ABSTRACT
More and more individual taxpayers are using the online services of commercial tax software to prepare and file their tax returns electronically. However, online tax return does have risks, especially the risk of information privacy evasion. Little research has been conducted on the linkage between taxpayers’ online information privacy concerns (TOIPC) and taxpayers’ behavior intentions, and factors affecting online taxpayers’ information privacy concerns. This study identifies three primary dimensions for TOIPC (that is, control, awareness, collection), conducts empirical testing on the relationship between each privacy concern dimension and taxpayers’ intention to use online tax software, and explores factors affecting TOIPC (age, gender, trust, perceived risk, and victim of previous privacy invasion). The results suggest that taxpayers with high privacy concern about information collection will be less willing to use online tax software to file tax. Taxpayers’ perceived risks have significant positive relationship with the taxpayers’ online information collection concern. Implications and future research are discussed.
Key words: Privacy concerns, online tax software, intention to use.
More and more individual taxpayers are using the online services of commercial tax software to prepare and file their tax returns. According to the most recent data from the Internal Revenue Service (IRS, 2017), 127.3 million taxpayers (about 87.6%) e-filed their tax returns in 2016 either through tax professionals (58.5%) or self-prepared (41.5%). There are many advantages in using software to prepare and file tax return, such as calculation accuracy, improved efficiency, decreased processing costs by the Internal Revenue Service (IRS, 2017), and quick refunds (Brink and Lee, 2015). However, online tax return does have risks, especially the risk of information privacy evasion. Due to the preparation, filing, and storage of tax returns in electronic form, the security of tax return information is a critical issue (Schwartz, 2008). Schwartz states that tax preparation software, like other software, is subject to hacking, viruses, account breaches, and software failures.
A report conducted by TURSTe/National Cyber Security Alliance (NCSA, 2016), Consumer Privacy Index reveals that 92% of US internet users worry about their privacy online and 74% have limited their online activity in the last year due to privacy concerns. Meanwhile, although the Internal Revenue Code regulation §301.7216-1 states there will be a criminal penalty for tax return preparers who knowingly or recklessly disclose or use tax return information for a purpose other than preparing a tax return, many tax return software companies do share taxpayers’ data with affiliates or marketing partners, including taxpayers’ name, address, email address, phone number, income, dependents, charitable contributions and deductions for college tuition, business losses, etc. (Murray, 2012). Taxpayers have become more anxious that their personal information may be shared, misused, disclosed to unrelated parties, and perhaps even stolen by identity thieves.
Sutton (2010) calls for more research to investigate the relationship between the use of tax software and taxpayers’ behaviors. However, little research has been conducted on the linkage between taxpayers’ online information privacy concerns (TOIPC) and taxpayers’ behavior intentions, and factors affecting online taxpayers’ information privacy concerns. One relevant study is from McLeod et al. (2008) and finds that individuals’ computer expertise can increase their trust in the software system’s privacy protection. In another study, Apostolou et al. (2016) demonstrate that calculative commitment and affective commitment mediate the relationship between trust in tax software and taxpayers’ intention to use e-file. These initial studies focus on the impact of trust on taxpayers’ intention to use tax software. Trust may be related to taxpayers’ privacy concerns about using online software, but it is a totally different theoretical construct from information privacy concern. Researchers still know little about the dimensionality of TOIPC, the linkage between each dimension of TOIPC and taxpayers’ behavior intention, and factors affecting taxpayers’ privacy concerns about the online tax software, and intention to use online tax software. To address the inadequacies in current literature, this study provides a comprehensive examination on three dimensions of TOIPC (control, awareness, collection), conducts empirical testing on the relationship between each dimension and taxpayers’ intention to use online tax software, and explores factors affecting TOIPC (e.g., age, gender, trust, perceived risk, victim of previous privacy invasion, etc).
This study makes several contributions from both academic perspective and practitioner perspective. First, this study contributes to the online privacy concern literature by investigating the taxpayers’ privacy concerns about using online tax software. To our best knowledge, this study is the first to apply the IUIPC model to study the dimensionality of taxpayers’ online privacy concerns. Due to the distinction in the transaction nature, the taxpayers’ attitudes (privacy concerns) and behaviors toward purchasing services from online tax software may be quite different from other types of online customers. The findings from this study confirm that in contrast to regular online customers who have privacy concerns in three aspects (information collection, control, and information policy awareness), taxpayers worry more about information collection than information control and information policy, and this worry has a significant negative impact on their intention to use online tax software.
Second, the samples used in prior studies on tax software did not distinguish on the kind of tax software: commercial online tax software (online version) or personally owned tax software (download/cd version). A taxpayer can choose to pay commercial tax software to prepare and file tax return directly online or purchase tax software and install on personal computer to prepare and file tax return. However, there is a huge difference in terms of the taxpayers’ attitudes towards privacy between filing tax return through personally owned tax software and filing tax return through online commercial tax software. Online environment is much riskier than a personal computer, because all data collected by online software are saved in a third-party server and the taxpayers’ information can be lost, misused and/or hacked. This study specifically focuses on the taxpayers’ intention to use online tax software and finds that taxpayers’ privacy concerns do affect their intention to choose online tax software to file tax return.
Third, from practitioners’ perspective, the findings from this study can serve as a useful guidance for online tax software providers to understand potential customers’ online information privacy concerns and initial ways to address their concerns. The results suggest taxpayers with high privacy concerns are less likely to use online tax software because of the potential perceived risks. To attract more customers, marketers should address taxpayers’ perceived risk of using online tax software so that their privacy concerns about information collection are alleviated. At the same time, the findings of the study shed some lights on how to improve the taxpayer’s compliance rate.
Hypothesis development
Taxpayers’ online information privacy concerns and behavior intention
Information system researchers have explored and verified the dimensions of online consumer privacy concerns by conducting empirical studies (Smith et al.,1996; Malhotra et al., 2004). Among these studies, Malhotra et al. (2004) developed the construct model of Internet users’ information privacy concerns (IUIPC) based on social contract theory (SC). SC has been used to study online customers’ perceptions of fairness and justice, particularly in the context of individual customer-online vendor relationships. Since online taxpayers are a category of online consumers, we apply the IUIPC model to discuss the dimensionality of TOIPC.
The IUIPC model identifies three dimensions of online consumer privacy concerns: collection, control, and awareness. According to Malhotra et al. (2004), SC theory suggests that an online firm’s collection of personally identifiable data is perceived to be fair only when the individual is granted control over the information, and the consumer is informed about the firm’s intended use of the information. As a result, it is possible to characterize IUIPC in terms of three factors: collection, control, and awareness of privacy practices. The collection factor captures the central theme of equitable information exchange based on the agreed social contract. Meanwhile, the control factor represents the freedom to voice an opinion or exit the situation. Finally, the awareness factor indicates understanding about established conditions and actual practices. Following Malhotra et al. (2004), we propose that TOIPC center on the same three major dimensions: collection, control, and awareness of privacy practices.
Collection refers to the degree to which a taxpayer is concerned about the collection of personal information by online tax software companies. This collection factor is grounded on SC’s principle of distributive justice, which relates to “the perceived fairness of outcomes that one receives” (Culnan and Bies, 2003). In an equitable exchange, taxpayers give up personal information in return for something of value after evaluating the costs and benefits associated with preparing and filing tax return online. Thus, taxpayers may have less concern and more willingness to use online tax software knowing that their information will be used fairly to help them prepare and file tax return.
Control represents the taxpayer’s freedom to voice an opinion (that is, approve or deny sharing personal information for marketing purpose) or exit (that is, opt-out). This factor is related to SC’s principle of procedural justice. According to the principle of procedural justice, individuals view procedures as fair when they are vested with control of the procedures (Thibaut and Walker, 1975; Tyler, 1994). In other words, taxpayers will have less concern if they believe they can exercise enough control to decide whether they want to give information and how it will be used.
Awareness of privacy practices refers to policies and procedures by which online tax software companies make taxpayers aware of how the collected information will be used. This awareness factor incorporates two types of justices: interactional and informational.
Interactional justice includes issues of transparency and propriety of information use stated during enactment of procedures. Informational justice relates to the possible disclosure of specified information. When taxpayers are given the specifics of how the information will be used, their perception of fairness increases, and they will have less privacy concerns.
Prior studies in information system area show that information privacy concerns affect individuals’ attitudes and willingness to purchase online (Milberg et al., 2000; Stewart and Segars, 2002; Dinev and Hart, 2006). As discussed in Li and Santhanam (2008), people who have high concerns about privacy may think it is risky to provide information and therefore may react negatively to requests for personal information. That is, they may be unwilling to disclose information or terminate the transaction. In terms of tax return, those taxpayers who have high concerns of leaking personal information and/or identify theft may choose other tax return methods rather than online tax software.
In the privacy literature, a major research stream has emerged that uses intention-based theories such as theory of reasoned action (TRA) (Li and Santhanam, 2008; Liu et al., 2005; Malhotra et al., 2004) to predict outcomes of individuals’ privacy concerns. According to these theories, the level of individuals’ privacy concerns, as an attitude variable, has a negative effect on intentions to use internet-based services. Similarly, it can be expected that the same relationship will apply to taxpayers’ online privacy concerns and their intention to use online tax software. Taxpayers with high online privacy concerns will be more hesitant and have less intention to use online tax software to prepare and file tax return. As a result, we propose the hypotheses that:
H1a: Taxpayers with high privacy concern about information collection will be less willing to use online tax software to file tax return than will taxpayers with low privacy concerns.
H1b: Taxpayers with high privacy concern about information control will be less willing to use online tax software to file tax return than will taxpayers with low privacy concerns.
H1c: Taxpayers with high privacy concern about awareness of information policy will be less willing to use online tax software to file tax return than will taxpayers with low privacy concerns.
Factors affecting taxpayers’ online information privacy concerns
Prior studies on privacy reveal that many factors may affect online customers’ privacy concerns, such as privacy experiences, privacy perceived risk, personality differences, demographic differences, information sensitivity, and culture/climate difference (Smith et al., 2011; Bansal et al., 2010; Dinev and Hart, 2004, 2006). To have a complete understanding of individuals’ perceptions to information privacy-related issues, researchers must start to address salient beliefs and contextual difference at a specific level (Li and Santhanam, 2008). In the situation such as the relation between taxpayers and tax software companies, certain factors may be identified as being sensitive to the taxpayers’ online privacy concerns. In this section, we discuss some potential antecedent factors that may affect taxpayers’ online privacy concerns when using online tax software.
Gender
Previous studies show that females have higher privacy concerns than their male counterparts (Sheehan, 2009; Rowan and Dehlinger, 2014). Specifically, Sheehan (2009) finds that women generally are more concerned than men about their personal privacy, and men are more likely than women to change their online behaviors when facing privacy concerns. And economics and finance literature show that women generally are more risk averse than men (Croson and Gneezy, 2009). In terms of online tax return preparation and filing, due to unpredictable online environment, female taxpayers may express higher privacy concerns than male taxpayers. As a result, we hypothesize that
H2: Female taxpayers have higher privacy concerns than male taxpayers when using online tax software to prepare and file tax return.
Age
Culnan (1995) finds that those consumers who were less likely to be concerned about privacy were more likely to be younger. And young generation is more exposed to the online social media and/or online shopping experience, which causes them to be less concerned about online privacy invasion. In addition, younger people generally is less conservative than the older people, which leads them to be more willingly to try new technologies, such as online tax software. Therefore, we propose that,
H3: There is a positive relationship between age and privacy concerns in terms of using online tax software to prepare and file tax return online.
Taxpayers’ perceived risk
Perceived privacy risk has been defined as the degree to which an individual believes that a high potential for loss is associated with the release of personal information to a firm (Featherman and Pavlou, 2003; Malhotra et al., 2004). Previous studies find that perceived risk is an antecedent to privacy concerns (Dinev and Hart, 2004, 2006). Specifically, Dinev and Hart argue that individuals who perceive the Internet as an environment in which there is a risk of other parties’ opportunistic behaviors should have privacy concerns about who has access to the personal information that they disclose. In terms of online tax return, taxpayers who perceive preparing and filing tax return online as risky would express more privacy concerns of personal information being misused or stolen. Schaupp et al. (2010) found that perceived risk has a significant negative effect on intention to use an IRS endorsed e-file system. As a result, we hypothesize that:
H4: Taxpayers with higher perceived risk about the usage of online software have higher privacy concerns than those with lower perceived risk.
Trust
As discussed in Smith et al. (2011), literature shows that trust has a significant relationship with privacy concerns (Metzger, 2004; Xu et al., 2010; Belanger et al., 2002; Bansal et al., 2010; Chellappa, 2008; Malhotra et al. 2004). For example, Belanger et al. (2002) found that trust can reduce online consumers’ privacy concerns. Similarly, taxpayers expressing trust in internet vendors should have lower online privacy concerns. Thus, we hypothesize that:
H5: There is a negative relationship between trust and privacy concerns in terms of using online tax software to prepare and file tax return.
Victim of previous privacy invasion
Previous studies found that there is negative relationship between privacy concern and previous privacy invasion experience. For example, Smith et al. (1996) found that individuals who have been exposed to or been the victim of personal information abuse should have stronger concerns regarding information privacy. Awad and Krishnan (2006) found that previous privacy invasions are negatively associated with willingness to be profiled online. And Bansal et al. (2010) find that previous online privacy invasion increase privacy concerns in the health industry. Using online tax software to prepare and file tax return is another type of online activity by customers. It can be expected that previous privacy invasion experience in other online activities should increase taxpayers’ online privacy concerns when deciding whether or not to use online tax software. Therefore, we propose that:
H6: Previous online privacy invasion experience increases taxpayers’ privacy concerns about using online tax software.
1. Those self-prepared taxpayers can efile either through tax software provider’s website (online version) or tax software installed in personal computers (download/cd version). For example, TurboTax has online version or download/CD version. For online version, all information are stored in TurboTax’s server. On the other hand, for download/cd version all information are stored in taxpayer’s personal computer. For this study purpose, we focus on the taxpayers who prepare and file tax return through online tax software (online version).
Sample
To test our hypotheses, we employed a survey methodology. Specifically, we created a questionnaire with 25 items adapted from the related literature. Participants were 89 undergraduate students enrolled at a public university who volunteered for the survey. All the 89 students had filed tax return before, but never used the services of online tax software. In return for volunteering, participants were given course credits. The average age of the participants is 21.12 with the range from 19 to 32. 44 participants are males and 45 are females. 11 participants claimed as accounting majors, 6 participants did not provide the major information, and the rest of the participants are non-accounting majors, such as Management, Marketing, and Business Administration.
Measurement
Appendix 1 describes each measure in details. A seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree) is used to catch participant’s answers to the questions.
Intention to use online tax software
Intention to use is measured by two items asking each subject’s intention to use online tax software to prepare and file tax return.
Taxpayers’ online information privacy concerns
Taxpayers’ online information privacy concerns were measured with an adapted instrument developed by Malhotra et al. (2004). The instrument, which we refer to as TOIPC includes three dimensions: collection, control, and awareness of privacy practices. There are three questions in each dimension.
Perceived risk
The instrument of perceived risks of information disclosure online is adapted from Xu et al. (2010). Perceived risk is measured by three items.
Privacy victim
The one-item measurement for privacy victim is adopted from Smith et al. (1996). Subjects are asked “How frequently have you personally been the victim of what you felt was an improper invasion of privacy?” A seven-point Likert scale, ranging from 1 (very infrequently) to 7 (very frequently) is used.
Trust
The instrument of trust is adopted from McKnight et al. (2002) and Hui et al. (2007). This instrument contains seven questions.
In this stage of the research, we only include those participants who had filing experience but never used online tax software before into the study to eliminate some bias.
DATA ANALYSIS AND RESULTS
Validity and reliability test
The unidimensionality of the scales, validity, and reliability are evaluated via confirmatory factor analysis, as we form a priori links between item measures. Table 1 summarizes our testing results.
Good measurement model fit was hard to achieve at first due to the small sample size and the relatively larger number of variables to measure. Upon further examination of parameter estimates and other diagnostics, it became apparent that several error terms were significantly correlated. The literature suggests that problematic indicators can be eliminated, assuming content validity is not seriously impacted and the elimination action will not make appreciable differences in either the measurement or hypotheses testing results (Nahm et al., 2003). Accordingly, we delete one problematic indicator (item 3 in information policy awareness concerns) after a careful review of each. The elimination of this item is mainly due to error correlation of this item with other items in the same construct. Error correlations indicate that they share variance and possibly measure the same content.
Unidimensionality refers to the existence of a single concept underlying a group of measures (Gerbing and Anderson, 1988). Unidimensionality can be accessed via scree plots and eigenvalues (Rencher, 1995: 464). A rule of thumb is that eigenvalues greater than 1.0 for the first dimension and eigenvalues less than 1.0 for the second dimension support the existence of construct unidimensionality. As shown in Table 1, eigenvalues for each construct conform to these expected values. Therefore, it is established that the items underlying the constructs are unidimensional.
Reliability is typically assessed using Cronbach’s alpha and composite reliabilities. For Cronbach’s alpha, a minimum value of 0.70 is considered acceptable for existing scales and a value of 0.60 is deemed appropriate for newly developed scales (Byrne, 1998). The constructs in this study exceed the threshold values recommended in the literature (Byrne, 1998: 199); thus, they are judged to possess acceptable reliability.
Convergent validity represents how well the item measures relate to each other with respect to a common concept, and is exhibited by having significant standardized factor loadings of measures on hypothesized constructs (Anderson and Gerbing, 1988). All factor loadings in Table 1 for the constructs are significant. Therefore, convergent validity for scales is established. Discriminant validity represents how well an item measure relates to its hypothesized construct versus other constructs in the model. Discriminant validity is tested by a Chi-square test of the difference between the latent variables (Byrne, 1998). A series of pairwise Chi- square tests of the difference involving two constructs is conducted. Because all of the Chi-square tests are significant at p < 0.001, the existence of discriminant validity for the scales is supported (Byrne, 1998: 199).
Regression analysis on the linkage between TOIPC and intention to use
Regression analysis is conducted to test hypotheses 1a to 1c by using intention-to-use as dependent variable and three dimensions of TOIPC (collection, control, and awareness) as independent variables. Table 2 presents the standardized beta weights for the predictors and the adjusted R2.
The set of three dimensions of TOIPC jointly account for a statistically significant amount of variance in a firm’s financial performance (adjusted R2 =0.065; p-value <0.05). Specifically, the results show that there exists a statistically significant negative relationship between information collection and the intention to use online tax software (Beta=-0.256; p-value <0.05). However, the linkages between the other two privacy concern dimensions (control and awareness) and the intention to use online tax software turned out to be insignificant. Therefore, H1a is supported, and H1b and H1c are not supported.
Regression analysis on factors affecting TOIPC
Given that the testing results from our sample only support the hypothesized relationship between information collection concerns and the intention to use online tax software, we use information collection concerns as the dependent variable, when we explore factors affecting taxpayers’ online privacy concerns. Regression analysis is conducted using gender, age, perceived risk, trust, and privacy victim as the independent variables. As presented in Table 3, the results show that all independent variables are insignificant except perceived risks (Beta=0.632; p-value <0.05). Thus, H4 is supported. H2, H3, H5 and H6 are not supported.
Robust test
In order to address the concern that the findings in the study is random due to the limited sample data, we collected additional survey data in the following semester based on different group of students in the same university. 95 participants reported that they filed tax return before, but never used the services of online tax software and are included in the robust test. The test results shown in Tables 4 and 5 are consistent with the findings.
This study investigates whether online privacy concerns have any impact on the taxpayers’ intention to use online tax software to prepare and file their tax return, and identities what factors that may affect TOIPC. Building on the IUIPC model developed by Malhotra et al. (2004), this study identifies three dimensions for TOIPC (collection, control, and awareness). Then we conducted empirical testing the impact of TOIPC on the taxpayers’ intention to use online tax software along the three dimensions. Specifically, taxpayers with high privacy concern about information collection will be less willing to use online tax software to file tax return than will taxpayers with low privacy concerns. However, this study didn’t find evidence that the other two dimensions of TOIPC information control concern and information policy awareness concern have significant effect on taxpayers’ willingness to use online tax software. These findings suggest that information collection concern is the biggest concern that taxpayers have when they decide whether or not to online tax software. In addition, we investigate the potential factors that may affect the taxpayers’ privacy concerns about the collection of personal information by online tax software. Among the variables tested, only perceived risks have significant positive relationship with the taxpayer’s online information collection concern, which suggests that when taxpayers perceive online preparing and filing tax return risky, they have higher privacy concerns about their personal information being misused or stolen.
As with all research, this study has limitations. First, because we surveyed the college students while they are also taxpayers, the results from this study may not be generalized to a sample of subjects with larger variance in income, age, race, culture, and education background. However, these results should shed lights on the ordinary taxpayers’ behavior intention to use online tax software to prepare and file tax return. Second, as discussed in Malhotra et al. (2004) that information privacy is a complex phenomenon, this study might not consider all potential relevant privacy variables to examine taxpayers’ reactions to information privacy threats. Future research could survey the taxpayers in general with different background and income levels to investigate what factors affecting the taxpayers’ decisions to choose/not choose online tax software to file tax return. Another future research might be to test if behavior intention and actual behavior are different in terms of using online tax software to file tax return as shown in this study. Although behavioral intention is a reliable predictor of actual behavior (Ajzen, 1991), there are many unpredictable factors that may affect individual’s actual behavior. Future study could also investigate the interactions among factors affecting taxpayers’ privacy concerns. For example, scholars can investigate the interactions between perceived risk and different dimension of trust.
The authors have not declared any conflict of interests.
REFERENCES
|
Ajzen I (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes 50(2):179-211.
Crossref
|
|
Anderson JC, Gerbing DW (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin 103(3):411-423.
Crossref
|
|
|
|
|
Apostolou B, Dorminey JW, Schaupp LC (2016). Trust in tax software as an antecedent to intention to e-File. Journal of Forensic and Investigative Accounting 8(2):241-256.
|
|
|
|
|
Awad NF, Krishnan MS (2006). The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quarterly 30(1):13-28.
Crossref
|
|
|
|
|
Bansal G, Zahedi FM, Gefen D (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems 49(2):138-150.
Crossref
|
|
|
|
|
Belanger F, Hiller JS, Smith WJ (2002). Trustworthiness in Electronic Commerce: the role of privacy, security, and site attributes. Journal of Strategic Information Systems 11(3/4):245-270.
Crossref
|
|
|
|
|
Brink WD, Lee LS (2015). The effect of tax preparation software on tax compliance: a research note. Behavioral Research in Accounting 27(1):121-135.
Crossref
|
|
|
|
|
Byrne BM (1998). Structural Equation Modeling with Lisrel, Prelis, and Simplis: Basic Concepts, Applications and Programming, Lawrence Erlbaum Associates, Mahwah, New Jersey.
|
|
|
|
|
Chellappa RK (2008). Consumers' trust in electronic commerce transactions: the role of perceived privacy and perceived security. Emory University, Atlanta, GA. Unpublished.
|
|
|
|
|
Croson R, Gneezy U (2009). Gender differences in preferences. Journal of Economic Literature 47(2):448-474.
Crossref
|
|
|
|
|
Culnan MJ (1995). Consumer awareness of name removal procedures: Implications for direct marketing. Journal of Direct Marketing 9(2):10-19.
Crossref
|
|
|
|
|
Culnan MJ, Bies RJ (2003). Consumer privacy: Balancing economic and justice considerations. Journal of Social Issues 59(2):323-342.
Crossref
|
|
|
|
|
Dinev T, Hart P (2004). Internet privacy concerns and their antecedents - measurement validity and a regression model. Behavior and Information Technology 23(6):413-423.
Crossref
|
|
|
|
|
Dinev T, Hart P (2006). An extended privacy calculus model for E-Commerce transactions. Information Systems Research 17(1):61-80.
Crossref
|
|
|
|
|
Featherman MS, Pavlou PA (2003). Predicting E-service adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies 59(4):451-474.
Crossref
|
|
|
|
|
Gerbing DW, Anderson JC (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research 25(2):186-192.
Crossref
|
|
|
|
|
Hui KL, Teo HH, Lee SYT (2007). The value of privacy assurance: an exploratory field experiment. MIS Quarterly 31(1):19-33.
Crossref
|
|
|
|
|
Internal Revenue Service (2017). Filing Season Statistics for Week Ending September 1, 2017. Available at:
|
|
View
|
|
|
Li X, Santhanam R (2008). Will it be disclosure or fabrication of personal information? An examination of persuasion strategies on prospective employees. International Journal of Information Security and Privacy 2(4):91-109.
Crossref
|
|
|
|
|
Liu C, Marchewka JT, Lu J, Yu CS (2005). Beyond concern - a privacy-trust-behavioral intention model of electronic commerce. Information and Management 42(2):289-304.
Crossref
|
|
|
|
|
Malhotra NK, Kim SS, Agarwal J (2004). Internet users' information privacy concerns (IUIPC): the construct, the scale and a causal model. Information Systems Research 15(4):336-355.
Crossref
|
|
|
|
|
McKnight H, Choudhury V, Kacmar C (2002). Developing and validating trust measures for e-commerce: an integrative typology. Information Systems Research 13(3):334-359.
Crossref
|
|
|
|
|
McLeod A, Pippin S, Mason R (2008). Individual taxpayer intention to use tax preparation software: Examining experience, trust, and perceived risk. In Proceedings of ISOneWorld Conference.
|
|
|
|
|
Metzger MJ (2004). Privacy, trust, and disclosure: Exploring barriers to electronic commerce. Journal of Computer-Mediated Communication 9(4):JCMC942.
Crossref
|
|
|
|
|
Milberg SJ, Smith HJ, Burke SJ (2000). Information Privacy: Corporate Management and National Regulation. Organization Science 11(1): 35-57.
Crossref
|
|
|
|
|
Murray TD (2012). Many tax preparation software companies share your personal data. Available at:
|
|
View
|
|
|
Nahm AY, Vonderembse MA, Koufteros XA (2003). The impact of organizational structure on time-based manufacturing and plant performance. Journal of Operations Management 21(3):281-306.
Crossref
|
|
|
|
|
National Cyber Security Alliance (NCSA) (2016). U.S. consumer privacy index 2016. Available at:
|
|
View
|
|
|
Rencher AC (1995). Methods of multivariate analysis, John Wiley & Son. Inc. Publication, Canada. pp. 380-407.
|
|
|
|
|
Rowan M, Dehlinger J (2014). Observed gender difference in privacy concerns and behaviors of mobile device end users. Procedia Computer Science 37:340-347.
Crossref
|
|
|
|
|
Schaupp LC, Carter L, McBride ME (2010). E-file adoption: A study of U.S. taxpayers' intentions. Computers in Human Behavior 26(4):636-644.
Crossref
|
|
|
|
|
Schwartz P (2008). The future of tax privacy. National Tax Journal 61(4):883-900.
Crossref
|
|
|
|
|
Sheehan KB (2009). An investigation of gender differences in on-line privacy concerns and resultant behaviors. Journal of Interactive Marketing 13(4):24-38.
Crossref
|
|
|
|
|
Smith HJ, Dinev T, Xu H (2011). Information privacy research: an interdisciplinary review. MIS Quarterly 35(4):989-1015.
Crossref
|
|
|
|
|
Smith HJ, Milberg SJ, Burke SJ (1996). Information privacy: Measuring individuals' concerns about organizational practices. MIS Quarterly 20(2):167-196.
Crossref
|
|
|
|
|
Stewart KA, Segars AH (2002). An empirical examination of the concern for information privacy instrument. Information Systems Research 13(1):36-49.
Crossref
|
|
|
|
|
Sutton SG (2010). The fundamental role of technology in accounting: Researching reality. Advances in Accounting Behavioral Research 13:1-11.
Crossref
|
|
|
|
|
Thibaut J, Walker L (1975). Procedural justice: A psychological analysis. L. Erlbaum Associates.
|
|
|
|
|
|
|
Tyler TR (1994). Psychological models of the justice motive: antecedents of distributive and procedural justice. Journal of Personality and Social Psychology 67(5):850-863.
Crossref
|
|
|
Xu H, Teo HH, Tan BCY, Agarwal R (2010). The role of push-pull technology in privacy calculus: the case of location-based services. Journal of Management Information Systems 26(3):137-176.
Crossref
|
|
|
|
APPENDIX
Research Constructs and Measures (Seven-point scales anchored with strongly disagree and strongly agree)
Intention to Use Online Tax Software
(1) Specify the extent to which you would like to use the online tax software for tax return. A seven-point Likert scale ranging from 1 = very unlikely to 7 = very likely, and another one ranging from 1 = willingly to 7 = unwillingly were used.
Taxpayer Online Information Privacy Concern (adapted from Malhotra et al. (2004)).
Collection
(1) It usually bothers me when online tax software companies ask me for personal information.
(2) When online tax software companies ask me for personal information, I sometimes think twice before providing it.
(3) I’m concerned that online tax software companies are collecting too much personal information about me.
Control
(1) Taxpayer information privacy is really a matter of taxpayers’ right to exercise control and autonomy over decisions about how their information is collected, used, and shared.
(2) Taxpayer control of personal information lies at the heart of taxpayer privacy.
(3) I believe that taxpayer privacy is invaded when control is lost or unwillingly reduced as a result of disclosing information to unauthorized parties.
Awareness (of Privacy Practices):
(1) Online tax software companies seeking information should disclose the way the data are collected, processed, and used.
(2) A good privacy policy should have a clear and conspicuous disclosure.
(3) It is very important to me that I am aware and knowledgeable about how my personal information will be used (drop).
Perceived Risk (adapted from Xu et al. (2010))
(1) Providing the online tax software companies with my personal information would involve many unexpected problems.
(2) It would be risky to disclose my personal information to the online tax software companies.
(3) There would be high potential for loss in disclosing my personal information to the online tax software companies.
Trust (adapted from Modified based on McKnight et al. (2002) and Hui et al. (2007))
(1) I feel that people are generally trustworthy.
(2) I feel that people are generally reliable.
(3) I am comfortable making a purchase on the Internet.
(4) I am comfortable relying on the Internet since Internet vendors generally fulfill their agreements.
(5) I feel that most Internet vendors are competent at serving their customers.
(6) I feel confident that encryption and other technological advances on the Internet make it safe for me to do business there.
(7) In general, the Internet is a safe environment in which to transact business.
