Keywords

1 Introduction

The Unicef [24] report indicates a 30 % growth per annum, for mobile phone adoptions in Africa within the 2002–2012 period. Sanou [22] the director of the International Telecommunications Union (ITU) claim that the number of mobile-broadband subscriptions will reach 2.3 billion by the end of 2014, with 55 % of them in developing countries. Availability of low cost handsets and cheap SIM cards contributed to the 20 % increase in the embracement of mobile phones in South Africa from 2005 to 2010, particularly among the youth [24]. The world’s emerging economies are speculated to foster economic and social development through the appropriation of Information Communication Technologies (ICTs) in form of mobile systems. This study identifies voting as one of the essential areas that mobile technologies can foster a quantum leap of evolution in servicing development for the resource constrained nations. The focus of the study is based on mobile technology use and acceptance within the voting environment.

Literature has emphasised on Human Computer Interaction (HCI) as the backbone of technology use and acceptance. Several studies have reported on various technology adoption frameworks that addresses technology use and acceptance. Among these frameworks include the task technology fit (TTF) theory. Previous studies on TTF have often stressed task characteristics and technology characteristics as the principal determinants of effective TTF. Since any process of TTF cannot be comprehended without considering user self-efficacy and user characteristics. The authors argue that any pre-occupation with TTF from the perspective of task characteristics and technology characteristics that does not embrace the individual self-efficacy and user characteristics is unrealistic and unauthentic. Contributing to contemporary debates on TTF within the HCI, this study provides self-efficacy as an antecedent for mobile voting TTF.

The purpose of this study is to empirically examine the possibility of extending the TTF theory by cooperating self-efficacy to the task characteristics and technology characteristics within the voting context. Drawing on these antecedents this study develops a factor model and empirically tests it on South African tertiary students to explore their mobile voting TTF. The study participants voted for the Information Technology (IT) organisation representatives using a mobile voting application developed by the researchers. Data was collected using a self-completion questionnaire from 217 participants at a South African University of Technology. The partial least squares (PLS) was employed for statistical analysis. Overall, the proposed model displayed a good fit with the data and rendered satisfactory explanatory power for mobile voting. Findings of the study suggested that device portability, reliable connectivity, and perceived voting privacy constituted the technology characteristics. Mobile voting efficiency, accuracy, and fairness contributed to mobile voting task characteristics. The study also confirmed the statistical significance of the user self-efficacy on TTF. Perceived ease of use led to electronic voting self-efficacy.

2 Literature Review

2.1 Mobile Phone Voting

Hasty global growth in Internet and mobile applications utilization has stirred numerous creativities directed at applying information and communication advances to develop what many refers to as "digital" or "electronic democracy" [7, 20]. An Internet or Electronic Voting System refers to a polling system that employs an electronic polling scheme that permit electorates to convey their votes to election officials via Information communication infrastructure [7]. According to [2, 25] electronic voting has several advantages which include but not limited to enabling the users to easily express their preferences from any location, exact interpretation of ballots and the virtually immediate publication of the results and these aspects may help to [20] increase the number of voters in public elections.

Soundness, unreusability/uniqueness, completeness are some of the major objectives for electronic voting system [2]. Soundness ensures that member of the electorate cannot invalidate the voting process. This implies that either the final tally is correct or that any error in the final tally can be detected and consequently corrected. Unreusability and completeness contribute to soundness. Unreusability ensures that a voter cannot vote twice. Completeness safeguards against the following (i) forge a vote; (ii) remove a valid vote from the final tally; (iii) add an invalid vote to the final tally [2]. In practice, the property of completeness requires that all and only the valid votes get counted. Other electronic voting objectives are privacy, eligibility, fairness, verifiability, uncoercibility, mobility, efficiency, scalability, deniable authentication [2, 16, 17, 25]. Several previous studies on electronic voting concentrate on technical design and implementation aspects of these technologies [2, 7, 16, 17, 25]. Insufficient studies have examined non-technical HCI aspects that foster adoption of electronic voting systems.

A previous study conducted in the United States of America examined factors that can affect a citizen’s intent to vote using electronic devices [20]. The study was underpinned by the Unified Theory of Acceptance and Use of Technology (UTAUT). The results indicated that performance expectancy, effort expectancy, social influence, trust in the internet, and computer anxiety were significantly related to intent to use electronic voting. Trust in the government was insignificant. Performance expectancy, social influence, and computer anxiety were related to intent to electronic voting for both young adults and seniors. Effort expectancy was related to intent to vote for the seniors but not young adults, and trust in the internet was related to intent to vote for young adults but not seniors [20]. However, this study will utilise the TTF theory to establish factors that influence utilization of the mobile phone voting systems.

2.2 Theoretical Framework

With reference to the “Task-Technology Fit Theory” developed by [14], IT becomes useful for one’s performance enhancement in cases where technology is accepted and utilised by the users resulting in a fit between technology and the assignments it is meant to support. In other words a fully fleshed TTF environment ensures smooth execution of work facilitated by a match between the technology and tasks involved. Several previous studies have attempted to extend this theory in different IT use settings [8, 18, 26]. Some of the above cited studies [8, 18, 26] attempted to extend the theory using individual characteristics. However, TTF has been insufficiently applied in mobile phone voting contexts. Besides limited studies have attempted to extend it using self-efficacy as an antecedent for TTF.

Self-efficacy. The concept of self-efficacy has been widely used in educational psychology [3, 4, 23]. Various authors within the educational technology context [5, 6, 9, 11] adopted the concept in an effort to explore the technology user’s confidence in using Information Communication Technologies (ICTs) in performing teaching and learning tasks. Research suggests that technology self-efficacy plays a significant role in an individual’s decision to use technology and how comfortable users are in learning skills related to effective use [5, 6]. Insufficient studies have applied the self-efficiency concept in the mobile phone voting HCI perspectives. This study aims to close this gap by expanding the TTF theory with the self-efficacy antecedent in the mobile phone voting environment (Fig. 1).

Fig. 1.
figure 1

Proposed research model for the mobile phone voting task technology fit

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Reference [23] argue that an individual’s belief in his or her ability to effectively preform a given task in a given context plays a role in that individual’s willingness in performing that task continually. Consequently a mobile voter’s belief in his or her ability to successfully cast his or her vote using a mobile phone plays a role in stimulating a voter’s willingness to vote using such technology in future (Table 1).

Table 1. The study operational definition contents
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Research Hypothesis:

H1: Task characteristics significantly leads to TTF

H2: Technology characteristics significantly leads to TTF

H3: Self-efficacy significantly leads to TTF

H4: TTF significantly leads to utilization

3 Research Design

3.1 Participants

The study attempted to systematically investigate the factors which affect utilization of mobile phone voting technology. The participants were IT organisation members. These IT organisation members were registered students at a University of Technology in South Africa. During the 2013 committee member elections, the organisation introduced mobile phone voting which ran parallel with the traditional paper based voting. The IT organisation voters’ role had 342 eligible voters. Exactly 301 voters casted their votes, among these voters 217 voted using mobile technologies while 84 voted using the traditional paper based voting system.

3.2 Research Instrument

An online survey was utilized for data collection. The questionnaire items of measure for task characteristics, technology characteristics, TTF and utilization where adopted from previous studies [14, 15]. The questionnaire items of measure for self-efficacy were adopted from [9]. However, all questionnaire items were tailor-made to meet the study’s objectives. The questionnaire was based on a 5-point Likert scales ranging from (1) strongly disagree to (5) strongly Agree.

3.3 Data Analysis

Reference [21] argue that PLS is suitable for the establishment of the investigation of the measurement and the structural model. For this reason PLS was employed in this study; particularly for investigating the measurement model with respect to its individual item loadings, construct reliability, convergent validity and discriminant validity. Subsequently, the structural model is assessed, in order to deduce observations regarding the causal relationships and their significance.

The measurement model for this study was assessed using the confirmatory factor analysis (CFA). A total of ten model-fit indices were employed for the purposes of examining the model’s overall goodness of fit. Table 2 shows that all the model-fit indices exceeded their respective common acceptable levels as recommended in [5, 13] which demonstrated a good fit between the model and data.

The Composite reliability (CR) and Cronbach’s alpha values (α) were employed in this study to estimate the internal consistency. According to [20], a minimum of 0.7 for both CR an α values is acceptable for good internal consistency. In this study all constructs produced values greater than 0.7 indicating a satisfactory internal consistency. With regards to convergent validity [1, 12] proposed that a minimum of 0.5 average variance extracted (AVE) value is adequate to support a good convergent validity. All the constructs in this study produced AVEFootnote 1 values ranging from 0.686 to 0.772 indicating satisfactory convergent reliability. Table 3 shows the Cronbach’s alpha values and, CR and AVE values for the constructs considered in this study.

Table 2. Fit indices for measurement and structural models
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Structural Model. The bootstrapping technique was used to test the structural model of the study [19]. A total of 500 resamples were conducted. The t-values assessment was based on a two-tail test with statistically significant levels of p < 0.05 (*), p < 0.01 (**) and p < 0.001 (***). Interestingly, the outcomes of the structural model in terms of direct effects, bootstrapping and t-statistics confirmed all the hypotheses of the study, at p < 0.001 significance levels. In particular, the technology characteristics construct is associated with the strongest significant relationship with TTF (H2: t-value = 0.454). The self-efficacy construct is associated with a relatively strong significant relationship with TTF (H3: t-value = 0.393). The relationship H1, although significant it has the least strong relationship with TTF construct (H1: t-value = 0.229). Finally, the relationship between TTF construct and technology utilization proved to be significant (H4: t-value = 0.348) (Fig. 2).

Fig. 2.
figure 2

Structural model for the mobile phone voting task technology fit

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Table 3. The study internal consistency and convergent validity
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4 Discussion

The findings suggest that the relationship between task characteristics and TTF has positive significant, which is in line with the findings of the previous studies [8, 14, 15]. The ability for one to successfully vote remotely, results validated accurately and the fairness of the process are some of the voting characteristics that lead to the relationship being significant.

This study validated TTF hypothesis that states that, there is a positive significance between technology characteristics and TTF. Previous studies [14, 15, 26] also confirmed significance of the relationship in their studies. An application utilised in this study supported the following goals: soundness, unreusability/uniqueness, privacy, eligibility, fairness, verifiability, efficiency, and mobility. These goals have been reported in previous studies as being essential for an electronic voting systems [7, 17, 25]. Mobility, privacy and fairness had the highest ratings contributing to TTF signifying that voters prefer casting their votes in areas of their convenience and not necessarily travel to cast their votes. Furthermore, voters prefer casting their votes in a secure environment in which they are guaranteed that no one will temper with their votes.

The study findings indicate a positive relationship between technology self-efficacy and TTF. Possible reason for this outcome might be associated with the population employed in the study. Since the participants involved were IT students, it might be that their proficiency in the use of technology that contributed to their positive mobile phone voting system self-efficacy. Factors such as ease navigation interface, ease log-in, ease casting of votes, and ease verification of votes contributed to a positive relationship between self-efficacy and TTF.

5 Conclusion

Statistical data presented in this study validated the original TTF relationships (i.e. (1) Technology characteristics have a positive relationship with TTF. (2) Task characteristics have a positive relationship TTF. (3) TTF has positive relationship with utilization. Furthermore, findings of the study reflect that the self-efficacy antecedent extended the TTF theory in the mobile phone voting context. These findings have potential to contribute to the factors that promote mobile phone voting usage within the HCI context.