Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=tbit20 Behaviour & Information Technology ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/tbit20 Assessing the impact of persuasive features on user’s intention to continuous use: the case of academic social networking sites Isaac Wiafe, Felix Nti Koranteng, Ferdinand Apietu Kastriku & Gifty Oforiwaa Gyamera To cite this article: Isaac Wiafe, Felix Nti Koranteng, Ferdinand Apietu Kastriku & Gifty Oforiwaa Gyamera (2022) Assessing the impact of persuasive features on user’s intention to continuous use: the case of academic social networking sites, Behaviour & Information Technology, 41:4, 712-730, DOI: 10.1080/0144929X.2020.1832146 To link to this article: https://doi.org/10.1080/0144929X.2020.1832146 Published online: 15 Oct 2020. Submit your article to this journal Article views: 653 View related articles View Crossmark data Citing articles: 10 View citing articles https://www.tandfonline.com/action/journalInformation?journalCode=tbit20 https://www.tandfonline.com/journals/tbit20?src=pdf https://www.tandfonline.com/action/showCitFormats?doi=10.1080/0144929X.2020.1832146 https://doi.org/10.1080/0144929X.2020.1832146 https://www.tandfonline.com/action/authorSubmission?journalCode=tbit20&show=instructions&src=pdf https://www.tandfonline.com/action/authorSubmission?journalCode=tbit20&show=instructions&src=pdf https://www.tandfonline.com/doi/mlt/10.1080/0144929X.2020.1832146?src=pdf https://www.tandfonline.com/doi/mlt/10.1080/0144929X.2020.1832146?src=pdf http://crossmark.crossref.org/dialog/?doi=10.1080/0144929X.2020.1832146&domain=pdf&date_stamp=15 Oct 2020 http://crossmark.crossref.org/dialog/?doi=10.1080/0144929X.2020.1832146&domain=pdf&date_stamp=15 Oct 2020 https://www.tandfonline.com/doi/citedby/10.1080/0144929X.2020.1832146?src=pdf https://www.tandfonline.com/doi/citedby/10.1080/0144929X.2020.1832146?src=pdf Assessing the impact of persuasive features on user’s intention to continuous use: the case of academic social networking sites Isaac Wiafe a, Felix Nti Korantengb, Ferdinand Apietu Kastrikua and Gifty Oforiwaa Gyamerac aDepartment of Computer Science, University of Ghana, Accra, Ghana; bDepartment of Information Technology Education, University of Education, Kumasi, Ghana; cDepartment of Development Policy, Ghana Institute of management and Public Administration, Accra, Ghana ABSTRACT Social networking sites enable people to connect, communicate and share ideas. These sites have therefore become key for information sharing. Particularly, academics and researchers have adopted them for networking and collaborations. This study seeks to investigate how embedded persuasive features on social networking sites designed for academics and researchers affect continuous use intention. The study adopted an existing model for assessing the effectiveness of persuasive features on systems and sampled 416 participants who are engaged in academic research and analyzed their responses. The results indicate that Social Support, Computer–Human Dialogue Support and Primary Task Support significantly impact how users perceive social networking sites designed for effective academic work. Contrary to existing knowledge that Perceived Credibility, Perceived Effectiveness, Perceived Effort and Perceived Social Support all impact an individual’s Intention to Continuous Use of a system, only Perceived Credibility was observed to impact Intention to Use continuously. The findings also proved that affective ties and mutual support on academic social networking sites influence behaviour. ARTICLE HISTORY Received 2 August 2018 Accepted 30 September 2020 KEYWORDS Academic social networking sites; persuasive technology; persuasive systems design; academic collaboration; use continuance 1. Introduction Knowledge sharing has become critical in the present era of globalisation and internationalisation. Sub- sequently, Social Networking Sites (SNS) have become key means for information and knowledge sharing. SNS can broadly be perceived as an internet or mobile-based social space where people connect, com- municate, create and share content with others (Boyd & Ellison, 2007) . The design of online social networks and their features can influence the formation of users’ behavioural and social norms. However, the func- tionalities enabled by a particular social networking site may not be fit for all purposes and users (Papacharissi 2009). For example, whilst YouTube is a video-sharing social network and a key platform for disseminating multimedia information, Mendeley is not conducive for the viral posting of videos. Hence, users on these platforms may exhibit different behavioural norms. Yet, social network use is highly pervasive within edu- cation society (Abrahim et al. 2019). Some researchers have argued that social network use has become one of the vital daily activities performed by researchers (Boumarafi 2015; Del Valle et al. 2017). Academics who seek to share knowledge or learn through social networks are encouraged to adopt academic specific social networking sites (Jeng, He, and Jiang 2015; Kor- anteng, Wiafe, and Kuada 2019). Academic Social Networking Sites (ASNS) are online social platforms designed purposely to enable academics articulate their professional network and simplify activi- ties including collaborative research (Oh and Jeng 2011). They are designed to facilitate effective and efficient knowledge sharing. Benbasat (2010) described them as neutral, because they do not have any agenda of their own except what they were built for. Recently, scholars have highlighted the increasing importance of ASNS within the scholarly community (Bardakci, Arslan, and Ünver 2017; Jeng, He, and Jiang 2015; Meishar-Tal and Pieterse 2017). Knowledge sharing, participating in a broader network of contacts, and enhanced visibility of the opportunities in academic community have all been listed as benefits of ASNS use (Bardakci, Arslan, and Ünver 2017). Despite its numerous benefits, ASNS have also been criticised. For example, Meishar-Tal and Pieterse (2017) and Collins, Shiffman, and Rock (2016) have all reported that social interaction that facilitates knowl- edge sharing is rare on ASNS, rather users predomi- nately use such systems to consume information. Though there are diverging opinions on whether or © 2020 Informa UK Limited, trading as Taylor & Francis Group CONTACT Isaac Wiafe iwiafe@ug.edu.gh Department of Computer Science, University of Ghana, P O Box LG163, Legon, Accra, Ghana BEHAVIOUR & INFORMATION TECHNOLOGY 2022, VOL. 41, NO. 4, 712–730 https://doi.org/10.1080/0144929X.2020.1832146 http://crossmark.crossref.org/dialog/?doi=10.1080/0144929X.2020.1832146&domain=pdf&date_stamp=2022-04-13 http://orcid.org/0000-0003-1149-3309 mailto:iwiafe@ug.edu.gh http://www.tandfonline.com not ASNS facilitate knowledge sharing or information consumption, there is no ambiguity on their ability to transfer information within academia. Both knowledge sharing and information consumption involve knowl- edge transfer. What is lacking are the factors that seek to promote continuous use of these websites. Currently, existing studies on continuous use of social networking sites mostly focus on generic sites (Christy, Pui-Yee, and Lee 2011; Ferebee and Davis 2009; Ku, Chen, and Zhang 2013). Again, although some studies have shown behav- ioural changes resulting from persuasive technology (Orji 2014), studies that specifically uncover the persua- sive features of ASNS and their relationships to use Continuance Intention is lacking. This study therefore explores the persuasive features that influence continuous use of ASNS. It adopted a research model proposed by Lehto and Oinas-kukkonen (2015) to evaluate the impact of the various persuasive features of academic social network sites on their con- tinuous use. The discussions in this paper are presented as follows: first a review of related literature, followed by the formulation of hypothesis, the methodology adopted for the study and analysis of the data collected. The findings, discussion of the implications of the findings and conclusions were finally presented. 2. Related literature 2.1. Use continuance intention of social networking sites As mentioned earlier, existing evidence on use continu- ance intention on social networking sties have focused on generic SNS. Some existing studies have demon- strated that the main factors that impact continuance use intention of SNS are effectiveness (Kwon and Wen 2010; Lin and Lu 2011a), social interaction ties, trust and shared values (Lin and Lu 2011b). In other studies, perceived enjoyment, perceived ease of use, satisfaction and subjective norms are the major contributors to intention to continuous use (Chang et al. 2015; Hong and Barry 2018; Mouakket 2015). Similarly, social pres- ence and personality traits have also been concluded to impact use continuance intention among students (Christy, Pui-Yee, and Lee 2011; Mouakket 2018). In all these studies, Facebook was used as the main social networking site. Hence the findings cannot be general- ised. More importantly, other studies have provided conflicting results. Ku, Chen, and Zhang (2013) explained that gratifications, perceived critical mass, subjective norms, and privacy concerns influence SNS perceived effectiveness with regional differences serving as moderating factors. Studies into social networking platforms designed for games have found that, social norms do not predict use continuance (Shu and Lin 2017). Others also demon- strated that flow experience plays a mediating role and it produces indirect effects in predicting continuance use of SNS (Chiao-Chen 2013). In particular, users’ intention to use SNS games is impacted by their optimal experience which is achieved through a user’s personal interaction with the system or his/her social interactions with other users (Choi and Kim 2004). Although this provides some amount of information regarding use continuance intention of SNS, it is a challenge to con- clude that similar factors may apply in ASNS. For example whereas prior generic SNS studies posit that ease of use strongly influence use continuance intention, Salahshour Rad et al. (2017) found the reverse on ASNS. Perhaps, this is because users’motivations for using SNS are often different from ASNS (Megwalu 2015). Indeed, many studies have shown that researchers do not prefer SNS for academic purposes (Roblyer et al. 2010; Segado- Boj, Chaparro Domínguez, and Castillo Rodríguez 2015) but rather ASNS. This is due to its versatility and support for academic activities. It provides social affordance that promotes or reinforces a self-monitor- ing culture among academics: a position that most uni- versities policies emphasise (Duffy and Pooley 2017). The next section discusses some of the major character- istics and differences between ASNS and SNS. 2.2. Academic social networking sites The introduction of Internet has undeniably trans- formed knowledge sharing and transfer: it has enabled personalisation of learning (de Hond and Rood 2017). Particularly, the capabilities of social networking sites and platforms further enhance the ubiquitous and viral spread of information. It has been proven that its ubiquitous property has permeated tertiary institutions by providing students and faculty with a platform that supports collaboration and knowledge sharing (Dabner 2012). Academics and researchers tend to engage more through the use of SNS, learn actively and also share knowledge on these sites (Azeta, Eweoya, and Ojumah 2014). Although, generic SNS support academics and researchers, the full potential of these platforms have not been exploited appropriately within the academic community (Bala 2018). Some scholars argued that designers fail to meet the requirement of academics and researchers in their designs (Neville, Heavin, and Walsh 2005). One major objective of scholars is the presentation of new knowledge which must be verified by other scholars (Megwalu 2015). They uphold facts: a key value in their profession. Accordingly, members BEHAVIOUR & INFORMATION TECHNOLOGY 713 of scholarly communities are directly associated with the contents they produce, and this enables an estab- lished system of meritocracy. Communication medium influences the content of communication. In other words, contents of generic SNS fail to reflect these scho- larly norms (Thelwall and Kousha 2014). Consequently, scholars are unable to physically authenticate the val- idity and relevance of the huge amount of information posted on SNS (Bright, Kleiser, and Grau 2015). This makes it unattractive to them. ASNS, as defined earlier, facilitate academic related activities (Meishar-Tal and Pieterse 2017). They pro- mote knowledge sharing amongst academics and researchers (Koranteng and Wiafe 2019) by providing a platform for distributing articles and publications, research agenda, citation scores, etc. Although existing generic SNS such as Facebook, Twitter, YouTube, etc., can be used for similar activities, ASNS have been con- sidered more effective (Jeng, He, and Jiang 2015). Their user interfaces, schemas, and other properties afford these activities with ease. Arguably, the behavioural norms exhibited on generic SNS are different from those exhibited on ASNS (Koranteng and Wiafe 2019). For instance, whilst a post by a user regarding a red card a player received during a match may receive responses on sites such as Facebook and Twitters, it will hardly receive any response from sites such as Aca- demia.edu or ResearchGate if it does not demonstrate any research agenda. As compared to generic SNS, existing ASNS are pro- vided, controlled and supported by research publishing outlets, academic institutions or organisations inter- ested in academic and research activities. This support makes them popular among research community. In addition, ASNS provide better methods for profile man- agement by mapping user details to their achievements. Publications, scholarly projects and citation indexes which are not part of most generic SNS are part of a user’s profile on ASNS. Additionally, academic seniority can be measured on these platforms as they are directly correlated to the number of nodes a user have within the network (Jordan 2014). Consequently, most academics prefer to use ASNS to connect with other scholars (Elsayed 2016; Nández and Borrego 2013). Although there are studies on ASNS, most have focused on issues relating to citation networks and bibliometrics (Li, Thelwall, and Giustini 2011), with less attention on how the features and properties of ASNS promote its continuous use. This is worrying, particularly consider- ing that a recent study on ASNS by Koranteng and Wiafe (2019) demonstrated that features that promote knowledge sharing on generic SNS are different from that of ASNS. Some scholars have recommended the use of persua- sive features on online networks (Wiafe, Nakata, and Gulliver 2011), whereas others have shown that some social networks exhibit characteristics that suggest that their designers factored persuasive features (Fogg 2009). Current studies have argued that the effectiveness of persuasive strategies in social networking sites is dependent on a user’s profile da Silveira, Nobre, and Cardoso (2014). Although ASNS possess characteristics such as simplicity and accessibility and this makes it ideal for persuasion (Oduor and Oinas-Kukkonen 2015), not all persuasive principles can be effective on these sites (Adaji and Vassileva 2016b), considering that different forms of gratifications determine how these sites are used (Sheldon et al. 2017). This raises the need for identifying various principles that are effec- tive for the different tasks on ASNS. With this backdrop, this study seeks to investigate features that support continuous use of ASNS. It is expected that the findings will provide pertinent infor- mation to both designers and researchers on how to bet- ter design ASNS. The next section is a discussion on the research model and hypothesis adopted for the study. 3. Research model and hypothesis Persuasive systems are systems that incorporate various strategies aimed at influencing users’ behaviour for a targeted outcome (Fogg 2007; Torning, Hall, and Oinas-kukkonen 2009). Researchers have demonstrated that incorporating persuasive features into systems in different domains such as e-commerce (Kaptein and Eckles 2012), health (Jalil and Orji 2016; Orji, Nacke, and Di Marco 2017; Wiafe and Nakata 2012a, 2012b), pro-environmental interventions (Midden & Ham, 2018), education (Dabi et al. 2018), safety and user training (Chittaro 2012; Forget and Chiasson 2008) pro- motes behaviour and attitude change. They operate either as computer-mediated persuasion or computer– human persuasion. In computer-mediated persuasion, persuaders influence users via computers (e.g. email, discussion platforms, social network systems) whereas in computer–human persuasion, the intention of the persuader is transferred into a computer or a device to persuade the user (Fogg 2007; Torning, Hall, and Oinas-kukkonen 2009). Oinas-Kukkonen (2010) con- ceptualised these systems as Behaviour Change Support System(s) (BCSS), since they facilitate human behaviour change. Despite the growing interest in the domain, a limited number of empirical studies exist (Lehto and Oinas-kukkonen 2015). However, one of the main chal- lenges of persuasive systems is how they are designed to engage its users (Wiafe, Nakata, and Gulliver 2014). 714 I. WIAFE ET AL. This is because most of the existing designs are ad hoc and thus do not follow any design principles (Wiafe and Nakata 2012a, 2012b). Arguably, ASNS can be classified as persuasive sys- tems since the intent of the designer is to provide a pri- mary support (Oinas-Kukkonen and Harjumaa 2009; Lehto and Oinas-kukkonen 2015) for academics and researchers to facilitate research work through social interactions. It is evident that ASNS exhibit some level of persuasive activities. Particularly, it depends on social influence, which is part of Perceived Social Support (explained later) from other academics and researchers to promote knowledge sharing. Yet, much is not known about how the persuasive features on these sites facili- tate continuous use. The study therefore uses a model proposed by Lehto and Oinas-kukkonen (2015) to investigate how persuasive features of ASNS impact its use continuance. 3.1. Construct definitions As mentioned in the previous section, this study adopted Lehto and Oinas-kukkonen’s (2015) model. The constructs in the model have been proven to be effective for studying persuasive features of systems. It has also been successfully used to analyze Perceived Per- suasiveness of a student Enterprise Resource Planning (ERP) system in a Higher Educational Institution (Dabi et al. 2018). Figure 1 is a diagrammatic represen- tation of the adopted research model. The model con- sists of Computer–Human Dialogue Support (DIAL), Social Identification (SOID), Perceived Social Support (SOCS), Primary Task Support (PRIM), Perceived Credibility (CRED), Perceived Effort (EFFO), Perceived Effectiveness (EFFE) and Continuance Use Intention (CONT). Table 1 presents the various constructs and their definitions from literature. 3.2. Hypothesis formulation 3.2.1. Social identification (SOID) Social Identification is an individual’s sense of belonging to a community. It is the perception of self-inclusiveness in a group (Fujita, Harrigan, and Soutar 2018; Bagozzi and Dholakia 2002). The Social Identity theory (Tajfel and Turner 1986) argues that people categorise them- selves into social groups and through group actions, they perceive themselves as members in a group. Conse- quently, this perception creates a norm where members treat each other as allies (Fujita, Harrigan, and Soutar 2018; Hsu and Lin 2008). According to Ellemers, Korte- kaas, and Jaap (1999), identification fosters loyalty and faithfulness in a group. Thus, group recognition facili- tates the development of sense of attachment and affec- tive support in groups. Jeng et al. (2012) argued that the existence of affective ties between members of groups on Figure 1. Research model. BEHAVIOUR & INFORMATION TECHNOLOGY 715 ASNS facilitate the creation of mutual devotion towards groups. Thus, it is hypothesised that: H1a: Social Identification positively influences Per- ceived Social Support on Academic Social Networking Sites. 3.2.2. Perceived social support (SOCS) The social cognitive theory (Bandura 1989) asserts that a person’s behaviour is partly shaped and influenced by their personal cognition (e.g. expectations) and influ- ences from social systems. This indicates that the sup- port, encouragement and motivation an individual receives from other social actors influence their behav- iour (Hwang et al. 2010). Dabi et al. (2018) added that the reciprocal exchange of resources between parties with the aim of improving the well-being of recipients also defines social support. Such reciprocal exchanges increase mutual support among group members (Tseng and Kuo 2014). Yet, there is a significant relationship between anticipated reciprocal relationship and intention to participate in online communities (Bock et al. 2005; Hamari and Koivisto 2013; Mafukata, Dhlandhlara, and Kancheya 2017). This intention is built on the presumption that users perceive systems as useful when there is exchange in Perceived Social Support (Lehto and Oinas-kukkonen 2015). Therefore: H1b: Perceived Social Support positively influences Perceived Effectiveness of Academic Social Networking Sites H1c: Perceived Social Support positively influences continuance use intention of Academic Social Net- working Sites 3.2.3. Computer–human dialogue support (DIAL) Computer–Human Dialogue Support defines the key principles targeted at keeping users active and motiv- ated when using a system. It support users to reach their intended behaviour (Oinas-Kukkonen and Harju- maa 2009; Lehto and Oinas-kukkonen 2015). Infor- mation Systems are social actors and as such people perceive their interaction with them to be similar to other social situations (Lee 2009; Nass and Moon 2000; Oyibo, Orji, and Vassileva 2017). It is therefore imperative to provide measures that support inter- actions between users and information systems (Oinas-Kukkonen and Harjumaa 2009). Computer– Human Dialogue Support may occur via system-to- user prompts, notifications, reminders and positive feedback from the system to the user. Thus, they facili- tate how users perceive social support. Accordingly, it is hypothesised that: H2a: Computer- Human Dialogue Support positively influences Perceived Social Support on Academic Social Networking Sites. Computer–Human Dialogue support may be further enhanced, for example by virtually notifying users of their accomplishments. This increases users’ positive attitude and encourages them to use these systems in performing their primary task (Lehto and Oinas-Kuk- konen 2011). Hence, Computer–Human Dialogue Sup- port promotes the performance of primary task. It is therefore hypothesised that: H2b: Computer–Human Dialogue Support positively influences Perceived Effectiveness of Academic Social Networking Sites H2c: Computer–Human Dialogue Support positively influences Primary Task Support of Academic Social Networking Sites Again, when members of a network are able to demonstrate their benevolence and integrity, it informs other members of their trustworthiness (Evans, Wens- ley, and Frissen 2015). Accordingly, online networks enable users to showcase their abilities and veracities. These characteristics are common on ASNS (Barbour and Marshall 2012), and they make users perceive the information on these sites as credible. Particularly, the provision of positive feedback and affective messages influence user confidence (Kahn and Isen 1993). Jeng et al. (2012) argued that affective ties on ASNS increases members’ commitment. Hence, Computer–Human Dialogue Support will influence a user’s confidence in ASNS. It is therefore hypothesised that: H2d: Computer–Human Dialogue Support positively influences Perceived Credibility of Academic Social Networking Sites 3.2.4. Primary task support (PRIM) System characteristics have been found to affect its effectiveness (Koranteng et al. 2019). Arguably, the most important feature of a system that enhances its persuasive experience is Primary Task Support (Lehto, Oinas-Kukkonen, and Drozd 2012). Primary Task Sup- port is the medium made available by a system to enable a user perform his or her objective task (Oinas-Kukko- nen and Harjumaa 2009). It is relatively synonymous to cognitive fit (Vessey and Galletta 1991), task-technology fit (Goodhue and Thompson 1995) and person-artefact- task fit (Finneran and Zhang 2003). In essence it aims at increasing the self-efficacy of users while reducing the cognitive burden of system use (Lehto, Oinas-Kukko- nen, and Drozd 2012; Koranteng, Sarsah, Kuada & Gyamfi 2020; Webster and Ahuja 2006). It also increases 716 I. WIAFE ET AL. positive affect (Derrick, Jenkins, and Nunamaker Jr 2011) which augments the persuasiveness of the source (Angst and Agarwal 2009). However, prior studies have established a relationship between perceived persuasive- ness and Perceived Effectiveness (Shang and Seddon 2000). Therefore, in this study, it is hypothesised that: H3a: Primary Task Support positively influences Per- ceived Effectiveness of Academic Social Networking Sites H3b: Primary Task Support positively influences Per- ceived Effort of Academic Social Networking Sites 3.2.5. Perceived credibility (CRED) In information systems, trust and credibility are major predictors of continuance use (Everard and Galleta 2006; Shin, Ahn, and Kim 2013). For a system to be deemed credible, it must build trust in users. Thus, Per- ceived Credibility affects the believability of a system (Lehto, Oinas-Kukkonen, and Drozd 2012). It is strengthened by recommendations from renowned sources as well as the subjective judgements of users after their first interaction with the system (Drozd, Lehto, and Oinas-Kukkonen 2012). Since, interactions on ASNS replicates the normal educational structure and enable users to build credible professional repu- tation (Barbour and Marshall 2012), users are inclined to believe the information on these sites. Also, ASNS employ various levels of security features which reduces users’ privacy concerns and trust (Rauniar et al. 2014). Although some users emphasise security and privacy concerns on social media (Koranteng et al. 2019; Rau- niar et al. 2014), studies have confirmed a significant relationship between system credibility and intention to use (Dwyer, Hiltz, and Passerini 2007; Rauniar et al. 2014). It is therefore hypothesised that: H4: Perceived Credibility positively influences Con- tinuance Use Intention of Academic Social Networking Sites. 3.2.6. Perceived effort (EFFO) Perceived effort is the degree of ease associated with the use of a system (Venkatesh et al. 2003): in this case ASNS. Two of the most used technology acceptance the- ories; Technology Acceptance Model (Davis 1989) and Unified Theory of Acceptance and Use of Technology (Venkatesh et al. 2003) have demonstrated that per- ceived effort significantly impacts one’s conception to use a system for a desired goal. Arguably, most ASNS users may be familiar with generic social networking sites and perceived them to be easy to use (Paliktzoglou and Suhonen 2014; Sun et al. 2014). It is expected that ASNS will also be perceived to be easy to use. Thus, it is hypothesised that: H5a: Perceived Effort positively influences Perceived Effectiveness of Academic Social Networking Sites H5b: Perceived Effort positively influences Continuance Use Intention of Academic Social Networking Sites 3.2.7. Perceived effectiveness (EFFE) According to Venkatesh et al. (2003) perceived effective- ness is closely related to performance expectancy and it is a strong predictor of Intention to Use. It conceptualises users’ perceptions as to whether the system is useful for performing a specific task (Wiafe, Koranteng, Tettey, Kas- triku & Abdulai, 2019). Thus, it measures users’ opinion about the successful use of ASNS for research and scho- larly activities. ASNS are useful because they enable users to connect with others and share scholarly infor- mation (Kwon and Wen 2010). In particular, they facili- tates the formation and maintenance of new and existing relationships (Bardakci, Arslan, and Ünver 2017; Curry, Kiddle, and Simmonds 2009). They also enable users to build valid academic reputation and demonstrate their research ability (Barbour and Marshall 2012). As men- tioned earlier, there is a relationship between effectiveness of generic social networking sites and user’s intention to use (Kwon and Wen 2010; Lin and Lu 2011b). To confirm these findings on ASNS, it is hypothesised that: H6: Perceived Effectiveness positively influences Con- tinuance Use Intention of Academic Social Networking Sites. 4. Research methodology An English questionnaire was developed using Google Forms and links were sent via emails, WhatsApp, Face- book, etc. Convenience sampling was used to recruit participants. Participation was purely voluntary. The cover page of the questionnaire contained a short letter that briefed respondents about the purpose of the sur- vey. The questionnaire sought to gather respondents’ demographics and their perceptions on the various fac- tors discussed. Their perceptions were measured using a five-point Likert scale. All the constructs were adopted from prior studies (as shown in Table 1). To ensure con- sistency and reliability of responses, a minimum of 3 questions were used to measure perception and attitude related questions. The questions were carefully designed to ensure anonymity and confidentiality. The question- naire was pretested with 10 respondents (these respon- dents were excluded from the main study). The Cronbach’s Alpha and Composite Reliability of all BEHAVIOUR & INFORMATION TECHNOLOGY 717 items recorded during the pretest phase were greater than 0.7. See appendix 1 for question items. A total of 418 responses were received after 3 months of administering the questionnaire. It was sent to 613 candidates. This suggests an effective response rate of 68.2%. A non-response bias test was performed by com- paring responses of the first 25% early responses to the 75% late responses. The findings indicated no signifi- cant difference between the two groups (see appendix 3). Only respondents who have subscribed to at least one ASNS (i.e. Academia.edu; ResearchGate; Mendeley or Kudos) were allowed to complete the entire question- naire and their responses were subsequently included in the analysis. All questions were mandatory. Majority (82.1%) of the respondents were males. Table 2 shows a summary of descriptive statistics of respondents. 5. Analysis and findings The proposed relationships between the latent variables were analyzed using Partial Least Square Structural Equation Modelling (PLS-SEM). PLS-SEM is ideal for studies that seek to predict relationships between con- structs (Hair et al. 2016). Moreover, it provides potent techniques for evaluating non-normally distributed samples (Gefen, Rigdon, and Straub 2011). It requires that the sample size should be at least ten times larger than the number of structural paths directed at a target construct in a structural model. SmartPLS 3.0 was used for the analysis. 5.1. Measurement Missing values check, and normality test were also per- formed to ensure that the data is normally distributed. The values of the skewness and kurtosis is presented in appendix 2. The results indicate that there were no missing data and hence treatments of missing values were not required. The research model consisted of eight (8) constructs and twenty-seven item indicators. The PLS analysis indicated that kurtosis values ranged from −0.884 (CONT2) to 3.253 (EFFO4) and Skewness ranged from −0.35 (SOCS3) to 1.233 (PRIM1). This conforms to Kline (2005) assertion for normalised data: kurtosis and skewness values should be lesser than 10 and 3 respectively. All item loadings met the reliability threshold of 0.7 as required (Barclay, Higgins, and Thompson 1995). Details of item loadings is attached in appendix 1. Internal consistency was measured with Cronbach’s Alpha and Composite Reliability. The results (as shown in Table 3) indicate that all constructs were valid (with values above 0.7). An evaluation of the con- vergent validity confirmed that Average Variance Extracted (AVE) of all constructs were above 0.5 as required (Wixom and Watson 2001). Furthermore, dis- criminant validity was evaluated with both Fornell and Larcker (1981) criterion and Heterotrait-Monotrait Ratio Test. Using the Fornell and Larcker (1981) cri- terion, the square root of AVE of latent variables were matched against correlations with other latent variables. The highlighted diagonal elements in Table 3 indicate that the square roots of AVEs of the latent variables were greater than the correlations with other latent vari- ables as required. Table 1. Definition of constructs. Construct Definition Sources Social Identification (SOID) The degree to which ASNS provide a means for users to identify themselves with other system users who share characteristics and common interest. Lehto and Oinas- kukkonen (2015), Aslam et al. (2013), Ma and Agarwal (2007) Perceived Social Support (SOCS) The degree to which ASNS motivate users by leveraging social influence and providing a means for other users to support each other. Lehto and Oinas- kukkonen (2015), Chiu, Hsu, and Wang (2006) Computer-Human Dialogue Support (DIAL) The degree to which ASNS are capable of providing relevant motivating feedback to users. Adaji and Vassileva (2016a), Lehto and Oinas-kukkonen (2015), Fogg and Nass (1997) Primary Task Support (PRIM) The degree to which ASNS provide support for the main goal or task of the individual user. Complex tasks are reduced into smaller subtasks. (Dabi et al. 2018; Lehto and Oinas-kukkonen 2015; Goodhue and Thompson 1995) Perceived Credibility (CRED), The degree to which ASNS promote trust, believability, reliability and credibility. Lehto and Oinas- kukkonen (2015), McKnight, Choudury, and Kacmar (2002) Perceived Effort (EFFO) The degree of ease associated with the use of ASNS. Dabi et al. (2018), Lehto and Oinas-kukkonen (2015) Perceived Effectiveness (EFFE) The degree to which using ASNS provide benefits to the user in relation to the primary task. Dabi et al. (2018), Lehto and Oinas-kukkonen (2015) Continuance Use Intention (CONT) The degree to which users intend to continue using ASNS. Lehto and Oinas- kukkonen (2015), Bhattacherjee (2001), De Guinea and Markus (2009) Table 2. Demographics of respondents (N = 418). Demographics Value Frequency Percentage Sex Male 343 82.1% Female 75 17.9% Age Below 30 308 73.7% 30–40 92 22.1% Above 40 18 4.2% Highest education level Postgraduate 334 80% Undergraduate 84 20% 718 I. WIAFE ET AL. As already indicated, discriminant validity was also assessed using Heterotrait-Monotrait Ratio (HTMT). The values in Table 4 suggests that the required maxi- mum threshold of 0.85 was met for all constructs (Clark and Watson 1995). The possibility of multicollinearity was also evaluated with Variance Inflation Factor (VIF). The results (as shown in Table 5) indicated that all values were below 3 as required by Hair et al. (2016). Thus, col- linearity did not influence the outcome of the study. 5.2. Structural model Figure 2 shows the analysis of the hypothesised model. The bootstrapping (5000 samples) technique was adopted to test the significance of path coefficients and variances of the model. This is because Hair et al. (2016) recommend the bootstrapping technique for sample size greater than 100. Path coefficients are con- sidered significant if the p-values are less than 0.05. All the constructs were modelled as reflective and tested with multiple indicators. The findings indicate that Computer–Human Dialo- gue Support and Social Identification explained 40.2% of the variance in Social Support. Computer–Human Dialogue Support also accounted for 39.4% and 9.6% of the variances in Primary Task Support and Perceived Credibility respectively. Primary Task Support also explained 9.2% of the variance in Perceived Effort. Together, Social Support, Computer–Human Dialogue Support, Primary Task Support and Perceived Effort accounted for 39.3% of the variance in Perceived Effec- tiveness. Social Support, Perceived Effectiveness, Per- ceived Effort and Perceived Credibility jointly explained 17.1% of the variance in Continuance Use Intention. Table 6 presents a summary of significant relationships observed from the study. Perceived Credibility was the strongest predictor of Continuance Use Intention (p-value: 0.000). In contrast, Perceived Effectiveness, Perceived Effort and Perceived Social Support were not significant predictors of Con- tinuance Use Intention with p-values; 0.308, 0.181 and 0.088 respectively. Computer–Human Dialogue Sup- port strongly predicted Perceived Credibility (p-value: 0.000), Primary Task Support (p-value: 0.000) and Per- ceived Effectiveness (p-value: 0.035). The relationship between Computer–Human Dialogue Support and Per- ceived Social Support was not statistically significant (p- value: 0.077). Similarly, there was no significant relationship between Perceived Effort and Perceived Effectiveness (p-value: 0.048). However, Perceived Social Support (p-value: 0.001) and Primary Task Sup- port (p-value: 0.441) had a strong effect on Perceived Effectiveness. Finally, there was a strong relationship between Social Identification and Perceived Social Sup- port (p-value: 0.000). The total effects and effect sizes (Cohen’s f 2) were also studied (see Table 7). Stone-Geisser (Q2) predictive relevance was also analyzed since they are more reliable than R2 (Lehto, Oinas-Kukkonen, and Drozd 2012). The relationship between two constructs is considered valid when Q2 is greater than zero. From Table 7, Q2 values for all relationships were greater than zero thus confi- rming their validity. However, effect size (Cohen’s f2) determine whether the effect is irrelevant (<0.02); small (0.02); medium (0.15) or large (0.35) (Cohen 2013). Table 7 also indicates that the effect of Perceived Effort and Perceived Social Support on Perceived Table 3. Construct validity and reliability. CA CR AVE CONT CRED DIAL EFFE EFFO PRIM SOCS SOID CONT 0.835 0.923 0.857 0.926 CRED 0.882 0.927 0.808 0.380 0.899 DIAL 0.710 0.838 0.634 −0.041 0.310 0.796 EFFE 0.824 0.883 0.653 0.010 0.344 0.480 0.808 EFFO 0.817 0.877 0.641 −0.019 0.184 0.263 0.297 0.801 PRIM 0.773 0.855 0.598 0.113 0.503 0.542 0.514 0.303 0.773 SOCS 0.775 0.816 0.597 0.102 0.232 0.282 0.475 0.179 0.529 0.773 SOID 0.784 0.822 0.607 0.138 0.335 0.359 0.523 0.300 0.496 0.631 0.779 Note: CA, Cronbach’s Alpha; CR, Composite Reliability; AVE, Average Variance Extracted. Table 4. Discriminant validity test with Heterotrait-Monotrait Ratio (HTMT). CONT CRED DIAL EFFE EFFO PRIM SOCS CRED 0.432 DIAL 0.244 0.387 EFFE 0.092 0.417 0.607 EFFO 0.102 0.232 0.323 0.331 PRIM 0.139 0.602 0.727 0.613 0.362 SOCS 0.247 0.339 0.406 0.609 0.251 0.747 SOID 0.199 0.445 0.522 0.668 0.414 0.697 0.801 Table 5. Collinearity testing with variance inflation factor (VIF). CONT CRED DIAL EFFE EFFO PRIM SOCS CRED 1.151 DIAL 1.000 1.438 1.000 1.148 EFFE 1.462 EFFO 1.107 1.119 PRIM 1.862 1.000 SOCS 1.302 1.389 SOID 1.148 BEHAVIOUR & INFORMATION TECHNOLOGY 719 Effectiveness were less than 0.02 hence irrelevant. Per- ceived Effectiveness had a small effect (0.021) while Per- ceived Credibility moderately (0.191) influenced Continuance Use Intention. The strongest relation was between Social Identification and Perceived Social Sup- port with effect size 0.539. 6. Discussion The use of academic social networking sites is rapidly gaining acceptance in higher educational institutions (Bardakci, Arslan, and Ünver 2017). This is because they facilitate knowledge sharing and collaboration among researchers (Koranteng and Wiafe 2019; Oh and Jeng 2011). As explained earlier, ASNS have the ability to link distances, and boost cross-disciplinary and cross-border collaborations. This is similar to inter- actions academics experience at conferences and other academic gatherings. They also facilitate the creation and expansion of academic professional networks. The present study adopted constructs from a model pro- posed by Lehto and Oinas-kukkonen (2015) to explain Figure 2. Analysis of research model. p*** < 0.005; p** < 0.05; pn.s-non-significant. Table 6. Significance of path coefficients. Proposed relationships Original sample (O) T statistics (|O/ STDEV|) P values Supported H1a: Social Identification positively influences Perceived Social Support on Academic Social Networking Sites. 0.516 4.988 0.000 Yes H1b: Perceived Social Support positively influences Perceived Effectiveness of Academic Social Networking Sites 0.300 3.091 0.001 Yes H1c: Perceived Social Support positively influences continuance use intention of Academic Social Networking Sites 0.151 1.355 0.088 No H2a: Computer- Human Dialogue Support positively influences Perceived Social Support on Academic Social Networking Sites. 0.131 1.426 0.077 No H2b: Computer-Human Dialogue Support positively influences Perceived Effectiveness of Academic Social Networking Sites 0.247 1.816 0.035 Yes H2c: Computer-Human Dialogue Support positively influences Primary Task Support of Academic Social Networking Sites 0.555 6.875 0.000 Yes H2d: Computer-Human Dialogue Support positively influences Perceived Credibility of Academic Social Networking 0.324 3.357 0.000 Yes H3a: Primary Task Support positively influences Perceived Effectiveness of Academic Social Networking Sites 0.217 1.665 0.048 Yes H3b: Primary Task Support positively influences Perceived Effort of Academic Social Networking Sites 0.414 5.085 0.000 Yes H4: Perceived Credibility positively influences Continuance Use Intention of Academic Social Networking Sites. 0.466 5.636 0.000 Yes H5a: Perceived Effort positively influences Perceived Effectiveness of Academic Social Networking Sites 0.014 0.148 0.441 No H5b: Perceived Effort positively influences Continuance Use Intention of Academic Social Networking Sites −0.100 0.910 0.181 No H6: Perceived Effectiveness positively influences Continuance Use Intention of Academic Social Networking Sites. −0.071 0.502 0.308 No 720 I. WIAFE ET AL. and predict factors that impact Continuance Use Inten- tion of ASNS. The results validated some of the hypoth- esised relationships and refuted others. Below is a discussion on the implications of the findings. 6.1. Implications of significant hypothesis Computer–Human Dialogue Support significantly affects Primary Task Support, Perceived Effectiveness, and Perceived Credibility. This is supported by Lehto and Oinas-kukkonen (2015). The findings suggest that users of ASNS perceive their interactions with compu- ters as similar to human-human interactions. Thus, effective dialogue support influences users’ perceptions and behaviour. ASNS such as ResearchGate actively engage users to improve their research impact via notifi- cations and alerts on user rating and profile updates. Hence, users are mostly motivated to performing research tasks on such platforms. This proves that users are more capable of performing their primary tasks (i.e. research work) when their interactions with the system is optimised. Moreover, the provision of effective dialogue principles creates the perception that ASNS are effective and credible. Computer–Human Dialogue Support features on ASNS also facilitate inter- actions between peers and colleagues. Research indi- cates that possible interactions between parties influence perceptions of trustworthiness (Koranteng et al. 2019). As explained earlier, majority of inter- actions on ASNS are between members who are sup- porting each other’s academic and research work. Within ASNS, credibility increases as Computer– Human dialogue increases. However, as discussed pre- viously, dialogue in ASNS are skewed towards computer mediation. Users perceive ASNS to be credible although they interact with humans (their peers). Accordingly, designers of ASNS may need to implement features that support Computer–Human dialogue. The pro- vision of some level of artificial intelligence into ASNS can serve as a motivator to increase credibility. In par- ticular, if such features are capable of providing accurate responses to challenging questions in the user’s domain of research. Social Identification was observed to significantly impact Perceived Social Support. This indicates that the design of ASNS enable users to connect with peers and colleagues with similar experiences. For example, based on a user’s profile, ResearchGate, Mendeley and Kudos are able to suggest projects, available funding and scholarships from other researchers, which are rel- evant to a user’s domain. By this means, researchers are able to identify and join relevant groups. As argued by Oinas-Kukkonen and Harjumaa (2009), members’ inclusiveness in group activities increases when there is a common objective. Thus, users will support each other by sharing ideas and brainstorming on complex concepts when they perceive themselves as part of a group. An individual’s perception of inclusiveness and mutual support fosters faithfulness and commitment towards groups (Jeng et al. 2012). It was also observed that Primary Task Support had a significant impact on Perceived Effectiveness and Per- ceived Effort. Thus, the provision of support for the main task or the reduction of complex task into smaller subtasks provides benefits to users of ASNS. Moreover, this provision eases the research activity. Primary Task Support describes the medium enabled by the system to support a user to complete a task. Accordingly, the Table 7. Total effect (Stone-Geisser Q2) with effects size (Cohen’s f2). Continuance use intention Perceived credibility Computer- human dialogue support Perceived effectiveness Perceived effort Primary task support Perceived social support Social identification Q2 Continuance use intention 0.137 Perceived credibility 0.427 (0.191) 0.081 Computer- human dialogue support 0.310 (0.106) 0.407 (0.084) 0.542 (0.416) 0.063 (0.006) Perceived effectiveness −0.160 (0.021) 0.202 Perceived effort −0.086 (0.005) 0.120 (0.021) 0.088 Primary task support 0.220 (0.030) 0.303 (0.101) 0.168 Perceived social support 0.046 (0.008) 0.280 (0.093) 0.136 Social identification 0.609 (0.539) Note: Total effects (effect size). BEHAVIOUR & INFORMATION TECHNOLOGY 721 provision of features that enable a user to perform the basic objectives or requirements lead to the perception that a system is easier to use. Academics use ASNS to augment existing relationships as well as facilitate inter- actions among colleagues. Consequently, they consider ASNS as effective platforms for conducting research. Several research works are made available on ASNS and academics do consider this as an effective approach for searching scholarly articles. ASNS provide accessi- bility to research articles and also initiate collaborations and discuss research findings. Arguably, discussions on research findings, ideas and challenging concepts are more informative on ASNS platforms when compared to physical academic gatherings. This is because phys- ical gatherings are limited to time and audience. How- ever, this limitation is not present on ASNS platforms. In particular, the lack of face-to-face discussions, enables new and novice researchers to confidently pre- sent their ideas and contributions on these platforms. This eases the burden of academics in their quest to aggregate knowledge. The findings validated the hypothesis that Perceived Social Support significantly affected Perceived Effective- ness and support earlier studies by Bagozzi and Dhola- kia (2002) and Kankanhalli, Tan, and Wei (2005). The findings re-echoe the existence of affective ties and mutual support on ASNS (Jeng et al. 2012) and their influences on an individual’s behaviour (Hwang et al. 2010). Reciprocal exchanges such as encouragement and motivation positively affect the well-being of group members (Dabi et al. 2018). Consequently, it can be inferred that users will perceive ASNS as effective for their activities when they observe that other mem- bers take concern and support each other towards a common goal. Although designers of ASNS cannot influence social support directly, they must provide fea- tures that will influence social support indirectly. They must encourage those who only consume information from these sites but do not contribute. Among the proposed antecedents of Continuance Use Intention (i.e. Perceived Social Support, Perceived Effectiveness, Perceived Effort and Perceived Credi- bility), only Perceived Credibility was observed to posi- tively impact Continuance Use Intention. Academics find ASNS as credible for academic activities and ASNS provide more effective methods for managing and verifying user profiles and also the content they post. They emulate normal education structure and reduce users’ privacy concerns, thus influencing inten- tion to continuously use them. The presence of other researchers of high reputation, and relevance of discus- sions that are characterised with these sites play a major role on users’ perception on the systems credibility and continuous usage. Considering this, designers of ASNS must ensure that their systems demonstrate factors that users will consider to be credible. Accordingly, designers of ASNS who seek to ensure continuous usage of their websites should provide features that sup- port credibility. 6.2. Implications of non-significant hypothesis Contrary to existing knowledge, Perceived Social Sup- port, Perceived Effectiveness and Perceived Effort did not significantly impact Continuance Use Intention. Thus, although these constructs may predict Continu- ance Use Intention in traditional persuasive systems (Lehto and Oinas-kukkonen 2015), the same cannot be confirmed on ASNS. Academic social networking sites mimic the functionalities on generic SNS, as such comprise features that academics are already familiar with. This makes it easier to use. Wu and Chen (2017) argued that such situations lessen the influence of some features on Continuance Use Intentions. This might have accounted for the insignificant influence of Perceived Effort on Continuance Use Intention. Again, users’ continuous intention to use a system is strongly dependent on the confirmation of their expec- tations of its effectiveness (Al-Emran, Arpaci, and Sal- loum 2020). However, although ASNS have similar characteristics as generic SNS, ASNS incorporate additional mechanisms which promote academic- specific information exchange (Ovadia 2013). Based on this, it is possible academics’ expectations toward the effectiveness of ASNS exceed their experiences on ASNS platforms. Perceived Social Support did not impact Continu- ance Use Intention. This is contrary to existing knowl- edge. A key aim of ASNS is to foster sharing of academic resources to promote knowledge acquisition and dissemination. Thus, although current features on ASNS that are designed to foster social support is pro- moting users’ perception on system effectiveness, they do not promote continuous use. Ideally, individuals are motivated when they are recognised for their impact and contribution (Danish and Usman 2010). Social sup- port may be considered as a bidirectional (giving and taking support) or a unidirectional (either giving sup- port only or taking support only). In this study, social support was considered to be a reciprocal event (i.e. bidirectional). Thus, users who provide support but do not receive support, may not perceive social support as a feature that promotes continuous use. In addition, when social support backfires, they introduce unnecess- ary stress, tension and anxiety (Orji et al. 2019) and this discourages system use intentions. More importantly, 722 I. WIAFE ET AL. this finding provokes the need for further studies to ascertain the casual effect of this observation. Particu- larly, studies have found that the provision of features that enable support from other academics and research- ers should motivate users’ positive intentions (Wiafe et al. 2020). Perhaps, there are confounding variables that serve as moderators which have not been identified in the case of ASNS. Findings on the relationship between Computer– Human Dialogue Support and Perceived Social Support were contrary to existing knowledge. Most communi- cation methods for most ASNS (e.g. ResearchGate) are text-based. Generic SNS platforms, however, use mul- tiple channels and methods for promoting dialogue. Specifically, they include the use of videos and audios: these are seldomly used on ASNS platforms. Replicating human to human social support on computing devices using on text is challenging. As such, it limits the amount of social support ASNS provides to its users. For instance, YouTube effectively facilitates social sup- port (Frohlich and Zmyslinski-Seelig 2012). The visual details it provides to users through videos, audios, emo- jis and animoji enhance user’s perception on social sup- port. This therefore suggests that existing methods available on ASNS that seek to promote or facilitate dia- logue is inadequate to promote social support. Accord- ingly, there is the need to incorporate other forms of dialogue on ASNS platforms. 7. Conclusion Although existing research has discussed factors that impact Continuance Use Intention, this study is novel because it sought to investigate how persuasive features embedded in Academic Social Networking Sites promote Continuance Use Intention. The findings confirmed some existing known relationships and refuted others. It is acknowledged that conven- ience sampling was used for the study, hence the findings cannot be generalised to all ASNS. Existing studies have argued that the main antecedents of Con- tinuous Use Intention include Perceived Social Sup- port, Perceived Effort and Perceived Effectiveness (Adaji and Vassileva 2017; Lehto, Oinas-Kukkonen, and Drozd 2012; Lehto and Oinas-kukkonen 2015). However, findings from this study proved otherwise. None of these significantly predicted Continuance Use Intention. It is however, acknowledged that exist- ing research investigated users of a single or different system as compared to this study that sought to inves- tigate a collection of systems (selected academic social networking sites). Thus, there is the need for further studies to be conducted to investigate why some of these relationships did not hold on academic social networks sites. In addition, whereas Primary Task Support had a sig- nificant impact on Perceived Effort, it failed to affect Perceived Effectiveness. Computer–Human Dialogue Support on the other hand, did not influence Perceived Social Support, yet it confirmed that the provision of dialogue support features in ASNS shall impact users’ perception on the effectiveness of the system. Consider- ing that ASNS platforms mostly focus on computer- mediated dialogue rather than Computer–Human Dia- logue Support, there is the need for further investi- gations on how computer-mediated dialogue impacts Perceived Social Support: this study did not address that. It would be intriguing if further studies can inves- tigate whether users of ASNS perceive computer- mediated dialogue as human–computer dialogue. In addition, it can be observed that the sample used for the study was skewed in terms of sex. That is 82.1% of males. Due to this analyzing the relationships among the various construct in terms of their sexes may not provide non-trivial findings. Consequently, there is the need for future studies to examine how the relationships vary between the different sexes using a non-skewed sample. Disclosure statement No potential conflict of interest was reported by the author(s). ORCID Isaac Wiafe http://orcid.org/0000-0003-1149-3309 References Abrahim, S., B. A. Mir, H. 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Question items and loadings Construct Items Question items Loading Source (Adapted) Computer-Human Dialogue Support DIAL1 The academic social networking site that I use motivates me to perform my daily activities. 0.827 Adaji and Vassileva (2016a), Fogg and Nass (1997) DIAL2 The academic social networking site that I use provides me with right feedback on the task I perform. 0.804 DIAL3 The academic social networking site that I use provides messages of praise when I complete a task. 0.755 Primary Task Support PRIM1 Academic social networking sites supports me in my daily activities. 0.703 Dabi et al. (2018), Goodhue and Thompson (1995) PRIM2 With the help of academic social networking sites, I am able to complete my daily activities. 0.833 PRIM3 It is easier to use a academic social networking site to support you to do your daily activities. 0.832 PRIM4 Academic social networking sites makes life easier. 0.715 Perceived Credibility CRED1 I trust all the information I receive on my academic social networking site. 0.895 Lehto and Oinas-kukkonen (2015), McKnight, Choudury, and Kacmar (2002) CRED2 In my opinion the content on the academic social networking site that I use is believable 0.897 CRED3 Overall, I consider the information I receive on my academic social networking site as credible. 0.905 Perceived Social Support SOCS1 Through academic social networking sites, I am able to get support when I need it. 0.806 Chiu, Hsu, and Wang (2006), Lehto and Oinas- kukkonen (2015) SOCS2 I share my experiences with my friends using academic social networking sites. 0.784 SOCS3 I learn from the experiences of my peers on my academic social networking site. 0.725 Social Identification SOID1 Through academic social networking sites, I can relate to the experiences of my colleagues. 0.795 Aslam et al. (2013), Ma and Agarwal (2007) SOID2 My friends on my academic social networking sites are my peers. 0.745 SOID3 I care about my friends on my academic social networking site. 0.796 Perceived Effort EFFO1 Learning to use academic social networking site is easy. 0.809 Venkatesh et al. (2003) EFFO2 Using academic social networking sites do not require a lot of effort. 0.889 EFFO3 The academic social networking site that I use is flexible to interact with. 0.775 EFFO4 Using academic social networking site is not difficult, 0.720 Perceived Effectiveness EFFE1 Using academic social networking sites enable me to learn effectively. 0.830 Venkatesh et al. (2003) EFFE2 Using academic social networking sites encourages me to learn. 0.857 EFFE3 Using academic social networking sites increases my effort towards challenging issues 0.811 EFFE4 In my opinion academic social networking site is helpful 0.729 Continuance Intention CONT1 I am considering to stop using academic social networking site. 0.946 Bhattacherjee (2001), De Guinea and Markus (2009) CONT2 I will prefer to use a different system that is not a academic social networking site. 0.905 Appendix 2. Data distribution Missing Standard deviation Excess Kurtosis Skewness DIAL1 0 0.924 0.604 1.008 DIAL2 0 0.707 2.331 1.067 DIAL3 0 0.692 0.849 0.755 SOID1 0 0.539 3.221 0.869 SOID2 0 0.747 −0.031 0.543 SOID3 0 0.669 0.233 0.276 SOCS1 0 0.732 1.812 0.99 SOCS2 0 0.801 2.106 1.21 SOCS3 0 0.461 1.517 −0.35 PRIM1 0 0.722 1.779 1.233 PRIM2 0 0.794 −0.26 0.612 PRIM3 0 0.792 −0.12 0.452 PRIM4 0 0.763 0.068 0.424 CRED1 0 1.035 −0.802 −0.031 (Continued ) BEHAVIOUR & INFORMATION TECHNOLOGY 729 Continued. Missing Standard deviation Excess Kurtosis Skewness CRED2 0 0.846 −0.844 0.248 CRED3 0 0.897 −0.127 0.372 EFFO1 0 0.571 2.691 0.688 EFFO2 0 0.714 1.151 0.77 EFFO3 0 0.563 1.698 0.338 EFFO4 0 0.602 3.253 0.913 EFFE1 0 0.778 −0.312 0.228 EFFE2 0 0.83 −0.128 0.543 EFFE3 0 0.78 0.957 0.891 EFFE4 0 0.639 0.196 0.2 CONT1 0 0.553 0.142 −0.051 CONT2 0 1.185 −0.884 0.294 Appendix 3. Non-bias response test Sample mean (M) Group 1 (n = 104) Sample mean (M) Group 2 (n = 314) Standard deviation (STDEV) Group 1 (n = 104) Standard deviation (STDEV) Group 2 (n = 314) T statistics (|O/ STDEV|) Group 1 (n = 104) T statistics (|O/ STDEV|) Group 2 (n = 314) P values Group 1 (n = 104) P values Group 2 (n = 314) CRED - > CONT 0.469 0.487 0.091 0.091 5.119 5.124 0.000 0.000 DIAL -> CRED 0.325 0.317 0.104 0.086 3.119 3.767 0.002 0.000 DIAL -> EFFE 0.218 0.253 0.150 0.143 1.645 1.725 0.043 0.045 DIAL -> PRIM 0.552 0.568 0.075 0.067 7.357 8.339 0.000 0.000 DIAL -> SOCS 0.124 0.126 0.105 0.094 1.254 1.390 0.108 0.085 EFFE -> CONT −0.017 −0.083 0.142 0.129 0.499 0.549 0.310 0.293 EFFO -> CONT −0.143 −0.109 0.116 0.117 0.861 0.847 0.197 0.200 EFFO -> EFFE 0.039 0.043 0.088 0.112 0.163 0.128 0.436 0.449 PRIM -> EFFE 0.221 0.193 0.109 0.113 1.980 1.916 0.027 0.031 PRIM -> EFFO 0.438 0.438 0.075 0.087 5.498 4.742 0.000 0.000 SOCS -> CONT 0.121 0.173 0.099 0.098 1.526 1.533 0.067 0.066 SOCS -> EFFE 0.306 0.326 0.084 0.085 3.581 3.536 0.000 0.000 SOID -> SOCS 0.529 0.546 0.083 0.097 6.209 5.337 0.000 0.000 730 I. WIAFE ET AL. Abstract 1. Introduction 2. Related literature 2.1. Use continuance intention of social networking sites 2.2. Academic social networking sites 3. Research model and hypothesis 3.1. Construct definitions 3.2. Hypothesis formulation 3.2.1. Social identification (SOID) 3.2.2. Perceived social support (SOCS) 3.2.3. Computer–human dialogue support (DIAL) 3.2.4. Primary task support (PRIM) 3.2.5. Perceived credibility (CRED) 3.2.6. Perceived effort (EFFO) 3.2.7. Perceived effectiveness (EFFE) 4. Research methodology 5. Analysis and findings 5.1. Measurement 5.2. Structural model 6. Discussion 6.1. Implications of significant hypothesis 6.2. Implications of non-significant hypothesis 7. Conclusion Disclosure statement ORCID References Appendices Appendix 1. Question items and loadings Appendix 2. Data distribution Appendix 3. 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