EXPLORING BIG DATA CHALLENGES: FACTORS AFFECTING INDIVIDUALS’ INTENTION FOR AUTHORIZING THEIR NETWORK OPERATORS THE USAGE OF THEIR PERSONAL
INFORMATION
Christian Fernando Libaque Saenz, Information and Telecommunication Technology Program, Department of Management Science, Korea Advanced Institute of Science and Technology, Daejeon, South Korea, [email protected]
Younghoon Chang, Department of Management Science, Korea Advanced Institute of Science and Technology, Daejeon, South Korea, [email protected]
Jimin Kim, Department of Management Science, Korea Advanced Institute of Science and Technology, Daejeon, South Korea, [email protected]
Myeong-Cheol Park, Department of Management Science, Korea Advanced Institute of Science and Technology, Daejeon, South Korea, [email protected]
Abstract
We are living in an era where data means opportunities. New technologies have made it possible to collect and store huge amounts of data. The telecommunications sector, specifically Network Operators, is one of the industries that could benefit from these opportunities. By exploiting this asset, Network Operators could survive lost revenue due to the commoditization of traditional services by other players such as Google and Skype. However, according to information privacy laws, without the permission of their customers, Network Operators have many limitations in using data. In this light, the purpose of this study is to analyze the main factors that affect individuals’ intention to grant permission to their Network Operators to use their personal information, and to look at the differences between smartphone and non- smartphone users. We used a survey to measure “intention”, “privacy-related attitudes”, and “salient beliefs”; and received 475 responses. The results are expected to have both theoretical and managerial implications. Moreover, these results may suggest to managers which strategies they should focus on to encourage their customers to give permission to use their personal information.
Key Words: Privacy, data usage, personal information, Network Operators.
1 INTRODUCTION
We are entering a new era where data is everywhere. New technologies have been providing capabilities to capture and store huge amounts of data. For example, when the Sloan Digital Sky Survey was launched in 2000 in New Mexico, the data captured and stored in the first weeks by its telescope were greater in quantity than all the data that had been collected in the entire history of astronomy (Cukier 2010).
Big Data is a term related to this explosion of data. Even though there is not a concrete definition for Big Data, Manyika et al. (2011) refers to it as “data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.” Big Data has three main features: volume – huge data amount, velocity – speed data creation, and variety – variety of sources. However, experts have considered a fourth characteristic, value – providing valuable insights (DataStax 2011). Therefore, the huge amount of data we are creating every day through many sources may reveal trends in real time, which can improve decision-making (UN 2012). In the business field, improving decision-making could help organizations to enhance their performance in tasks such as minimizing risks and costs, and creating new products and services (McGuire et al. 2012). For example, Avanade (2012) conducted a survey of 569 high-level decision-makers in 18 countries, finding that 42% of the respondents’ companies have leveraged data to increase an existing revenue stream, while 31% have used it to create new sources.
Of course, not all industries have the capabilities to gain benefits from Big Data. Figure 1 shows the five potential industries that would benefit the most, one of them being the telecommunications industry, noting that Network Operators – telcos – have the capabilities to take advantage of this trend.
Figure 1. Big Data Opportunity Heat Map by Industry
Additionally, telcos are facing a hyper-competitive environment because of the commoditization of traditional services, and players such as Google and Skype eating into their revenues (Detica 2012). For instance, Green (2012) forecasted that by 2020 the telecom industry will have experienced a loss of US$ 479 billion in voice revenues due to over-the-top (OTT) VoIP players, while Dharia (2012) forecasted that by 2016 the loss in short message service (SMS) revenues will reach US$ 54 billion due to OTT social messaging players. Hence, telcos must find new revenue sources.
In short, telcos have an opportunity to overcome this trend of decreasing revenues by using data for business benefits (Sheina & Bali 2012). Nevertheless, there are challenges to overcome, privacy being the most sensitive issue for users (TRUSTe 2011) because we are dealing with personal information. In this sense, governments have attempted to protect individuals’ privacy by enacting laws or directives, which must be followed by all sectors, including the highly regulated telecom industry. For example, Directive
EC 95/461995, issued by the European Union, states that for a legitimate personal data1 process, unambiguous consent from customers is needed.
Author (Year) Research Topic
Culnan and Armstrong (1999) Disclosure of personal information in an electronic world.
Phelps et al. (2000) Privacy concerns to disclose personal information for direct marketing.
Malhotra et al. (2004) Measurement of internet user’s information privacy concerns.
Dinev and Hart (2006) Factors influencing willingness to disclose personal information in e-commerce.
Hui et al. (2006) Motivators for online information disclosure.
Norberg et al. (2007) Paradox between intention and behavior for information disclosure.
Lee (2009) Factors affecting adoption of m-banking (e.g., benefits and privacy).
Li et al. (2010) Privacy calculus in e-commerce.
Xu et al. (2011) Link between individual perceptions and Institutional Privacy Assurances.
Tan et al. (2012) Privacy concerns and social network sites (SNS).
Table 1. Literature Review
Table 1 shows some previous studies on privacy and willingness to disclose personal information. We can see that these studies focused on e-commerce, the Internet, or SNS, but no study has addressed the privacy calculus in the telecommunications sector. Moreover, the telecommunications sector differs from the mentioned domains in two features. First, telcos are already storing their customers’ personal information at the time they provide services; hence, we address the willingness to give permission to use this information rather than to disclose it2. Second, we can divide users into two groups: non-smartphone and smartphone users. The rationale is that the characteristics between them are remarkable in terms of data production. Indeed, non-smartphone users mainly access SMS and voice services, while smartphone users access a wider range of services such as banking, SNS, and entertainment. Thus, an extension is needed, considering the importance of the usage of personal information for telcos.
2 THEORETICAL FRAMEWORK
2.1 Theory of Reasoned Action (TRA)
Human behavior has been a widely studied field in social sciences. Many of these studies used TRA (Ajzen & Fishbein 1980) and its extended version, the theory of planned behavior (TPB) (Ajzen 1988) in a wide range of behaviors. Even though these theories present parsimonious frameworks, results have shown that these frameworks explain considerable variance in their dependent variables: intention to perform behaviors and behaviors themselves (Ajzen 1985; Ajzen 1991).
TRA focuses on volitional behaviors and thus is appropriate for this study. TRA postulates that a behavior is determined by an intention, while this intention is determined by attitude (ATT) and subjective norm (SN). ATT reflects the individuals’ positive or negative evaluation of the performance of the behavior, and SN refers to the influence from specific people’s expectations about the behavior. However, for a deeper understanding of the behavior, we should analyze the beliefs influencing ATT and SN: behavioral and normative beliefs, respectively. The former reflects the subjective probability that the behavior will produce a certain outcome, while the latter refers to the individuals’ beliefs that specific groups think they should or should not perform the behavior. These beliefs are not fixed and depend on the behavior.
1 Personal data is defined as “any information relating to an identified or identifiable natural person.”
2 Disclosure of information is a necessary precondition for the usage of it. The costs associated with the disclosure of personal information depend on the usage of this information, and not on the disclosure itself (Brandimarte et al. 2012).
In the case of ATT, Triandis (as cited in Davis 1989) argued that behavioral beliefs and ATT are co- determinants of intentions. Accordingly, we found studies supporting this research stream, which focused on the direct effect of behavioral beliefs on intention (Davis 1989; Dinev & Hart 2006). On the other hand, Ajzen (1991) concluded that personal considerations are the main contributors in predicting behavioral intention and their effect dwarfs the influence of perceived social pressure. For example, prior research found that SN has a weak and limited role in shaping intentions (White et al. 2009). Furthermore, some scholars deliberately dropped SN from their data analyses (e.g., Sparks et al. 1995).
Our research follows the course of these studies. Thus, we focused on behavioral beliefs, attitudes, and intention. Previous studies found that intention predicts behavior well (Ajzen 1991), and this relationship becomes particularly valuable when time lags are unpredictable (Krueger Jr et al. 2000), as in our case.
From a literature review, we identified privacy-related and inhibitor-related beliefs, and privacy-related attitudes ruling behaviors that threat personal information. Finally, we followed these two TRA rules: 1) the more favorable (unfavorable) the ATT, the stronger (weaker) the intention; and 2) the stronger the beliefs linking the behavior with positive (negative) outcomes, the more favorable (unfavorable) the ATT.
2.2 Privacy-related attitudes: Privacy concerns and trust
Prior studies in the privacy domain have addressed information privacy concerns as one of their main constructs (Culnan & Armstrong 1999; Dinev & Hart 2006). However, past research found contradictory results about the role of privacy concerns. For example, Chellappa and Sin (2005) found a significant direct effect of privacy concerns on behavioral intention, while Tan et al. (2012) found that privacy concerns have a moderator effect rather than a direct effect. Therefore, this research should attempt to shed light on the role of privacy concerns in a different sector, specifically, telecommunications.
Trust was found by previous research to be another important antecedent of behavioral intention in the privacy field. Indeed, results support a direct effect of trust on intention (Bart et al. 2005; McKnight et al.
2002). However, Bart et al. (2005) concluded that the strength of this relationship varies depending on the trustee’s characteristics. Hence, an analysis of this relationship in other sectors such as telcos is needed.
2.3 Privacy-related beliefs: Risks, benefits, control, awareness, and security
Perceived information risks and perceived benefits are also an important part of the privacy calculus. Past research shows that both of these beliefs are present during the decision process (Culnan & Armstrong 1999; Dinev & Hart 2006). Furthermore, this notion of calculus is consistent with Petronio’s (2002) communication privacy management (CPM) theory, which claims that disclosure has both benefits and risks, and a balance of these two variables is needed for deciding whether or not to disclose information.
In this sense, the telecommunications sector should not remain indifferent to this notion of calculus.
On the other hand, Westin (1967) defined information privacy as “the claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others.” Prior research has operationalized this variable through perceived control over information (Phelps et al. 2000; Malhotra et al. 2004). Even though the findings of these studies support the important role of perceived control on intention to disclose personal information, they are mostly focused on the commerce domain. Therefore, the present research attempts to explore the role of perceived control in a different environment, the telecom sector, taking into account CPM, which claims that people develop their privacy rules depending on the context.
Similarly, in their work about fair information practices, Culnan and Armstrong (1999) not only addressed the effect of control on individuals’ concerns, but also the effect of notice. Moreover, in a study about online disclosure of personal information, Hoffman et al. (1999) found that 69% of Web users did not provide personal information to websites because they were not aware of how their information would be used. These findings are consistent with Foxman and Kilcoyne’s (1993) argument about privacy’s dual
dimension, where control is the active dimension and awareness is the passive dimension. The latter refers to the degree of consumers’ awareness of organizational information privacy practices (Malhotra et al. 2004). Even though these studies agree with the claim that awareness may help to build trust, few of them have tried to quantify this effect. The present research attempts to provide clearer insights.
Another term associated with privacy is security. Even though most people use these two terms interchangeably, they are not the same. Privacy, as we mentioned before, is the individuals’ claim to control their information, while security is the protection of that information (Prescient 2002). The present study focuses on individuals’ perception of protection as a proxy measurement of security in the same manner than other scholars did by analyzing perceived information protection (Li et al. 2010).
2.4 Inhibitor-related beliefs: The role of resources
TPB complements TRA by adding perceived behavioral control (PBC) as a variable that captures controllability and self-efficacy in non-volitional behaviors. Even though we targeted our behavior under the TRA framework because of its volitional nature, we have special interest in the controllability dimension of PBC. Controllability refers to individuals’ judgments about the availability of resources to perform the behavior (Ajzen 2002). Principally, we will focus on time and effort, as they may be needed to perform the behavior under study. However, we use a different approach from controllability in TPB by addressing time and effort as personal investment; that is, we will focus on the role of the individuals’
beliefs about the resources needed to perform an action as a possible inhibitor to accomplish the behavior, regardless of the availability of these resources.
3 RESEARCH MODEL AND HYPOTHESES
Figure 2 shows our research model.
Figure 2. Research Model
3.1 Information Privacy Concerns (CON)
CON is the individuals’ level of anxiety regarding their information privacy (Lanier & Saini 2008).
According to Fagan et al. (2003), anxiety is a state of mind of discomfort in threatening scenarios. In addition, Allport (as cited in Wang & Pfister 2008) defined ATT as a state of mind as well. Thus, we posit that CON is an unfavorable ATT. In this sense, following TRA’s first rule, we hypothesize that CON has a direct negative effect on intention because the former reflects an unfavorable evaluation of the performance of the behavior. This statement is consistent with previous findings (Chellappa & Sin 2005;
Dinev & Hart 2006), and consistent with Vroom’s (1964) expectancy theory, which postulates that individuals tend to minimize negative outcomes such as anxiety.
Hypothesis 1: CON negatively influences intention.
3.2 Trust (TRU)
TRU is the individuals’ perceptions that telcos will act accordingly to the former’s expectations (Pavlou
& Fygenson 2006). Trust literature posits that TRU has two elements: cognitive (perceptions) and emotional (attitude) (Komiak & Benbasat 2006). Accordingly, Jones (1996) approached TRU as the trustors’ attitude of optimism about the trustees’ goodwill. Thus, we claim that TRU is a favorable ATT.
Following TRA’s first rule, we hypothesize that TRU has a direct positive effect on intention because the former reflects a favorable evaluation of the performance of the behavior. This hypothesis is consistent with previous research supporting this relationship (Dinev & Hart 2006; McKnight et al. 2002).
Hypothesis 2: TRU positively influences intention.
3.3 Perceived information risks (PIR)
PIR is the individuals’ beliefs of potential loss associated with giving permission to their telcos to use their personal information (Malhotra et al. 2004). This variable is closely related to CON. However, PIR refers to the apprehension of possible loss, while CON refers to the internalization of this possibility of loss (Dinev & Hart 2006).
As we mention in section 3.1, CON is defined as the level of anxiety regarding privacy in threatening scenarios. Thus, we claim that high PIR may lead individuals to have high levels of CON, and vice versa for low PIR. This statement is consistent with TRA’s second rule, and consistent with risk literature, which postulates that perception of risks can lead individuals to a state of unpleasant feelings (Dowling
& Staelin 1994). In other words, a positive effect of PIR on CON is expected (Dinev & Hart 2006).
Hypothesis 3: PIR positively influences CON.
On the other hand, Mayer et al. (1995) defined TRU as the willingness to take risks. Therefore, in a threatening scenario it is expected that as the individuals’ perceptions of risks rise, their intentions to take these risks (i.e., trust) decline. Following this direction, as past studies did, we hypothesize a negative direct effect of PIR on TRU (Dinev & Hart 2006), which is consistent with TRA’s second rule.
Hypothesis 4: PIR negatively influences TRU.
3.4 Perceived data control (PDC)
PDC operationalizes subjective privacy. PDC is the individuals’ beliefs in their ability to manage the usage of their personal information (Westin 1967). Prior research suggests that giving control to individuals over personal information mitigates CON because control loads individuals with expectations that negative outcomes will not occur (Culnan & Armstrong 1999; Malhotra et al. 2004). Following these findings, we postulate that the individuals’ PDC has a negative effect on CON. This hypothesis is consistent with TRA’s second rule, and consistent with psychology literature, which suggests that a lack of perceived control can lead individuals to have anxious feelings (for a review see Rapee et al. 1996).
Hypothesis 5: PDC negatively influences CON.
On the other hand, Brandimarte et al. (2012) claimed that the individuals’ intention to take risks increases when they feel in control. As we mentioned in section 3.3, Mayer et al. (1995) defined TRU as the willingness to take risks. Consequently, we postulate that TRU increases when PDC do so, and vice versa
when the latter decreases. This hypothesized positive effect of PDC on TRU has support in previous research findings (Das & Teng 2001; Joinson et al. 2010), and is consistent with TRA rules.
Hypothesis 6: PDC positively influences TRU.
3.5 Perceived Information Protection (PIP)
PIP is the individuals’ perceptions that telcos have the ability to safeguard their personal information from security breaches (Pavlou & Fygenson 2006). Indeed, individuals have no power to protect their information stored by telcos. In this sense, Yamaguchi (as cited in Xu 2007) claimed that in such cases where individuals lack power, they are motivated to rely on proxy control. Proxy control is the individuals’ belief that powerful others have the ability to achieve the former’s desired outcomes. Thus, PIP and proxy control are similar concepts. Furthermore, Xu (2007) found that individuals use proxy control to alleviate their CON, which is consistent with TRA’s second rule. Hence, as we did in section 3.4, here we found support in psychology literature to claim that, regardless the control agent (i.e., self or others), the lower the perception of control, the higher the CON (i.e., negative effect of PIP on CON).
Hypothesis 7: PIP negatively influences CON.
Moreover, Gefen et al. (2003) claimed that trust is a three-dimensional construct, made up of competence, integrity, and benevolence. We are interested in competence, which is the trustor’s perception that the trustee has the ability to perform as the former’s expectations (Pavlou & Fygenson 2006). Accordingly, we hypothesize a positive effect of PIP on TRU, as previous research findings suggest (Chellappa &
Pavlou 2002; Liu et al. 2004). The rationale is that individuals build their TRU based on the telcos’ ability to keep information secure (i.e., PIP). This statement is consistent with TRA’s second rule.
Hypothesis 8: PIP positively influences TRU.
3.6 Perceived Policy Awareness (PPA)
PPA is the individuals’ perceptions of their understanding about telcos’ privacy practices (Malhotra et al.
2004). As we explained in section 2.3, this variable is a passive dimension of control. In addition, literature about institutional privacy assurances posits that privacy policies influence decisions about disclosure of personal information (Xu et al. 2011). The rationale is that privacy policies load individuals with proxy control over their personal information because customers attempt to align themselves with this powerful force (Xu 2007). Then, as we did in sections 3.4 and 3.5, following psychology literature, we hypothesize a negative effect of PPA (i.e., proxy control) on CON, which is consistent with TRA’s second rule, and consistent with previous findings (Culnan & Armstrong 1999; Malhotra et al. 2004).
Hypothesis 8: PPA negatively influences CON.
Also, privacy policies may push companies to refrain from opportunistic behavior (Xu 2007). In other words, privacy policies may load individuals with beliefs that telcos will act accordingly to the former’s expectations – fulfilling the concept of TRU. Consequently, we hypothesize a positive effect of PPA on TRU. This hypothesis is supported by prior research findings (Liu et al. 2004; Milne & Boza 1999), and consistent with TRA’s second rule.
Hypothesis 9: PPA positively influences TRU.
3.7 Perceived Monetary Benefits (PMB) and Perceived Non-Monetary Benefits (PNB)
Individuals may be willing to engage in threatening behaviors if they can obtain benefits from them (Culnan & Armstrong 1999); that is, perceived benefits could work as facilitators (Hui et al. 2006). In fact, perceived benefits are beliefs linking a behavior with positive outcomes. In this case, we follow the approach described in section 2.1 about beliefs and ATT as co-determinants of intention, in the same
fashion than other scholars did (Lee 2009). Then, by following TRA’s rules, we posit that perceived benefits have a positive effect on intention. This hypothesis is consistent with expectancy theory, which claims that individuals tend to maximize positive outcomes. We divided benefits into PMB and PNB.
Hypothesis 11 (Hypothesis 12): PMB (PNB) positively influences intention.
3.8 Perceived Personal Investment (INV)
INV is the individuals’ beliefs about the resources they will need to give permission to their telcos to use their personal information (adapted from Ajzen 2002). Economics theories approach resources as learning costs (before action) that could become sunk costs (after action). The former refers to the perceived resources that will be expended in performing an action, while the latter refers to the perceptions of non- recoverable resources invested in performing an action (Jones et al. 2002). Hence, INV is learning costs of performing the behavior under study. Following this economic approach, cost-benefit models suggest a negative effect of perceived costs on intention (Frazier 1983), which is consistent with TRA’s rules. Thus, we hypothesize that INV has a negative direct effect on intention.
Hypothesis 13: INV (time and effort) negatively influences intention.
4 RESEARCH METHODOLOGY
The survey method will be used in this research. All measurement items were drawn from a literature review and adapted to the present research. A refinement procedure was done based on a pilot test, which consisted of 30 preliminary samples from mobile users at KAIST. All constructs were measured on a seven-point Likert scale. We used smart PLS as a tool for checking reliability and validity measures. For the full scale test, we collected 475 responses from actual mobile users in Korea. We used an online panel company (Wise Research Co.) to collect the samples. The respondents were assured that the data would be collected and results reported with their anonymity protected. The full scale sample was composed of 240 males (50.5%) and 235 females (49.5%). The age of respondents was almost equally distributed, and the respondents’ mobile carriers represented the current market share of the Korean mobile market. We are still analyzing the results and they will be presented in future studies.
5 IMPLICATIONS (EXPECTED)
5.1 Implications for Theory and Research
Above all, this study pioneers a privacy calculus in the telecom sector. We provide a comprehensive model that describes the privacy calculus manipulated by individuals who decide to give telcos permission to access their personal information usage. Moreover, a comparison between smartphone and non-smartphone users will be provided. Second, as it entails an application of TRA, the present research establishes beliefs and attitudes that could be adapted to related behavior studies. Moreover, the findings from our study can be referenced by other countries that have similar phenomena. Third, as a contribution to privacy studies, we expect to clarify the effect of privacy concerns on intention, validate the role of trust and control in a new environment, quantify the effect of policy awareness on trust, and shed light on the role of resources from a learning-cost perspective.
5.2 Implications for Practice
The proposed model shows a set of factors that managers in the telecom sector might manipulate to encourage their customers to authorize the usage of their personal information. Therefore, managers should focus on the significant factors that have a positive relationship with intention. By adapting the factors into their strategies, telcos will be equipped with a valuable asset in a Big Data era.
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