Journal of Enterprise Information Management Decision-making and satisfaction in campus e-voting: moderating effect of trust in the system Norazah Mohd. Suki, Norbayah Mohd. Suki, Article information: To cite this document: Norazah Mohd. Suki, Norbayah Mohd. Suki, (2017) "Decision-making and satisfaction in campus e- voting: moderating effect of trust in the system", Journal of Enterprise Information Management, Vol. 30 Issue: 6, pp.944-963, https://doi.org/10.1108/JEIM-08-2016-0151 Permanent link to this document: https://doi.org/10.1108/JEIM-08-2016-0151 Downloaded on: 16 March 2018, At: 15:08 (PT) References: this document contains references to 74 other documents. 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Suki Labuan Faculty of International Finance, Universiti Malaysia Sabah, Labuan International Campus (UMSKAL), Labuan, Malaysia, and Norbayah Mohd. Suki Faculty of Computing and Informatics, Universiti Malaysia Sabah, Labuan International Campus (UMSKAL), Labuan, Malaysia Abstract Purpose – The purpose of this paper is to examine the determinants that influence students ’ decision-making and satisfaction in campus e-voting, and to investigate the moderating effect on students ’ decision-making and satisfaction in campus e-voting between students with different levels of trust in the system. Design/methodology/approach – This study employed a quantitative method and applied the use of self-administered questionnaires among university students who have at least experienced once in casting votes electronically in the past year during the campus e-voting period. The data were analysed using partial least square-structural equation modelling (PLS-SEM) approach. Findings – The PLS-SEM approach revealed that voters ’ commitment to vote was the strongest determinant of students ’ decision-making and satisfaction in campus e-voting. Voters ’ high satisfaction with campus e-voting was based on the commitment and requirement of students of the university to vote. Compulsory voting was not a hassle for them in order to achieve campus development and sustainability. A moderation analysis revealed that the relative influence of commitment to vote on students ’ satisfaction in campus e-voting was higher in the group with medium level of trust than among the group with high level of trust. Practical implications – The election commission of the university and the university management should increase students ’ turnout and commitment to vote during campus e-voting by outlining effective marketing strategies, campaigns and promotions across a number of digital platforms, including mobile SNS. They need to ensure that voters can sense their involvement is warranted and will continue to vote electronically in the next campus election. Originality/value – The research yielded an exclusive perspective into students ’ decision-making and satisfaction in campus e-voting. It also uncovered the influence of moderating effect of trust in the system in developing countries which is marginally concealed in the literature. The measurements produced can be used as a research tool for more exploratory and explanatory research related to political marketing among young adult voters. Keywords Satisfaction, Perceived knowledge, Confidence, Information seeking, Commitment to vote, E-voting Paper type Research paper 1. Introduction Campus e-voting, also known as campus electronic voting, is related to the voting activities performed via electronic means to simplify the chores of casting and counting votes and save voters ’ time and energy from long queues and complex voting processes and procedures. E-voting refers to “ casting a ballot via a broader range of electronic telecommunications technology including telephones, cable and satellite television, and computers without internet connection ” (Gibson, 2001, p. 564). In contrast to traditional voting, campus e-voting is a viable, alternative method which allows voters to receive the election results on the day of the election itself, within seconds of its closing or at the end of the election period. Another vital trait of campus e-voting is that all previous election results Journal of Enterprise Information Management Vol. 30 No. 6, 2017 pp. 944-963 © Emerald Publishing Limited 1741-0398 DOI 10.1108/JEIM-08-2016-0151 Received 31 August 2016 Revised 2 February 2017 Accepted 9 February 2017 The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com / 1741-0398.htm 944 JEIM 30,6 Downloaded by Göteborgs Universitet At 15:08 16 March 2018 (PT) are freely available in the voters ’ accounts. Besides, the utilisation of the electronic counting method or remote voting over the internet in campus e-voting also saves time for the university election commissions and administrators during the tabulation process. At the same time, the university election commissions and administrators benefit greatly in terms of reduced cost of printing, mailing and tabulating paper ballots workloads, and the reduction of the involvement of manpower required at the voting sites. With this system, democracy in the selection of the best and most suitable candidate is upheld, as the voters practice the right to make their decisions independently and cast secret votes without any influence from a third party. This contributes into the implementation and acceptance of e-government in different countries (Akhtar Shareef et al. , 2014; Beynon-Davies, 2007; Choudrie et al. , 2005). A considerable number of studies on e-voting have been conducted in the western countries in a broad range of research coverage. For instance, e-voting adoption was studied by Aljarrah et al. (2016) and Schaupp and Carter (2005). In another study, Basirat (2012) conducted a study regarding the national culture of the e-voting system, while Prandini and Ramilli (2012) investigated the e-voting system assessments in accordance to international standards. Besides, security issues faced by e-voting have also been accentuated by previous researchers. For example, a study on public trust in e-voting technology was conducted by Lippert and Ojumu (2008) and strategies in constructing secure elections were the focus of Moynihan (2004) in his research. The coverage of ways to inhibit e-voting threats was examined by Oravec (2005), while the countermeasure of assaults in e-voting was researched by Peng (2011). Apart from that, measures to attract voters and modernisation of e-voting were examined by Pieters and Robert (2007), while Lopez García (2016) conducted a study to examine e-voting schemes. A significant number of scholars in the past have noted that the e-voting system affects voters ’ capability to exercise their right to vote and their willingness to receive the unpretentious election results and outcomes (Aljarrah et al. , 2016; Schaupp and Carter, 2005; Singh and Roy, 2014; Winchester et al. , 2015). However, academicians such as O ’ Cass (2002) and O ’ Cass and Pecotich (2005) noted that there are inadequate amount of studies on the decision-making process of young adult voters ’ . Additionally, further examination into campus e-voting is insufficient in the setting of a developing country. Alomari (2016) and Powell et al. (2012) stressed that there is a need to reconnoitre the extent to which vital aspect such as trust in the internet affect voters ’ intentions to use the e-voting system in the developing countries. For these reasons, the demand for new research to inspect the progression of voters ’ behaviour and decision-making in campus e-voting is warranted, particularly in issues such as perceived knowledge, commitment to vote, involvement, voters ’ confidence, stability, information seeking and satisfaction, besides trust in the system. In line with the aforementioned rationale, this research aims to answer the following research questions: RQ1. What determinants influence students ’ decision-making and satisfaction in campus e-voting? RQ2. Are there moderating effect on students ’ decision-making and satisfaction in campus e-voting between students with different levels of trust in the system? Based on the two questions, the contribution of this research is twofold. First, this research examined the determinants that influence students ’ decision-making and satisfaction in campus e-voting. Second, the moderating effect on students ’ decision-making and satisfaction in campus e-voting between students with different levels of trust in the system was also investigated. The empirical results acquired from this study offered instrumental insights to the election commissions and the university management in the university to apply appropriate policies and procedures to retain existing voters and attract potential 945 Campus e-voting Downloaded by Göteborgs Universitet At 15:08 16 March 2018 (PT) voters by stressing on matters such as perceived knowledge, commitment to vote, involvement, voters ’ confidence, stability, information seeking and satisfaction on campus e-voting. The outline of the remainder of this paper is as follows: the literature review is presented in Section 2, while Section 3 details the methodologies applied. This is followed by Section 4 which presents the data analysis and the discussion is elaborated in Section 5. The final section describes the conclusion and future research directions. 2. Literature review This section reviews the literature on issues pertaining to perceived knowledge, commitment to vote, involvement, voters ’ confidence, stability, information seeking and satisfaction related to e-voting. 2.1 Perceived knowledge Perceived knowledge refers to “ factual knowledge of actors, history and institutions; and conceptual knowledge of theories and belief systems ” (McAllister, 1998, p. 7). With abundant knowledge, voters can cast their votes in strategic and utility-maximisation terms, as acknowledged by Sheppard (2015) who studied compulsory voting and political knowledge. Those who are politically knowledgeable have a better idea on how to obtain any thoughts from information search (Sniderman et al. , 1991). In a study by Singh and Roy (2014) on political knowledge, decision calculus and proximity voting, individuals who are more politically knowledgeable are often involved in a detailed decision process before casting their ballots. Notably, knowledge is an essential component in voting, decision-making and satisfaction (O ’ Cass, 2002; O ’ Cass and Pecotich, 2005). Based on this rationale, the following hypothesis was derived: H1. Perceived knowledge has a significant impact on students ’ satisfaction with campus e-voting. 2.2 Commitment to vote Commitment to vote among users varies between positive and negative feedbacks. This is because voters perceive computer systems may adjust voting records in a way that is unnoticeable by voters or election observers (Anderson and Needham, 1995). In relation to this, Lijphart (1997) identified that mandatory voting may function as an encouragement and education for better turn out. These conclusions were echoed by others such as Mackerras and McAllister (1999), who noted that compulsory voting warrants voters to cast a ballot thoroughly. Indeed, compulsory voting upsurges citizens ’ political knowledge due to incidental knowledge in the process of voting as voters are informed of the necessity to vote (Sheppard, 2015). Hence, the following hypothesis was derived: H2. Commitment to vote has a significant impact on students ’ satisfaction with campus e-voting. 2.3 Involvement Involvement is defined as “ the personal relevance of an object based on inherent needs, values, and interests ” (Zaichkowsky, 1985, p. 342). It is equal to importance, interest, attachment and/or motivation expressed towards an object (Laroche et al. , 2003; Rothschild and Houston, 1980). It is categorised by low and high involvement and reliant on how the voter construes the circumstances (Antil, 1984). According to Burton and Netemeyer (1992) and Henn and Foard (2012), low involvement is related to low political knowledge and it may 946 JEIM 30,6 Downloaded by Göteborgs Universitet At 15:08 16 March 2018 (PT) be more appropriate to voting when compared to high involvement (Ben-Ur, 2007). Low involvement is not related to the information that is not relevant to the argument when making judgement on the trustworthiness of e-voting (Mitra et al. , 2008). Besides, Burton and Netemeyer (1992) stated that positive involvement affects voters ’ knowledge and confidence in the preferred candidate. According to previous research, involvement is considered as one of the most important factors in determining consumers ’ decision-making processes and behaviours (Celsi and Olson, 1988; O ’ Cass, 2002; O ’ Cass and Pecotich, 2005; Petty et al. , 1983). Therefore, based on the aforementioned literature, the following hypothesis was posited: H3. Involvement has a significant impact on students ’ satisfaction with campus e-voting. 2.4 Voters ’ confidence Confidence is described as “ a feeling, which reflects the coherence of the information and the cognitive ease of processing it ” (Kahneman, 2011, p. 212). Voters ’ confidence can be boosted when there is appropriate execution of the voting process in terms of the security of the ballot, acceleration of the handling of results, and ease in the voting process. With poor management, planning and design, the whole electoral process can be weakened (Peter, 2011). Previous studies from Schaupp and Carter (2005) and Winchester et al. (2015) asserted that voters ’ decision-making and participation is affected by issues of confidence in the preferred candidate, trust and performance, which determines the probabilities of consistently voting for the similar party again. Voters ’ confidence in decision-making is influenced by information search, knowledge and involvement of the voters (O ’ Cass and Pecotich, 2005). In other studies, scholars such as Ben-Ur (2007) avowed that voters ’ decision-making and satisfaction is affected by their confidence as well. When voters gain the information or experience related to voting, such knowledge allows them to make a confident judgment (Burton and Netemeyer, 1992). However, deficiency in confidence causes young voters to be increasingly reluctant to participate in politics, which in turn confines their engrossment (Harris et al. , 2010). They may not have confidence in their voting decisions even though they are confident in the democratic process (Manning, 2013). Inadequate levels of political knowledge causes susceptibility in voting confidence (Henn and Foard, 2012). Based on the above discussion, the subsequent hypothesis was formulated: H4. Voters ’ confidence has a significant impact on their satisfaction with campus e-voting. 2.5 Stability Stability is defined as “ a situation where a voting scheme is stable if its outcomes are such that, for any preference profile, every threat has a counter threat ” (Pattanaik, 1976, p. 1). This study considers voters ’ stability as an individual assertion towards a specific political party, whereby they are consistent every time they cast their votes. The stability of an election process affects their trust and confidence. According to Burton and Netemeyer (1992), confidence has a significant impact on the stability of preferences over time. High-involvement voters are more stable in their voting decisions and show higher resistance to change in their preference towards the candidate or party in voting, of which their choice is stable (Ben-Ur, 2007; Burton and Netemeyer, 1992). On the contrary, low-involvement voters express lower stability and consequently provide low commitment to their favourite candidate (Ben-Ur, 2007). A research study by O ’ Cass (2004) found that voting stability is affected by aspects such as voter feelings, involvement and satisfaction. Thus, the next hypothesis proposed is as follows: H5. Stability has a significant impact on students ’ satisfaction with campus e-voting. 947 Campus e-voting Downloaded by Göteborgs Universitet At 15:08 16 March 2018 (PT) 2.6 Information seeking Information seeking is related to the process where people deliberately search for information to update their current knowledge (Marchionini, 1995). An evaluation on young adult voters ’ decision-making involvement within a compulsory political system by Winchester et al. (2015) found four key themes surrounding the information seeking process: the reach of the available information, the frequency of the information presented, the creativity of the message, and one-way vs two-way communication. Information seeking in the voting context tends to be more internalised for lower-involvement decision among young adult voters and their external search works are narrower (Winchester et al. , 2015). In an analogous manner, previous studies of passive search for information backings showed that in a low-involvement environment, voters minimally look for more information (Ben-Ur, 2007; Rothschild, 1978). In addition, it was noted that people with low involvement or low interest in a particular subject have negligible enticement to hunt for information (Apospori et al. , 2010; Sears, 1987). Based on this rationale, the following hypothesis was derived: H6. Information seeking has a significant impact on students ’ satisfaction with campus e-voting. 2.7 Satisfaction User satisfaction refers to the “ user ’ s psychological or affective state resulting from a cognitive appraisal of disconfirmation ” (Bhattacherjee, 2001, p. 351). According to Aljarrah et al. (2016), Henn et al. (2002) and Schaupp and Carter (2005), they confirmed that satisfaction in voting reflects young people ’ s reliance and confidence in democracy to cast votes. Although e-voting systems facilitate the reduction of costs, paper usage, technical hitches, support speeds and flexibility for the disabled, voters ’ decision-making and satisfaction are affected in terms of their inability to check whether their votes are correctly recorded and corroborated (Bishop and Wagner, 2007). Satisfaction is superior when the final result tops one ’ s expectations and pre-purchase evaluation while decision-making determines their post-purchase satisfaction (Anderson et al. , 1994; Dube and Menon, 2000; Tam, 2011). Within the voting perspectives, post-purchase dissatisfaction is marginal among high-involvement voters due to active information seeking, higher confidence and more knowledgeable compared to low-involvement voters (O ’ Cass, 2002, 2004). Accordingly, the former pays more concern on the results of the election and eludes dissatisfaction in their decision. They are also willing to wait for the next election to rectify the errors should they make mistakes in the current election compared to the latter (O ’ Cass, 2004; Winchester et al. , 2015). 2.8 Trust in the system Trust in the system is related to self-assurance in a system reliability and integrity in spite of the risks involved during the engagement of any transactions (Barbalet, 2009). Trust has been studied among citizens in developed countries with regards to various contexts including trust in the internet and trust in the government. For instance, Avgerou (2013), Carter and Bélanger (2005) and Powell et al. (2012) conducted a study among US residents and found that the likelihood of the usage of e-government services including e-voting is affected by trustworthiness in the system and the electoral authorities. Scholars such as Alomari et al. (2012) avowed that trust in the internet, trust in the government, attitudes, beliefs, computer and internet skill confidence, and website design jointly affect e-voting adoption among nations. On a similar note, Alomari (2016) found that voters ’ attitudes, complexity, perceived usefulness and trust in e-government including e-voting are significant factors in the acceptance of e-voting. However, studies on trust in the system as moderator variables have yet to examine the impact of students ’ satisfaction with campus e-voting. Henceforth, this study investigates the moderating role of trust in the system in 948 JEIM 30,6 Downloaded by Göteborgs Universitet At 15:08 16 March 2018 (PT) order to acquire better insights of the relationships between perceived knowledge, commitment to vote, involvement, voters ’ confidence, stability, information seeking and students ’ satisfaction in campus e-voting. Thus, the hypothesis postulated was: H7. Trust in the system moderates the relationship between perceived knowledge, commitment to vote, involvement, voters ’ confidence, stability, information seeking and students ’ satisfaction in campus e-voting. Figure 1 illustrates the proposed theoretical framework of this study whereby students ’ satisfaction with campus e-voting is anticipated to be influenced by aspects of perceived knowledge, commitment to vote, involvement, voters ’ confidence, and stability, as well as information seeking. Besides that, it is postulated that trust in the system moderates the relationship between perceived knowledge, commitment to vote, involvement, voters ’ confidence, stability, information seeking and students ’ satisfaction in campus e-voting. 3. Methodology 3.1 Participants and procedure This study employed a quantitative research design and applied the distribution of a structured self-administered questionnaire among 200 students in a public higher learning institution located in the Federal Territory of Labuan, Malaysia. This method is appropriate as it generalises the sample data to the population and reveals the patterns and trends. Data collection was conducted using a convenience sampling method conducted in January 2016 over a period of two weeks. This allows the researcher to control the representativeness of the sample. The respondents were pre-screened and restricted to university students with active academic status and registered in the university data management system. The respondents should also have at least a one-time experience in casting votes electronically in the past year during the campus e-voting period. In total, 75 per cent of the 200 questionnaires distributed (i.e. 150 respondents) responded positively in completing the structured self-administered questionnaire by rating their degree of agreement to the propositions in the survey. This implies that the responses were completed and usable. The sample size is accurate as per partial least square-structural equation modelling (PLS-SEM) approach. The prerequisite of a sample size must be at least ten times the largest number of structural paths directed at a particular latent construct in the structural model (Hair et al. , 2011). Several studies acknowledged that a sample size of H7 H6 H5 H4 H3 H2 H1 Perceived Knowledge Commitment to Vote Involvement Voters’ Confidence Stability Information Seeking Satisfaction with Campus E-voting Trust in the System Figure 1. Proposed theoretical framework 949 Campus e-voting Downloaded by Göteborgs Universitet At 15:08 16 March 2018 (PT) less than 500 is adequate (Hair et al. , 2010; Iacobucci, 2010). The justification for using students as the sample in this study is that no attempt was made to estimate the population parameters. Hence, a student sample is adequate to investigate the influence of students ’ decision-making in campus e-voting in a public higher learning institution in Malaysia. 3.2 Questionnaire development and instrument The questionnaire was developed in English and encompassed three sections. The first section sought to acquire the respondents ’ general demographic characteristics, such as gender, age and level of education. The next section comprised of questions about the respondents ’ experience in campus e-voting. The final section of the questionnaire included 31 questions on the aspects related to e-voting (see Appendix 1), such as perceived knowledge (seven items), commitment to vote ( five items), involvement ( five items), confidence (three items), stability (two items), information seeking (three items) and satisfaction (seven items). This multi-item measurement items were designed in a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree) and were adapted from the following sources: perceived knowledge from Burton and Netemeyer (1992), O ’ Cass (2002), O ’ Cass and Pecotich (2005), Flynn and Goldsmith (1999) and Goldsmith et al. (1998), commitment to vote from the pilot study, and involvement, confidence, information seeking, as well as satisfaction from O ’ Cass (2002) and O ’ Cass and Pecotich (2005). Finally, the items for stability were adapted from O ’ Cass and Pecotich (2005). 3.3 Statistical technique The completed questionnaires were entered into the Statistical Package for Social Sciences computer programme Version 21. The data were analysed using several statistical techniques such as descriptive analysis, including mean, standard deviation, skewness and kurtosis. Next, the PLS-SEM approach supported by Smart-PLS 2.0 was applied to test the research hypotheses with the rationale it is a powerful multivariate analysis technique which eludes parameter estimation biases in-built in regression analysis, demands least constraints on measurement of the constructs, and is useful under non-normality conditions (Hair et al. , 2014). Subsequently, the moderating effect was performed to investigate whether trust in the system significantly moderates the relationship between perceived knowledge, commitment to vote, involvement, voters ’ confidence, stability, information seeking and students ’ satisfaction in campus e-voting. 4. Data analysis Table I displays the frequency distribution of the respondents ’ demographic profiles. A total of 150 students were involved in the study, with 35 per cent of them male and 65 per cent of them female. Besides, more than three quarter of the respondents (78 per cent) ranged from 19 to 22 years old, while 15 per cent of the respondents were less than 19 years old, and the remainder of the respondents ranged between 23 and 26 years old. In regards to the level of education, 59 per cent of the respondents were well-educated and held degrees, followed by 36 per cent who held STPM/Matriculation certificates, and the remaining 5 per cent with diplomas. 4.1 Experience on campus e-voting Table II depicts the respondents ’ experience in campus e-voting. The respondents were required to provide information related to the frequency of casting votes in campus e-voting, level of convenience, trust in the campus e-voting system, as well as the language used. Descriptively, a majority of the respondents had one-time experience of casting votes in the campus e-voting system and this contributed to 62 per cent of the total respondents. 950 JEIM 30,6 Downloaded by Göteborgs Universitet At 15:08 16 March 2018 (PT) The remaining 38 per cent of the respondents had experienced it twice. The study discovered that close to 31 per cent of the respondents considered themselves to have experienced high level of convenience on the campus e-voting system, while 58 per cent reflected that they had medium level of convenience. The remaining 11 per cent of the respondents experienced low convenience level while conducting campus e-voting. As for the frequency distribution of the levels of trust on the campus e-voting system, a small proportion of the respondents (28 per cent) had high level of trust, whereas most of the respondents (72 per cent) had medium level of trust on the campus e-voting system during the voting period. More than half of the respondents (64 per cent) responded that English language is the preferred type of language used in the campus e-voting system, while the remaining 36 per cent preferred Malay language. 4.2 PLS PLS-SEM was executed via a two-stage data analysis: measurement model and structural model. The measurement model was inspected for the psychometric properties of the measures, for latent construct via assessments of the internal consistency reliability, convergent validity and discriminant validity of the construct measures. In the structural model, the statistical significance of path coefficients was examined via t -tests by utilising a bootstrapping resampling technique with 500 sub-samples (Yi and Davis, 2003) to examine the impact of the exogenous variables (i.e. perceived knowledge, commitment to vote, involvement, voters ’ confidence, stability and information seeking) on the endogenous variable (i.e. satisfaction in campus e-voting). Variables Frequency Percentage Gender Male 52 34.6 Female 98 65.4 Age (years old) o 19 11 7.3 19-22 117 78.0 23-26 22 14.7 Educational level STPM/Matriculation 54 36.0 Diploma 7 4.7 Degree 89 59.3 Table I. Demographic profile of respondents Variables Categories Frequency Percentage Frequency of casting of votes in campus e-voting Once 93 62.0 Twice 57 38.0 Level of convenience on the campus e-voting system High 46 30.7 Medium 88 58.7 Low 16 10.7 Level of trust towards campus e-voting system High 42 28.0 Medium 108 72.0 Language used in campus e-voting system English 96 64.0 Malay 54 36.0 Table II. Experiences on campus e-voting 951 Campus e-voting Downloaded by Göteborgs Universitet At 15:08 16 March 2018 (PT) 4.2.1 Measurement model . The reliability of the measurement items was analysed via Cronbach ’ s α and composite reliability. The results of Cronbach ’ s α and composite reliability for all variables were greater than 0.70 (see Table III), indicating strong reliability among the measures. The convergent validity of the construct measures was assessed based on the factor loadings and average variance extracted (AVE). A significant and acceptable measurement item loading occurs when the values are beyond 0.50 (Hair et al. , 2010). The factor structure matrix in Table IV shows that all factor loading items topped 0.50 with no cross loadings, specifying that the measurement items well represented the identifiable variables. However, several items with loadings lower than 0.50 were discarded beforehand. Additionally, the convergent validity was also attained as the AVE values were beyond 0.50 as determined by Fornell and Larcker (1981), indicating that more than one-half of the variance observed in the items were accounted for by the hypothesised constructs. Discriminant validity was examined by comparing the shared variance between factors with the AVE of the individual factors (Fornell and Larcker, 1981). In Table V, the shared variance between factors was below the square root of the AVE of the individual factors, ratifying the discriminant validity. Moreover, all the correlations between the six factors were below 0.70, affirming a valid level of discriminant validity and multicollinearity was vague in this research (Pallant, 2007; Sussman and Siegal, 2003; Tabachnick and Fidell, 2007). According to the results, perceived knowledge showed the strongest association with satisfaction in campus e-voting ( r ¼ 0.430, p o 0.01), followed by involvement ( r ¼ 0.302, p o 0.01) and stability ( r ¼ 0.298, p o 0.01). However, further evaluation of the coefficients showed that commitment to vote was also significantly correlated to satisfaction in campus e-voting ( r ¼ 0.118, p o 0.05). Besides, the means for all constructs ranged from Variables Items Standardized loadings Cronbach ’ s α Composite reliability Average variance extracted Perceived knowledge PK1 0.728 0.855 0.893 0.587 PK2 0.837 PK3 0.854 PK5 0.790 PK6 0.784 PK8 0.565 Commitment to vote CTV2 0.748 0.756 0.784 0.645 CTV4 0.855 Involvement I1 0.672 0.866 0.902 0.651 I2 0.842 I3 0.794 I4 0.849 I5 0.860 Voters ’ confidence C1 0.851 0.831 0.898 0.747 C2 0.890 C3 0.851 Stability S1 0.570 0.835 0.921 0.853 S2 0.547 Information seeking IS1 0.903 0.866 0.917 0.786 IS2 0.858 IS3 0.899 Satisfaction SC1 0.836 0.809 0.874 0.635 SC2 0.815 SC3 0.822 SC4 0.708 Table III. Reliability and validity analysis 952 JEIM 30,6 Downloaded by Göteborgs Universitet At 15:08 16 March 2018 (PT) 3.018 to 3.673 on a five-point Likert scale of 1 ¼ strongly disagree to 5 ¼ strongly agree, confirming that the bulk of respondents expressed positive agreement on campus e-voting. Next, the skewness of all constructs stretched from 0.064 to 0.716, which fell under the cut-off value of ± 2, while the kurtosis values oscillated from 0.015 to 1.312, which were well below the inception of ± 10. The results implied that the scores conformed to a normal distribution or a bell-shaped curve. Factor items Perceived knowledge Commitment to vote Involvement Voters ’ confidence Stability Information seeking Satisfaction PK1 − 0.159 0.272 0.256 0.465 0.728 0.287 0.383 PK2 − 0.264 0.307 0.235 0.573 0.837 0.366 0.338 PK3 − 0.154 0.303 0.240 0.557 0.854 0.333 0.403 PK5 − 0.196 0.236 0.207 0.553 0.790 0.257 0.431 PK6 − 0.171 0.271 0.365 0.533 0.784 0.243 0.363 PK8 − 0.009 0.340 0.223 0.459 0.565 0.256 0.304 CTV2 0.748 0.014 0.013 − 0.110 − 0.045 − 0.271 − 0.037 CTV4 0.855 − 0.117 − 0.226 − 0.307 − 0.274 − 0.347 − 0.136 I1 − 0.234 0.068 0.290 0.672 0.398 0.122 0.224 I2 − 0.195 0.354 0.407 0.842 0.545 0.252 0.416 I3 − 0.157 0.331 0.195 0.794 0.573 0.223 0.459 I4 − 0.249 0.281 0.267 0.849 0.610 0.205 0.335 I5 − 0.285 0.261 0.300 0.860 0.605 0.254 0.354 C1 − 0.086 0.851 0.265 0.249 0.338 0.212 0.454 C2 0.016 0.890 0.329 0.253 0.305 0.266 0.516 C3 − 0.136 0.851 0.312 0.388 0.342 0.230 0.579 S1 − 0.084 0.570 0.311 0.378 0.352 0.184 0.891 S2 − 0.122 0.547 0.262 0.452 0.511 0.283 0.955 IS1 − 0.164 0.338 0.903 0.291 0.284 0.178 0.266 IS2 − 0.119 0.273 0.858 0.438 0.388 0.124 0.309 IS3 − 0.106 0.315 0.899 0.260 0.218 0.147 0.239 SC1 − 0.340 0.182 0.130 0.092 0.250 0.836 0.156 SC2 − 0.272 0.035 0.009 0.082 0.212 0.815 0.052 SC3 − 0.352 0.186 0.039 0.208 0.273 0.822 0.194 SC4 − 0.261 0.388 0.301 0.406 0.439 0.708 0.357 Note: Italic values are items loadings which surpassed the threshold value of 0.500 Table IV. Factor structure matrix of loadings and cross loadings Variables 1 2 3 4 5 6 7 (1) Perceived knowledge 0.766 (2) Commitment to vote 0.079 0.803 (3) Involvement 0.599** 0.043 0.807 (4) Voters ’ confidence 0.345** 0.228** 0.328** 0.864 (5) Stability 0.374** 0.184* 0.388** 0.515** 0.924 (6) Information seeking 0.304** 0.229** 0.361** 0.347** 0.268** 0.887 (7) Satisfaction 0.430** 0.118* 0.302** 0.297** 0.298** 0.209** 0.797 Mean 3.018 3.479 3.039 3.540 3.180 3.673 3.104 SD 0.532 0.551 0.815 0.788 0.605 0.847 0.641 Skewness 0.574 0.260 0.064 0.542 0.716 0.424 0.640 Kurtosis 1.193 0.087 0.015 0.973 1.084 0.389 1.312 Notes: The diagonal elements shown in italic are the square root of the average variance extracted. *,**Significant at the 0.05 and 0.01 levels (two-tailed) Table V. Inter-construct correlations and AVE along the diagonal 953 Campus e-voting Downloaded by Göteborgs Universitet At 15:08 16 March 2018 (PT) In PLS-SEM, the goodness of fit (GoF) was calculated by the SQRT of the average communality and multiplied by the average of R 2 . The observed GoF for the measurement model was 0.440 which was higher than the threshold value of 0.36 as put forward by Tenenhaus et al. (2004) and Vinzi et al. (2010), inferring that the quality of the GoF indicators is good and high. 4.2.2 Structural model . Table VI depicts the statistical results for the structural model of the PLS approach, including path coefficients, t -values, p -values and R 2 . The R 2 value of the model was 0.723 signifying that 72.3 per cent of the variance in satisfaction in campus e-voting can be described by all exogenous variables. Four paths out of the six relationships tested were significant at the significance level of 95 per cent. Specifically, perceived knowledge showed significant positive impact on students ’ satisfaction with campus e-voting ( β 1 ¼ 0.313, t -value ¼ 4.761, p o 0.05). Thus, H1 is supported. Likewise, the standardised β coefficients pointed out that the commitment to vote, as hypothesised in H2 , also showed significant effect on students ’ satisfaction with campus e-voting ( β 2 ¼ 0.334, t -value ¼ 7.303, p o 0.05), inferring that H2 is endorsed. Indeed, this factor is anticipated and believed to be the strongest determinant of students ’ decision-making and satisfaction in campus e-voting. However, the PLS approach quantified that the influence of the involvement factor on students ’ satisfaction with campus e-voting was not significantly evident ( β 3 ¼ 0.096, p W 0.05). Thus, H3 is not supported. On the other hand, voters ’ confidence, as postulated in H4 , had a significant effect on students ’ satisfaction with campus e-voting ( β 4 ¼ 0.151, t -value ¼ 2.903, p o 0.05). Therefore, H4 is maintained. Likewise, as proposed in H5 , stability showed significant positive effect ( β 5 ¼ 0.129, t -value ¼ 2.903, p o 0.05) on students ’ satisfaction with campus e-voting. Thus, H5 is supported as predicted. On the other hand, the respondents noted that information seeking had no effect on their satisfaction with campus e-voting ( β 6 ¼ 0.008, t -value ¼ 0.182, p W 0.05). Hence, the posited H6 is not reinforced by the data and is rejected. Figure 2 illustrates the results of structural model of impact on students ’ satisfaction with campus e-voting. 4.3 Moderating role of trust in the system The moderating effect of voters ’ trust in the system on the relationship between perceived knowledge, commitment to vote, involvement, voters ’ confidence, stability, information seeking and students ’ satisfaction in campus e-voting, as proposed in H7a - H7f , respectively, was tested using the equation developed by Chin (2003). Out of the 150 respondents, 42 of them were classified into high level of trust, while the remaining 108 respondents were grouped into medium level of trust (see Table II). The PLS-SEM results in Table VII depicted that voters ’ trust in the system significantly moderated the influence of perceived knowledge on students ’ satisfaction in campus e-voting ( Δ β ¼ 0.47, p o 0.05), commitment