Examining online channel selection behav

28 Int J Electronic Marketing and Retailing, Vol 6, No 1, 2014 Examining online channel selection behaviour among social media shoppers: a PLS analysis Sajad Rezaei International Business School (IBS), Universiti Teknologi Malaysia (UTM), Jalan Semarak 54100 Kuala Lumpur, Malaysia Email: mmg.sajad@gmail.com *Corresponding author Wan Khairuzzaman Wan Ismail College of Business Administration, Jazan University, Kingdom of Saudi Arabia and International Business School (IBS), Universiti Teknologi Malaysia (UTM), Jalan Semarak 54100 Kuala Lumpur, Malaysia Email: mwkhair@jcba.edu.sa Abstract: Online social media shopper’s channel selection behaviour have been neglected despite its significant effect on purchase decision in both offline and online markets The purpose of this study is to examine the influence of transactive memory system (TMS) factors (specialisation, credibility and coordination), knowledge sharing (KS) and communication quality on online social media channel selection for shopping activities Structural equation modelling (SEM) was performed using partial least squares (PLS) analysis to examine measurement model and structural model for reflective constructs A total of 336 online questionnaires were collected from users of collaborative projects, social networking sites, blogs, content communities, virtual game worlds, and virtual social worlds Our results reveal that TMS factors, KS and communication quality positively influence online channel selection In addition, specialisation, credibility and coordination statistically contribute to TMS as a second order construct This study extends the social exchange theory (SET) and TMS model in B2C retail context rather than B2B Practical implications, limitations and directions for future research are discussed Keywords: online channel selection; transactive memory system; TMS; knowledge sharing; KS; communication quality; social shopper groups; online retailing Reference to this paper should be made as follows: Rezaei, S and Ismail, W.K.W (2014) ‘Examining online channel selection behaviour among social media shoppers: a PLS analysis’, Int J Electronic Marketing and Retailing, Vol 6, No 1, pp.28–51 Biographical notes: Sajad Rezaei is a PhD candidate at International Business School (IBS), Universiti Teknologi Malaysia (UTM) He holds a Bachelor of Business Administration (BBA) from Azad University of Iran and Master of Copyright © 2014 Inderscience Enterprises Ltd Examining online channel selection behaviour among social media shoppers 29 Business Administration (MBA) from Multimedia University (MMU), Malaysia He has published his research papers in Computers in Human Behavior, Journal for Global Business Advancement, International Journal of Innovation and Learning, International Journal of Retail & Distribution Management and International Journal of Business Environment His current research interests include retailing, E-commerce marketing, consumers experience and shopping behaviour Wan Khairuzzaman Wan Ismail is currently affiliates with Jazan University and Universiti Teknologi Malaysia (UTM) He obtained his Bachelor degree in Business Administration and MBA from Western Michigan University, USA He also has a PhD from University of Derby, UK His research interests are in technology management and innovation, work design, culture and leadership Introduction Multichannel strategies have become a standard approach to reach customers (Oppewal et al., 2012) and retailers with both physical and online stores have more opportunities to reach potential consumers by combining physical channels and online channel to build a competitive business model that increases business revenues Previous studies argue that a multi-channel strategy could be employed by retailers to make merchandises available to their customers through more than one distribution channels, and such multi-channel retailers can utilise the online channel to offer a wider range of business functionalities and to expand their market shares in various geographical, time, and product-related terms (Jang et al., 2013) In practice, ‘consumers’ choice of different shopping channels such as offline stores, TV channel, and Internet are mostly determined by the available marketing information Guo and Barnes (2011) Multi-channel shoppers tend to search for information in an online channel might finally purchase from a local and offline store (Chandrashekaran and Suri, 2012).  The internet technology has changed the behaviour of retailers in managing the channel and channel strategy (Chang, 2012; Izquierdo-Yusta and Newell, 2011) which begs new insights from theoretical perspective in understanding e-consumers behaviour (Bellaaj and Zekri, 2012; Zamzuri et al., 2008) Our knowledge and information has been affected by the fast pace and substantial use of social media influences all aspects of information flow (Wodzicki et al., 2012) and the way we interact with retailing In a context of retailing, where service and price levels differ among retailers and where searching for information about a product is free (switching cost reduction), consumers have control over their choices in terms of channels and retailers and thus can exercise their consumer power Switching channels could therefore induce a retailer’s switching behaviour (Heitz-Spahn, 2013) The intention of consumers to share information in online shopping relates to evaluating the benefits and risks of online behavioural tracking and the release of personal information (Jai et al., 2013) Therefore, the aim of this study is to examine the influence of transactive memory system (TMS) factors (specialisation, credibility and coordination), 30 S Rezaei and W.K.W Ismail knowledge sharing (KS) and communication quality on online social media channel selection for shopping purposes 1.1 Social shopper group  The sociability of consumers is critically important in internet store image and purchase intention (Fan et al., 2013) Social media enables easy and fast generation, sharing, diffusion and purchasing of various contents among people online (Bach and Kim, 2012), it is changing the business landscape (Rodriguez-Ardura et al., 2010) and redefining how businesses communicate across several channels of distribution (Rapp et al., 2013; Ellonen and Kosonen, 2010; Lohtia et al., 2013) Nevertheless, online shoppers are enable to easily surf and shop with online retailers through numerous devices such as personal computers, smart phones and most convenience tablets (Jai et al., 2013; Rezaei et al., 2014) According to Kaplan and Haenlein (2010), 75% of internet surfers used social media such as: Facebook, blogs, LinkedIn and Twitter by joining social networks, reading blogs, or contributing reviews to online shopping websites They categorise social media into six category including collaborative projects, social networking sites, blogs, content communities, virtual game worlds, and virtual social worlds As a social network tools, Facebook offer a valuable means to influence consumer decision-making process aiming to generate sales and revenue (Hyllegard et al., 2013) Likewise, as predicted global marketers spend more than $7.72 billion on social networking websites, a 48.5% increase from $5.2 billion in 2011 (Yang, 2013) which shows the important implication of this trend for online retailing Social media has penetrated the online retailers via providing a variety of ways for retailers to offer a more social and effective purchase experience (Harris and Dennis, 2011; Daliri et al., forthcoming) Facebook fan pages, for example, which can also be used to connect multiple social media and work as a social hub to help stimulate interaction These trends brought the importance of online information review and its application in retailing context (Tuma and Decker, 2013) Furthermore, almost half of online shoppers directly and indirectly interact with a retailer on most of social networking sites for in which retailers and brands are capitalising on this new promotional dimension to strengthen customer relationships programmes (Rapp et al., 2013) Literature review and hypotheses development  2.1 Social exchange theory The theoretical research framework in this study has been built upon the social exchange theory (SET) and TMS (see Figure 1) SET is a fundamental concepts of classical and modern economics in analysing human attitude and overall behaviour (e.g., Emerson, 1976; Thibaut and Kelly, 1959) The social exchange model specify that in order to maximise rewards and minimise costs, people and organisations interact with each other (Shiau and Luo, 2012) In addition, exchange theory has become an effective model and theoretical background for defining and explaining in marketing, retailing and consumer behaviour (Miller and Kean, 1997) “Exchange theory declare that actions are results of Examining online channel selection behaviour among social media shoppers 31 actual or expected reward under a premise that personal calculation and experience are necessary for certain action” [Cheung and Chan, (2010), p.210] Shiau and Luo (2012) show that social exchange factors are dominant elements in understanding consumers’ intention to engage toward shopping and that vendor creativity significantly affect the intention to engage in online group buying In the social context, the exchange behaviour shows several implication of individual behaviour towards certain object Figure Research model H3     Communication Quality           H6 H1   Transactive Memory System Specialization   Credibility Coordination     Online Channel Selection H4   H5 H2   Knowledge Sharing (KS)   Previous studies have adopted SET, to explain participants’ willingness in sharing knowledge (Zhang et al., 2013) Kankanhalli et al (2005) developed SET to identify cost and benefit factors affecting electronic knowledge repositioning usage The resources that facilitate KS between participants could be results in a network of relationships between individual in virtual communities and group such as social media (Chang and Chuang, 2011) This is worth knowing, if exchange in our species is made possible by evolved, neurocomputational programmes specialised for exchange itself (Cosmides and Tooby, 1994) Finally, KS is considered as social exchange because sharing of knowledge between participants is a long-term relationship and participants have general expectations of the benefits on sharing knowledge (Zhang et al., 2013) In the following subsection, the concept of KS and its relationship with research constructs id discussed 2.1.1 Knowledge sharing KS defined and explained differently by numerous literature As discussed, KS roots in the SET and has become a main and fundamental research issue and subsequently in the different management field and disciplines (Boer et al., 2011) and is a behaviour when people distributes knowledge to other members (Hsu et al., 2007) It is believed to be one of the most critical processes in knowledge management practice (Gupta et al., 2012) Although the privacy is a concern in a social network context (Mohamed and Ahmad, 2012) It may seem paradoxical that KS mostly requires individuals and clients to work together; to share and combine their knowledge but the virtualisation usually means that 32 S Rezaei and W.K.W Ismail they are physically apart (Skyrme, 1999) KS is an essential process among knowledge management procedure as it can transfer employees’ knowledge and individuals to others who are able use flow of knowledge to improve work performance (in the work environment example) (Zhang et al., 2013) Aiming to satisfy the desire to interact with and help others, online social networks have become a part of the lives of most internet users (Chang and Chuang, 2011) In order to share knowledge related in a common interests and topics most of users join virtual communities (Hung and Cheng, 2013) in which two levels of KS which are initial KS and continuous KS spreading rapidly (Zhang et al., 2013) 2.2 Transactive memory system TMS built upon the idea of theory of the group mind (Wegner, 1987) This social aspect of ‘knowing who knows what’ refer to transactive memory and knowledge that individuals have about what another person knows (Shen and Gallivan, 2008) The concept of TMS involve encoding, storing, and retrieving information and knowledge from different prospects that mostly develops in close relationships A TMS is “a group-level concept, referring to the operation of the memory systems of the individuals and the processes of communication that occur within the group” [Wegner, (1987), p.191] It TMS is integration of a person memory systems and communications between people (Oshri et al., 2009) Thus, TMS could be interpreted as sharing a system for encoding, storing and retrieving information and knowledge for certain purposes (Yoo and Kanawattanachai, 2001; Ren and Argote, 2011) Similarly, Akgün et al (2005) stressed that TMS enable the interchange of data, information and knowledge among users TMS can enhance performance through improvement of communication and coordination as a result of the team’s awareness of the collective knowledge that is available (Hsu et al., 2012) The process of TMS contains of three dimensions such as: specialisation, coordination, and credibility (Michinov et al., 2008; Ren and Argote, 2011; Chen et al., 2013) Because of multidimensional nature of TMS, there is no consensus on how to measure this paradigm (Theiner, 2013) This study measure TMS by three dimensions include: specialisation, credibility and coordination 2.2.1 Specialisation Specialisation refers to “the acknowledgement of distributed expertise within the team or group” [Michinov et al., (2008), p.328] Study investigated the effect of auditor specialisation on both audit quality and audit fees in the local government audit market in the USA therefore indicated that, there is a positive relation between auditor specialisation and audit quality (Lowensohn et al., 2007) Similarly, specialisation found as the positive association between audit firm industry specialisation and overall client disclosure quality (Dunn and Mayhew, 2004) Heung et al (2011) determine the factors influencing the development of medical tourism in Hong Kong has shown that advances in technology and the spread of information have changed the nature of specialisation and communication among countries The dimensions of competitive strategies for example: specialisation, channel selection, and other factors such as technological leadership, brand identification, customer service, speed of delivery, and pricing policy can develop differentiation (Fawcett et al., 1993; Mehra and Floyd, 1998) Furthermore, firms Examining online channel selection behaviour among social media shoppers 33 preferably using these aspects to potentially differentiate themselves from other rivals (Scully and Fawcett, 1993) 2.2.2 Credibility Lucassen and Schraagen (2013) describe credibility as the believability of information On the other hand, credibility is degree in which group members trusted each other task expertise (Michinov et al., 2008; Chen et al., 2013; Williams, 2013) The source of credibility could determine the confidence of group member who have in their thoughts about a ‘persuasive message’ (Tormala et al., 2006) Besides, it is essential to build credibility among prospect customers (Schoenbachler and Gordon, 2002) Supposedly, in banking industry credibility is necessary to ensure its good reputation and must give confidence to customer about its services (Williams, 2013) The general findings of source credibility research indicate that a low-credible source either has its messages discounted in various ways or prompts individuals to expend more effort in making a decision (Kao, 2013) 2.2.3 Coordination TMS occurs when individual group members have worked together and opportunity to communicate with each other directly (Michinov et al., 2009) In literature coordination discussed as how smoothly members are capable to collaborate during a completion of a given task (Theiner, 2013) Obviously, to accomplish a task, cooperation mechanism is essential (Noë, 2006) and it refers to the ability of the team members to work together efficiently with greater cooperation, less confusion and misunderstandings (Michinov et al., 2008) Likewise, coordination could be perceived as the harmonious adjustment and interaction of several people and enormous things to achieve a goal or certain effect (Mishra et al., 2012) Literature shows an effective coordination through electronic media is highly related to a clear norms of behaviour, common understanding about the problems at hand, and a context for interpreting knowledge (Shen and Gallivan, 2008) 2.3 Communication quality Consumer’s communication quality has critical implication for retailing in developing customer’s database The internet advancement changed people live and communication (Belanche et al., 2010; Singh and Cullinane, 2010) In addition, communication quality has changed dramatically appreciating technology development (Corby, 2008) Communication has been defined as “the formal and informal sharing of credible and meaningful information between exchange partners” [Coote et al., (2003), p.597] Specifically, it is the completeness, credibility, accuracy, timeliness and adequacy of communication flows between members (Thomas et al., 2013; Wu et al., 2013) Communication is a critical mechanism in IT systems in the way that the tools by which group members share information It is critical to have high work quality achieved by effective communication and coordination of diverse resources (Hsu et al., 2012) Internet communication quality was found as moderator in determining extensive Internet use and perceived family support (Appel et al., 2012) 34 S Rezaei and W.K.W Ismail 2.4 Hypotheses development There is a connection between signals and cooperative behaviour and cooperation can improve a selection for efficient communication to enable coordination (Noë, 2006) Literature suggest that the greater communication and coordination practices leads to more organisational innovation (Auh and Menguc, 2005) and plays a major role in enhancement of coordination and collaboration (Mishra et al., 2012) Study by Billard and Pasquale (1995) has created a model for distributed decision making, distributed game automata, focuses on how communication affects the quality of decisions and found with insufficient communication, the players are unable to avoid mutually conflicting decisions and, hence, are unsuccessful in adaptive coordination Nevertheless, Srikanth and Puranam (2011) has stated that successful coordination requires the creation of sufficient common ground, and direct communication Studies has done about exploring the effects of SourceForge.net coordination and communication tools and argued that using communication and coordination tools will improve the efficiency of open source projects (Koch, 2009) Similarly, the effectiveness of coordination is determined by the quality of communication among professionals providing care to delicate people in the community (Cramm et al., 2013) Drawn upon literature on TMS, relationships between knowledge credibility and communication quality are positive and statistically significant Schoenbachler and Gordon (2002) suggested that the reputation and perception of dependability in any particular companies which can be pro-active in developing credibility through communication strategy and credibility can be quickly erased through poor company performance Study has done regarding instructor credibility as a potential factor of instructors’ prosaically communication behaviours by using structural equation modelling (SEM) technique and found credibility enhances or magnifies the positive effects that pro-social communication behaviours have on student learning (Schrodt et al., 2009) Various communication quality attributes have been suggested; among them clearness, accuracy, currency, and credibility play an important role (Dai et al., 2013) Besides source credibility, which is as important for facilitating intercultural communication (Brown and Youmans, 2012) Generally, source credibility can affect communication effectiveness through a variety of distinct mechanisms, depending on how message recipients process the communication messages (Kao, 2013) Thus the followings are hypothesised: H1 There is a positive relationship between TMS and communication quality H2 There is a positive relationship between TMS and KS H3 There is a positive relationship between TMS and online channel selection Accordingly, KS begs an appropriate communication channels and surprisingly the communication systems themselves cannot it all Communication and KS is a combination of exchange programmes, documentation in which encourages knowledge transfer, flow and learning (Hansen et al., 2013) and this approach would help the vision and customer goals within development of new product process (McAdam et al., 2008) The cross functional group cooperation and working could be achieved through Examining online channel selection behaviour among social media shoppers 35 communication and KS to improve product design and product and service engineering (Huang, 2009) Similarly, active communication of the ongoing KS helps users to develop a clearer vision of KS expectations (O’Neill and Adya, 2007) Therefore, using online social media, users might share experiences about retail to plan their future purchase KS is affected by a variety of reasons (Cabrera et al., 2006) Communication is vital for the functioning of KS networks (Wagner and Buko, 2005) Furthermore, operations processes addresses KS and communication which are key risk areas (Hansen et al., 2013) Evidence suggests that shopping, similarly to other addictive behaviours, for approximately 6% of men and women, can share many physiological and behavioural characteristics of addiction (Hartston, 2012) Explicit knowledge exists in symbolic or written form but the implicit knowledge can be expressed in written form but is not yet expressed (Zhang et al., 2013) In terms of codification, “tacit knowledge is typically considered more novel and valuable because it contains details about customers’ latent needs and usage motives” [Mahr and Lievens, (2012), p.165] but “explicit knowledge usually stored in knowledge repositories which can be directly exploited by knowledgebased systems or humans to solve specific problems” [Liu et al., (2011), p.427] Accordingly the skills and internet expertise of internet users might influence users consideration of channel and retail selection (Rezaei and Amin, 2013) Therefore, the type of knowledge flow and information process among individual buyers in the social context also may vary depends on the type of social values and type of knowledge Thus, we hypothesise that: H4 There is a positive relationship between KS and communication quality H5 There is a positive relationship between KS and online channel selection H6 There is a positive relationship between communication quality and online channel selection Research method To conduct statistical analysis and test the proposed theoretical framework (Figure 1), a total of 550 email invitation were sent to the university students in Malaysia The online questionnaire was designed in three sections In the first section, the respondents were asked about type of activities that they are involved online This section was designed to ensure that respondents have experienced with collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds The second section contains demographic profile of respondents and third section content those questions regarding to exogenous and endogenous constructs We managed to collect 341 questionnaires through the email survey out of which were incomplete making a total usable questionnaire of 336 Therefore, the respondent’s rate in this study is 61.09% Table depicts the demographic profile of respondents in our study 36 S Rezaei and W.K.W Ismail Table Respondents profile Demographic Gender Age Culture Education level University Type of social media used Characteristics Frequency (%) Male 39.0 Female 61.0 Below 18 39.0 19 to 24 38.7 35 to 44 17.0 45 and above 5.4 Malay 42.3 Chinese 36.9 Indian 20.8 Diploma holder 16.1 Bachelor holder 37.5 Master holder 34.8 PhD holder 11.6 UTM 30.1 UPM 48.5 MMU 21.1 Collaborative projects 10.7 Blogs 15.8 Content communities 25.3 Social networking sites 27.1 Virtual game worlds 15.5 Virtual social worlds 5.7 3.1 Measurement scales In section the third section which was the main section of the questionnaire, the factors that affect social media user’s behavioural intention was translated into several questions (the measurement scales are presented Appendix A) point Likert scale with the scale format which set to asks target population to indicate the extent to which they agree or disagree with a series of mental belief or behavioural belief statements about a given object TMS was constructed with dimensions namely specialisation, creditability and coordination To measure specialisation items, creditability items and coordination items were adopted from Li and Huang (2013) To measure KS items were adopted from Hsu and Chang (2012) Moreover, to measure communication quality items from Chen et al (2013) and to measure consumers channel selections items were adopted from Rajamma and Neeley (2005) Examining online channel selection behaviour among social media shoppers 37 3.2 Common method variance Common method bias or CMV may exist due to the single survey method to collect primary data in an empirical study such as social science domain (Rezaei and Ghodsi, 2014) This study addressed common method variance (CVM) with potential threat by following guideline proposed by Podsakoff et al (2003) At the data analysis stage three statistical techniques: the Harman’s one-factor test, in the partial correlation procedures, the structural model marker-variable technique were conducted Thus, our statistical result shows that CVM is not a concern in this study 3.3 Analysis of measurements and structural model The SEM was employed using partial least squares (PLS) analysis to assess measurement and structural model for reflective constructs We employed PLS-SEM to analyse the data by applying SmartPLS software (Ringle et al., 2005) PLS-SEM is advantageous compared to covariance-based SEM when analysing predictive research models that are in the stages of theory development (Gimbert et al., 2010) According to Hair et al (2011), reasons for using PLS-SEM is “if the goal is predicting key target constructs or identifying key driver constructs” The common approach in reporting result is to present them in two steps (Chin, 2010): first is to focus on the reliability and validity of the item measures and second structural model (Hair et al., 2013; Rezaei and Ghodsi, 2014) By performing PLS Algorithm procedures, reflective measurement models’ validity assessment focuses on convergent validity and discriminant validity Moreover, discriminant validity was assesses according to Fornell and Larcker (1981) criterion Before assessing the structural model we examine the structural model for collinearity Followed by Hair et al (2013) to assess structural model we performed bootstrapping to assess the path coefficients’ significance and to assess predictive relevance we also performed blindfolding to obtain cross-validated redundancy measures for each construct Moreover, the level of the R² values, the f2 effect size, and the predictive relevance (Q2 and the q2 effect size) were assessing in line with structural model Results 4.1 Assessment of measurement model To assess reflectively measured models, we first examine outer loadings, composite reliability, average variance extracted (AVE = convergent validity) and discriminant validity for all exogenous and endogamous constructs Table presents reliability, internal consistency and convergent validity which show that all outer loadings of the constructs are sufficiently above the minimum threshold value of 0.70 All reflective constructs have high levels of internal consistency reliability, as demonstrated by the above composite reliability values The AVE values (convergent validity) are well above the minimum required level of 0.50, thus, demonstrating convergent validity for all three constructs Appendix B shows the measurement model specifications including outer loadings and path coefficients results from PLS algorithm 38 Table S Rezaei and W.K.W Ismail Reliability, internal consistency and convergent validity Second-order construct* First-order construct CQ KS OCS TMS TMSC TMSCO TMSS Item Outer loadings AVEa Composite reliabilityb CQ1 0.856 0.745 0.921 CQ2 0.882 0.297 CQ3 0.872 0.280 CQ4 0.843 0.287 0.686 0.916 Cronbachs Outer alpha weights 0.886 0.886 0.294 KS1 0.847 KS2 0.796 0.287 0.224 KS3 0.793 0.192 KS4 0.870 0.257 KS5 0.834 OCS1 0.865 0.243 OCS2 0.884 0.211 OCS3 0.882 0.199 OCS4 0.833 0.179 OCS5 0.852 0.188 0.732 0.942 0.927 0.211 OCS6 0.815 TMSC1 0.827 0.179 TMSC2 0.839 0.229 TMSC3 0.883 0.241 TMSC4 0.862 0.241 TMSC5 0.843 0.235 0.724 0.757 0.929 0.940 0.905 0.920 0.229 TMSCO1 0.880 TMSCO2 0.859 0.237 0.235 TMSCO3 0.884 0.234 TMSCO4 0.862 0.225 TMSCO5 0.865 TMSS1 0.919 TMSS2 0.903 0.246 TMSS3 0.860 0.234 TMSS4 0.838 0.225 TMSS5 0.801 0.209 0.219 0.749 0.937 0.915 Notes: CQ: communication quality; KS: knowledge sharing; specialisation (TMSS); creditability (TMSC) and coordination (TMSCO) a Average variance extracted (AVE) = (summation of the square of the factor loadings) / {(summation of the square of the factor loadings) + (summation of the error variances)} b Composite reliability (CR) = (square of the summation of the factor loadings) / {(square of the summation of the factor loadings) + (square of the summation of the error variances)} 0.240 Examining online channel selection behaviour among social media shoppers 39 Table presents the discriminant validity of the key constructs The off-diagonal values in the matrix indicates the correlations between the latent constructs, thus, the Fornell-Larcker criterion for discriminant validity has been met The results on the next slide indicate there is discriminant validity between all the constructs Table Construct CQ KS OCS TMSC TMSCO TMSS Discriminant validity of the key constructs a CQ KS OCS TMSC TMSCO TMSS 0.828 0.460 0.318 0.319 0.356 0.856 0.417 0.445 0.412 0.851 0.198 0.170 0.870 0.016 0.865 b 0.863 0.486 0.540 0.400 0.300 0.370 a Notes: CQ: communication quality; KS: knowledge sharing; specialisation (TMSS); creditability (TMSC) and coordination (TMSCO) b The square root of AVE of every multi-item construct (first-order and second-order) is shown on the main diagonal Table shows loading and cross loadings in assessing the measurement model Comparing the loadings across the columns, indicator’s loadings on its own construct are in all cases higher than all of its cross loadings with other constructs Thus, the results indicate that there is discriminant validity between all constructs Moreover, Table shows that weights of the first order constructs on the designated second order construct indicating that TMS is a second order factors with three dimensions namely specialisation (TMSS), creditability (TMSC) and coordination (TMSCO) The weight for TMSS is 0.921 with t-value of 71.597; implying first order construct designated on TMS Similarly, TMSC with weight of 0.957 and t-statistics of 124.848 and TMSCO with the weight of 0.900 and t-statistics of 60.964 shows a first order constructs designated on TMS Table Loading and cross loadings Research construct Item CQ KS OCS TMSC TMSCO TMSS CQ CQ1 0.856a 0.551 0.631 0.608 0.561 0.595 CQ2 0.882 0.596 0.553 0.549 0.649 0.557 CQ3 0.872 0.527 0.403 0.562 0.504 0.575 CQ4 0.843 0.523 0.678 0.482 0.520 0.543 KS1 0.602 0.847 0.658 0.575 0.562 0.605 KS2 0.476 0.796 0.538 0.412 0.404 0.447 KS3 0.406 0.793 0.427 0.377 0.357 0.460 KS4 0.568 0.870 0.594 0.477 0.504 0.479 KS5 0.548 0.834 0.552 0.458 0.471 0.458 KS a Note: Italicised values are loadings for items which are above the recommended value of 0.5 40 S Rezaei and W.K.W Ismail Table Research construct OCS TMSC TMSCO TMSS Loading and cross loadings (continued) Item CQ KS OCS TMSC TMSCO TMSS OCS1 0.811 0.572 0.865 0.560 0.621 0.591 OCS2 0.594 0.637 0.884 0.589 0.625 0.582 OCS3 0.657 0.571 0.882 0.557 0.584 0.546 OCS4 0.680 0.524 0.833 0.484 0.476 0.476 OCS5 0.705 0.569 0.852 0.538 0.546 0.533 OCS6 0.644 0.607 0.815 0.582 0.560 0.562 TMSC1 0.531 0.525 0.557 0.827 0.623 0.655 TMSC2 0.523 0.473 0.541 0.839 0.629 0.442 TMSC3 0.558 0.499 0.583 0.883 0.608 0.632 TMSC4 0.554 0.443 0.529 0.862 0.519 0.542 TMSC5 0.545 0.460 0.536 0.843 0.627 0.691 TMSCO1 0.557 0.467 0.531 0.543 0.880 0.621 TMSCO2 0.626 0.503 0.599 0.539 0.859 0.522 TMSCO3 0.654 0.523 0.621 0.697 0.884 0.626 TMSCO4 0.588 0.486 0.562 0.668 0.862 0.594 TMSCO5 0.649 0.477 0.592 0.633 0.865 0.568 TMSS1 0.625 0.532 0.597 0.774 0.608 0.919 TMSS2 0.600 0.570 0.593 0.787 0.660 0.903 TMSS3 0.586 0.486 0.547 0.745 0.632 0.860 TMSS4 0.534 0.507 0.533 0.739 0.578 0.838 TMSS5 0.490 0.484 0.503 0.671 0.532 0.801 a Note: Italicised values are loadings for items which are above the recommended value of 0.5 Table Weights of the first order constructs on the designated second-order construct Second-order constructs First-order constructs Weight T-value TMS TMSS 0.921 71.597* TMSC 0.957 124.848* TMSCO 0.900 60.964* Notes: *Critical t-values: 2.58 (significance level = 1%) 4.2 Assessment of structural model After confirming the reliability and validity of the construct measures, we then assess the structural model results to determine the model’s predictive capabilities and the relationships between the constructs Before assessing the structural model for significant, we examine the structural model for collinearity We consider tolerance levels below 0.20 (VIF above 5.00) in the predictor constructs as indicative of Examining online channel selection behaviour among social media shoppers 41 collinearity that is too high Our empirical assessment reveal that collinearity is not exceeds above thresholds By performing the PLS-SEM algorithm the required estimates are obtained for the structural model relationships (the path coefficients or beta), which shows the hypothesised relationships between the constructs Accordingly, by deploying Bootstrapping procedures we examined which path coefficients are significant as shown in Table Table Summary of hypotheses testing Path Path coefficients Standard deviation Standard error H1 TMS -> CQ 0.530 0.055 H2 TMS -> KS 0.622 0.052 H3 TMS -> OCS 0.113 0.060 H4 KS -> CQ 0.307 0.056 H5 KS -> OCS 0.190 0.047 H6 CQ -> OCS 0.657 0.059 Hypotheses T statistics Decision 0.055 9.584** Supported 0.052 11.993** Supported 0.060 1.880* Supported 0.056 5.499** Supported 0.047 4.034** Supported 0.059 11.057** Supported Notes: Critical t-values: *Two-tailed test are 1.65 (significance level = 10%) **2.58 (significance level = 1%) As depicted in above table, hypothesis which proposes a positive relationship between TMS and CQ was accepted with path coefficient of 0.530, standard deviation of 0.055, and standard error of 0.055 and t-value of 9.584 The path coefficient is relatively high The H2 (TMS and KS) with path coefficient = 0.622, standard deviation = 0.052, standard error = 0.052 and t-value = 11.993 was accepted Moreover, H3 (TMS and OCS) with path coefficient = 0.113, standard deviation = 0.060, standard error = 0.060 and t-value = 1.880; the H4 (KS and CQ) with path coefficient = 0.307, standard deviation = 0.056, standard error = 0.056, and t-value = 5.499; the H5 (KS and OCS) with path coefficient = 0.190, standard deviation = 0.047, standard error = 0.047, and t-value = 4.034; H6 (CQ and OCS) with path coefficient = 0.657, standard deviation = 0.059, standard error = 0.059, and t-value = 11.057 were accepted Appendix B shows the measurement model specifications including path coefficients Accordingly, the size of endogenous construct for CQ =0.578 and Q2 =0.362 implying high predictive relevance of construct Moreover, for KS, R2 = 0.386 with Q2=0.15; OCS, R2 = 0.772 and Q2 =0.401 which are highly relevant Table Results of R2 and Q2 R2 Q2 CQ 0.578 0.362 KS 0.386 0.15 OCS 0.772 0.401 Endogenous construct The f2 effect size measures the change in the R² value when a specified exogenous construct is omitted from the model It is used to evaluate whether the omitted predictor construct has a substantive impact on the R² values of the endogenous construct Table 8(a) and 8(b) depicts path coefficients, f2 and q2 42 S Rezaei and W.K.W Ismail Table 8(a) Path coefficients, f2 and q2 CQ PC* f KS q PC f OCS q CQ KS 0.307 0.102 0.090 TMS 0.530 0.148 0.109 0.622 0.401 0.100 PC f2 q2 0.657 0.361 0.098 0.190 0.053 0.001 0.113 Notes: Path coefficients f2 effect size q2 effect size Table 8(b) Path Coefficients, f2 and q2 TMSC TMSCO TMSS PC* f2 q2 PC f2 q2 PC f2 q2 0.957 0.579 0.124 0.900 0.519 0.245 0.921 0.602 0.265 CQ KS TMS Notes: Path coefficients f2 effect size q2 effect size Discussion By understanding social media and social aspects of customer’s behaviour, retailers can bridge the gap between interactions and consumers channel selection Consumer generate content on social media influence value co-creation activities within the firm and specifically retailer’s survival (Fisher and Smith, 2010) Comparing with offline shopping, there are some disadvantages, and advantages of online shopping such as lack of face-to-face communication and difficulty in judging the quality of commodities due to lack of physical touching (Liu et al., 2013; Chandrashekaran and Suri, 2012) In fact, when online shopping was first introduced, most consumers felt unsafe due to the perceived risks of conducting online payment Therefore they prefer to place the order via the traditional channels where they can consult the salesperson (Jang et al., 2013) Consumers interact with sales personnel; their friendliness and knowledge can affect consumers’ purchasing decision in a physical store (Lim and Dubinsky, 2004) Similarly, customers in online channels generally perceive greater information control than customers using offline channels in purchasing activities (Gensler et al., 2012) From the college students’ perspectives, online search makes more sense as the Internet has been a part of their daily activities (Fong et al., 2013) Endo et al (2012) declare that consumers have a harmony between purchase type and shopping mode, and they tend to shop tangible products from brick-and-mortar stores rather than online Because the possibility to actually touch and try out products before buying them is unique to offline channels at least for physical items (Kollmann et al., 2012) Accordingly forcing customers to use certain channels may turn them off because it steers them to use Examining online channel selection behaviour among social media shoppers 43 channels that are contrary to their preferences This may cause reactance and dissatisfaction (Konuş et al., 2013) 5.1 Managerial implications By shopping through the online channel, online social shoppers believe that they save time and can order more products Furthermore, our empirical results shows that online social media users prefer to surf the online shopping channels They use the internet channel to order because the product is unavailable at the local store and that they believe the online channel shopping is more convenient than going to the store To enhance and influence of consumer’s channel selection behaviour, managers should look into three important dimensions of TMS including specialisation, creditability and coordination Accordingly, our results show that each online group member has specialised knowledge of some aspects of shopping activities, knowledge about an aspect of the shopping activities that no other team member has Different online group member are responsible for expertise in different areas of shopping Moreover, the specialised knowledge of several different online group members is needed to complete the shopping activities and users know which online group members have expertise in specific areas of shopping Therefore, management of online channel retail depends heavily on specialisation of online group member Furthermore, online channel selections depend on perception creditability by users Online social shoppers should be comfortable in accepting procedural suggestions from other online group members and have trust on the other members’ knowledge Users should be confident of the credibility of information that other online group members bring to the discussion This is critically important in understanding user’s generation of information regarding shopping activities On the other hand, coordination among online social shopper groups influences KS, communication quality and online channel selection behaviour Users in online group member work together in a well-coordinated fashion in which online group members have very few misunderstandings about shopping Online group members not need to backtrack and start over for shopping This will help online group members accomplish the task smoothly and efficiently and avoid confusion about how to complete the shopping activities KS contributes to online channel selection and communication quality in the way that users frequently contribute knowledge to other people in shopping activities And usually actively share their knowledge with others within shopping activities and spend a lot of time conducting KS activities within shopping activities This has critical managerial implication because users spend more time in discussing the complicated problems with other people within shopping activities and normally involve themselves in discussions of various topics rather than specific ones This is important because in developing communication quality, online group members answer questions in a timely manner, provide thoughtful and useful responses, thus, assist online group members in their online purchase 5.2 Limitations and directions for future studies This study left some limitations and suggest new avenue for researcher to fill those gaps Firstly, this study is limited to a sample size of Malaysian university students Although 44 S Rezaei and W.K.W Ismail the sample size in this study is not an issue, future researchers should evaluate the proposed hypothesis in other countries among non-student online social shoppers Secondly, this study has focused on social media 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OCS5 OCS6 Communication quality Online channel selection Measurement By shopping through the online channel, I save a lot of time I’m ordering more things via the online channel I like shopping via the online channel I love to surf the online shopping channels I use the internet channel to order because I can’t find what I want in the local store Online channel shopping is more convenient than going to the store Online group members answer each other’s questions in a timely manner Online group members response to each other’s questions are correct and useful Members on our team answer each other’s questions in a thoughtful manner Overall, online group members response were useful to purchase a product I frequently contribute my knowledge to other people in shopping activities I usually actively share my knowledge with others within shopping activities I usually spend a lot of time conducting knowledge sharing activities within shopping activities I usually spend a lot of time in discussing the complicated problems with other people within shopping activities I usually involve myself in discussions of various shopping topics rather than specific topics Our online group member works together in a well-coordinated fashion Our online group members have very few misunderstandings about shopping Our online group members need not to backtrack and start over a lot for shopping Online group members accomplish the task smoothly and efficiently There is not confusion about how we would accomplish the shopping activities I am comfortable accepting procedural suggestions from other online group member I trust that other members’ knowledge about the shopping is credible I am confident relying on the shopping information that other online group members bring to the discussion When other online group members give information, I don’t need to double-check it for myself I have much faith in other members’ ‘expertise’ Each online group member has specialised knowledge of some aspect of shopping activities I have knowledge about an aspect of the shopping activities that no other team member has Different online group member are responsible for expertise in different areas of shopping The specialised knowledge of several different online group member is needed to complete the shopping activities I know which online group members have expertise in specific areas of shopping Notes: items; – strongly disagree to – strongly agree KS1 KS2 KS3 KS4 KS5 TMSCO1 TMSCO2 TMSCO3 TMSCO4 TMSCO5 Coordination TMSC1 TMSC2 TMSC3 TMSC4 TMSC5 Credibility TMSS1 TMSS2 TMSS3 TMSS4 TMSS5 Specialisation Knowledge sharing (KS) Transactive memory system Source Rajamma and Neeley (2005) Chen et al (2013) Hsu and Chang (2012) Li and Huang (2013) 50 S Rezaei and W.K.W Ismail Appendix A Measurements Examining online channel selection behaviour among social media shoppers Appendix B Measurement model specifications: outer loadings and path coefficients (PLS algorithm results) (see online version for colours) 51