stages of consumer decision making. Table 6 provides a guideline for the marketers with regard to right usage of social commerce construct & building Trust in accordance with stages of consumer decision making and strategies to build thereon. The results inferred establish significant relationship of Trust & social commerce construct (SCCs) with stages of consumer decision making. However, they are in very inceptive stages. The amplitude of usage of Social commerce construct (SCCs) exhibiting positive relationship has to be strengthened further and those exhibiting negative relationship has to be built upon. The usage of social commerce constructs (SCCs) in Pre-purchase stage can be reinforced further & build Trust thereon. Comparatively, both forums & communities and ratings & reviews have to strengthen to turn out to be constructive social commerce construct (SCCs). Also, Trust in the stages of Need Recognition and Purchase Decision has to be straightened out as positive trend.

Table 6. Summary - Trust & Social commerce construct (SCCs) impact on Stages of consumer decision making
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7 Limitations and Scope of Further Study

The study was confined to four social networking sites viz., Facebook, Instagram, Google Plus and Twitter. Also, the study was limited to active social networking site users who purchased fashion products online recently (less than 6 months) based in Chennai. The study provides further scope to carry out preferences of social commerce construct(s) & trust factor across fashion product categories. It also provides further scope of research to carry out social networking site specific research.