Technology and Health Care 22 (2014) 515–529 DOI 10.3233/THC-140811 IOS Press

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Nonlinearities in personalization-privacy paradox in mHealth adoption: The mediating role of perceived usefulness and attitude Xiaofei Zhanga,b, Xitong Guoa,∗ , Feng Guoa and Kee-Hung Laib a School

of Management, Harbin Institute of Technology, Harbin, Heilongjiang, China of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China b Department

Received 28 September 2013 Accepted 12 March 2014 Abstract. BACKGROUND AND OBJECTIVE: Personalization in healthcare refers to individualizing services and products based on patients’ health conditions and interests. In order to deliver highly personalized offerings, mHealth providers need to use patients’ health information, which provokes patients’ concerns over personal health information leakage. So the personalizationprivacy paradox is an important issue in the mHealth context. To gain a better understanding of this paradox, we take the personalization and privacy paradox factors as independent variables, incorporating the nonlinear relationships between personalization and privacy, and take attitude and perceived usefulness as middle variables to study mHealth adoption. METHODS: The hypothesized model is tested through an empirical research of a 489-respondent sample in China. PLS is used for data analysis. KEY FINDINGS: (1) Personalization and privacy are found to influence mHealth adoption intention via attitude and perceived usefulness; (2) there is a substitution relationship, also called negative synergy between personalization and privacy in mHealth contexts; (3) attitude mediates the effect of perceived usefulness on intention, indicating a significant role of attitude. Keywords: mHealth, personalization, privacy, attitude, TAM, nonlinear modeling

1. Introduction Technology is a double-edged sword, especially the technologies applied in healthcare. The application of information and communication technologies (ICT) to support healthcare is called eHealth [1]. The use of mobile devices expanded the scope of eHealth systems to give us mHealth [2], which changed the way we access health information and made healthcare portable and prompt [3]. In recent years, more and more people have started to use mHealth to get health information and services [4], which has fueled debate and controversy about health information privacy. By analyzing and delivering health data, mHealth can improve healthcare quality and patient’s safety, which reduces healthcare time and ∗

Corresponding author: Xitong Guo, School of Management, Harbin Institute of Technology, 92 West Dazhi Street, Nan Gang District, Harbin 15001, Heilongjiang, China. Tel.: +86 0451 86424022; Fax: +86 0451 86414024; E-mail: xitongguo@ gmail.com. c 2014 – IOS Press and the authors. All rights reserved 0928-7329/14/$27.50 

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costs, but sharing patient’s health data in a large distributed and diverse environment introduces privacy risks [5]. Health information privacy is a big challenge in eHealth development [6], and failure to gain a better understanding of this problem before possible technology solutions are available would be one of the main obstacles in eHealth generalization. eHealth personalization can help service providers develop strong relationships with users and extend server time [7]. Using mobile technologies in healthcare has made it possible for mHealth service providers to deliver different kinds of services and products to their consumers based on the patient’s diseases, demographic characteristics, life experiences and needs. Personalized services are based on patient’s personal health data [7]. So the more health information received, the better services are offered. On the other hand, the more health information a provider receives, the more concerned the patients become regarding privacy issues. This phenomenon has been referred to as “personalization and privacy paradox” [8]. Thus, it has turned into an serious issue for mHealth practitioners and researchers to reduce privacy concern for users and achieve maximum personal information, yet, very little research has been conducted regarding the paradox in mHealth contexts [9]. In previous research, scholars found out that when incorporating the nonlinear relationships between the key constructs, the model predicts behavior better [10–13]. Omitting the nonlinear relation from the prediction model tends to underestimate or overestimate the model’s performance, resulting in inaccurate, partial or incomplete interpretations [14]. While the nonlinearities between personalization and privacy were seldom observed [15], especially in the mHealth context. More importantly, on the dependent variable in previous IS-research almost every two constructs of which the nonlinearities were tested had a synergistic (positive or negative) effect, while the two constructs in our research are antagonism. By analyzing the nonlinear relationship between a positive and a negative variable, we extend the nonlinearities theory. In this study, we assume that the “personalization and privacy paradox” related variables could influence user’s behavior intention on whether to adopt mHealth services or not. We take TAM as our basic model. TAM has been widely used to explain user adoption of new technologies and “come to be one of the most widely used models in IS, in part because of its understandability and simplicity” [16]. Attitude is an important variable to consider in explaining usage behavior, which is a broad consensus by both scholars and practitioners [17]. Previous researches have separately studied attitude and preserved usefulness and found out that they could mediate the effect of personalization and privacy [15,18]. We take preserved usefulness and attitude as middle variables in our model. Moreover, we studied the relationship between the two mediating effects to obtain a better understanding of our model, which in turn lead to a different conclusion from previous results. The remainder of the paper is organized as follows. Section II elaborates the theoretical foundations and theoretical model proposed of this paper. Section III provides the details of our research methodology, followed by section IV, discussions of the results and implications. Section five summarizes our research work. 2. Literature review and research model 2.1. Mobile health services (mHealth) As a type of eHealth service, mHealth mainly delivers health services through mobile devices [19]. There are many definitions of mHealth. We introduced the one made by Robert et al. (2006) which is widely accepted. mHealth refers to “emerging mobile communications and network technologies for

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Author and year Hung and Jen (2012) [26]

Table 1 Previous studies about mHealth Research Analyzed sample Factors setting size (N) Mobile Health Management 170 Perceived Usefulness (PU), Services Perceived Ease of Use (PEOU), Attitude(ATT)

517

Theory basis TAM Model

Yang and Wang Asthma Care Mobile Service (2012) [27] (ACMS)

700

Subjective Norm, Innovativeness, TAM, TRA, and Managerial Support PEOU, PU, IAM model ATT

Akter et al. (2011) [28]

Mobile Health Information System

216

Trustworthiness, Consumer trust

TRA Model

Cocosila et al. (2010) [29]

Mobile Health

52

Perceived Overall Risk (POR), Intrinsic Motivation (IM), Intrinsic Motivation (IM)

Motivational Model (MM)

healthcare systems” [20]. The usage of mobile technologies in healthcare has led to the development of mHealth [21]. mHealth mainly integrates mobile computing, medical sensors, and portable devices to ensure healthcare [22]. As a type of eHealth, mHealth has many advantages over eHealth due to the characteristics of mobile devices, such as portability, mobility and ubiquity, which enables more personalization and generalization to mHealth services [23]. mHealth systems could generally be classified into 5 groups: communication infrastructure, device type, data display, application purpose and application domain [24]. In terms of these system categories, mHealth services can also be classified into five types according to V.W. Consulting: health information retrieval, remote reservation, remote diagnosis, electronic medical records access and health consultation [25]. As mHealth is a new emerging phenomenon, empirical studies on this issue are still rare. Table 1 shows various researches about mHealth adoption. 2.2. Technology acceptance model (TAM) Customer behavior is mainly affected by behavior intention, and in return, their attitude influence behavior intention [30]. Two main factors influence this: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) [31]. PU refers to the extent to which a potential user believes that the use of such technology would improve his performance and PEOU means to the extent to which a potential user thinks that it is easy for him to use such technology. Perceived usefulness and perceived ease of use both affect usage intention [30]. We also reviewed some TAM related researches in healthcare. Hu et al. were one of the first to introduce TAM to healthcare. They examined the model in physicians’ telemedicine acceptance [32]. Yarbrough and Smith studied electronic medical record acceptance among physicians via a model incorporating attitude in TAM [33]. Guo et al. studied the barriers for elderly adopting mHealth by investigating the inbibitional antecedents of PU and PEOU [34]. Holeden and Karsh reviewed the TAM in health context and came to a conclusion that TAM is an important theoretical tool for health research, but there was much room for improvement [35]. According to the classic theory of TAM, we proposed the hypotheses below. H1: PEOU will positively affect the intention to adopt mHealth. H2: PU will positively affect the intention to adopt mHealth. H3: PEOU will positive affect PU of mHealth services.

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2.3. Attitude Much evidence suggests that consumers often hesitate to use or buy internet-based services mainly due to the uncertainty of the virtual service provider’s behavior or private information leakage [36]. Attitude plays an important role in helping customers care less about such concerns. To put it simply, Attitudes (ATT) are referred to a potential user’s positive or negative to evaluate the adoption of a new technology [35]. Although many scholars tested behavior intention as a dependent variable in the TAM, they also argued that attitude was an important factor influencing the potential user’s adoption of a new technology [37,38]. Previous scholars studied the relationship between attitude and intention to use a new application and discovered that attitude was related to behavior intention. Drennan et al. (2005) found that people’s attitude had positive effects on the intention to adopt online learning portals [39]; the study of Sivo et al. (2007) indicated users’ intention to use the course management system was positively related to users’ attitude [40]. Some researchers also explored the attitude-intention relation in eHealth areas. Hung et al. (2013) studied the applications of the primary health information system (PHIS) and showed that the more positive attitude one potential user have towards PHIS, the more likely he or she would have the intention to adopt the PHIS [41]. mHealth as one form of eHealth is a new application, so the attitude to mHealth is positively linked to the intention to adopt mHealth. Thus, we propose the hypothesis below: H4: ATTITUDE will positively affect the intention to adopt mHealth. Perceived usefulness refers to a potential user believes that the use of the new technology would improve his performance to some extent [30], but whether people adopt a new application depends on their attitudes [38]. People’s attitude to use a new application is related to the extent to which they believe it would improve their performance [41]. In other words, the new application should be time saving, efficient and accurate. For instance, the study of Selim (2003) showed that the impact of students’ attitude to use the course website was that the students should feel the course website was effective and could improve performance [42]. According to Teo et al. (2011), a particular technology enhancing the performance of an individual had significant influence on his or her attitude to use the technology [43]. mHealth as a new application should be time saving and improve work performance, so that it will induce people’s positive attitude to use it. Thus, we propose the hypothesis below: H5: PU will positively affect Attitude. 2.4. Personalization-privacy paradox 2.4.1. Personalization (PSN) Understanding and responding to the customers’ needs are becoming more important for product and service providers [44]. Adomavicius and Tuzhilin defined personalization as “the ability to provide content and services that are tailored to individuals based on knowledge about their preferences and behaviors” [45]. In mHealth, personalization refers to tailoring and recommending health services according to specific patients and diseases [15]. Personalization can be realized in three mHealth functions: (1) providing health services based on users and their preferences; (2) making health information real time accessible, and reminding users of diagnosis or treatment; (3) online consultation, a patient can consult his doctor through a mobile phone and get the doctor’s advice. Personalization will affect a user’s belief about mHealth. By gathering and analyzing users’ health data to articulate the users’ actual needs, mHealth can achieve a higher personalization. The potential

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variability of the health cases is very high in terms of patient conditions, so personalization is badly needed. Personalized mHealth services can meet varied health needs, which will make users believe that the mHealth services are useful for their health [15]. If a company provides personalized mHealth services, it gives the impression of competence and conscientiousness to potential users, thus potential users would have a positive attitude on the services [9]. Thus, we propose the hypotheses below: H6 Personalization will positively affect PU. H7 Personalization will positively affect Attitude. 2.4.2. Privacy concerns (PRVC) Although personalization can meet users’ needs to a certain extent, it requires users to disclose personal health information, and this may provoke users’ concerns over personal health information leakage. Privacy is defined as “the moral right of individuals to be left alone, free from surveillance or interference from other individuals or organizations, including the state” [46]. Privacy concerns were first raised by Smith, according to whom privacy concerns mainly came from four aspects: concerns about the information collection process, concerns about unauthorized usages, concerns about possible errors produced in the storage, concerns about illegal access to the sensitive data [47]. Many empirical evidences show that privacy concerns can directly or indirectly influence behavior intention in the internet-based environment [48]. Privacy concerns manifest in a lack of understanding of the service provider, especially of its capability, then provokes user concerns about information leakage and irrational usage. Only through the promise of quality service and protecting customers’ private information, can service providers release the privacy concerns and enhance their usage intention [15]. In the mHealth context, if a user has a high concern about privacy, he would be cautious about health information sharing and would rather go to the real hospital for help. So he may think that mHealth is useless for him. On the other hand, a user’s concerns about privacy will influence how the user perceives the provider in the situation where the provider requests personal information. He would believe the provider requires too much information to provide specific services regardless. Then he would believe the provider is incompetent and irresponsible and have a negative attitude towards the provider and its services. Thus, we propose the hypotheses below: H8: Privacy concerns will positively affect PU. H9: Privacy concerns will positively affect Attitude. 2.5. The nonlinear relationships between PSN and PRVC As social science is developing, the relationships of variables have become more complex and nonlinear [11]. Although some scholars argued that nonlinear relationships might be more important than we thought, the nonlinear relationships continue to go relatively unexplored [49]. Meanwhile, omitting nonlinear effects in research may understate or overstate the main effects and result in incomplete explanations. As the research moves along, the study of nonlinear relationships has attracted the attention of many scholars over the past several decades [50]. The nonlinear relationships between key constructs have been observed in IS and non-IS research, for example, Ajzen examined the nonlinear relationships in TRA and TPB model [51]; Terry et al. explored the nonlinear relationship between group norms and attitudes in Behavioral Decision-Making [52].

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One of the common methods of studying nonlinear relationships is the interaction method [12]. Assume that X1 , X2 are influencing factors ofY . Considering their nonlinear relationship, the function can be written as: Y = β0 + β1 X1 + β2 X2 + β3 X1 ∗ X2 + ξ

When β3 > 0, X1 and X2 are complements; when β3 < 0, X1 and X2 are substitutes; when β3 = 0, X1 and X2 are independent. Most IS scholars studied the nonlinear relationships following the method above and they took it for granted and believed that the two factors were synergetic. However sometimes the impact factors are not synergistic [53]. According to the previous studies, we extend the method above to antagonistic factors as follows: Assume that X1 , X2 are antagonistic factors of Y . Considering their nonlinear relationship, the function can be written as: Y = β0 + β1 X1 + β2 X2 + β3 X1 ∗ X2 + ξ

When β3 > 0, X1 and X2 are substitutes; when β3 < 0, X1 and X2 are complements; when β3 = 0, X1 and X2 are independent. As far as we know, the nonlinearities between personalization and privacy concerns are seldom studied. In the study of personalization-privacy paradox, Lee and Cranage found that the combination of high personalization with high privacy assurance will lead to a higher positive consumer perceived usefulness [15]. As the perception towards mHealth services is voluntary, so if someone believes that mHealth can adequately meet his personalized needs, the changes of privacy concerns may slightly influence his attitude and perceived usefulness. In return, if someone harbors concerns about information leakage when using mHealth, the changes of personalization may also slightly influence attitude and perceived usefulness. So we propose their combined influence and that they can be substitutes, which is called Edgeworth-Pareto substitutability [12,54]. That is to say, when the influence of personalization increases, the marginal effect of privacy concerns will be reduced, and vice versa. Thus, we propose the hypotheses below: H10: The nonlinearities between PSN and PRVC will positively affect PU, indicating substitutability. H11: The nonlinearities between PSN and PRVC will positively affect ATTITUDE, indicating substitutability. To sum up, this paper was designed to: (1) Gain a better understanding of personalization and privacy paradox in mHealth adoption. (2) Enrich the theory of nonlinear interaction between two antagonistic variables. (3) Take the nonlinear interaction into personalization and privacy paradox to discover the interaction of personalization and privacy. (4) The mediating role of attitude and perceived usefulness between personalization and privacy and behavior intention were both tested [15,55], but the impacts of perceived usefulness on behavior intention sometimes is mediated by attitude [41]. Therefore, we conducted this research to discover the influencing mechanism of personalization and privacy on behavior intention. Figure 1 presents the proposed model of our research. 3. Methodology 3.1. Measures model To test the hypotheses, we conducted a questionnaire to collect data in a field survey of consumers of a company delivering health services to different people in Heilongjiang, China. This company plans

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Table 2 Respondent demographics Frequency

Percentage

Gender Female Male

219 270

44.8% 55.2%

Age  30 30 ∼ 60  60

299 119 71

61.1% 24.3% 14.5%

Mobile device usage experience < 4 years 4–8 >8

188 177 124

38.4% 36.2% 25.4%

Fig. 1. Proposed model.

to provide mHealth service and regards its existing customers as potential users. Before distributing the questionnaires in the company, respondents are introduced to mHealth services in order to help them gain a better understanding of the personalization services. Construct measures were adopted from previous researches with all items assessed on 5-point Likert-type scales (see Appendix A). We sent out 510 questionnaires, and incentives (10 eggs) were provided to those who submitted the accomplished survey to encourage participation. 491 valid questionnaires were collected after removing the invalid ones. Table 2 presents the demographic information of our respondents. As shown in Tables 3, 4 and 5, a preliminary assessment of the survey instrument was conducted by partial least squares (PLS). As presented in Tables 3 and 4, the composite reliabilities (CR) are greater than 0.90, average variances extracted (AVE) are greater than 0.70 and Cronbach’s α are greater than 0.85, highly above the suggested cut-off values of 0.70, 0.50 and 0.70 [56,57]. Factor loadings are significant and higher than 0.70, cross loadings are much lower than factor loadings. All these suggest good construct reliability and convergent validity. According to Fornell et al., the square root of AVEs and the correlations can assess the discriminate validity [58]. As shown in Table 5, the square roots of average variances extracted (AVE) were bigger than 0.80 and bigger than the correlations. Therefore, the discriminate validity is reasonable. Thus, the measurement model has been tested and found to be reliable and valid.

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X. Zhang et al. / Nonlinearities in personalization-privacy paradox in mHealth adoption Table 3 Convergent validity and internal consistency Constructs AI

# of items 3

Cronbach’s alpha 0.886

Composite reliability 0.929

AVE 0.814

Loadings (t-value) 0.890 (50.4) 0.905 (67.8) 0.911 (61.8)

PEOU

4

0.868

0.909

0.715

0.831 (41.3) 0.863 (46.2) 0.856 (50.5) 0.830 (40.5)

PU

4

0.900

0.930

0.770

0.868 (48.4) 0.892 (61.4) 0.895 (66.6) 0.853 (43.3)

ATT

4

0.919

0.943

0.805

0.881 (60.6) 0.913 (80.6) 0.913 (88.5) 0.911 (57.7)

PRVC

3

0.879

0.924

0.802

0.899 (41.0) 0.882 (27.0) 0.905 (41.9)

PSN

3

0.884

0.928

0.812

0.905 (71.9) 0.910 (55.5) 0.888 (58.0)

Note 2: when |t value| > 2.58, P < 0.01; when |t value| > 1.96, P < 0.05. Table 4 Item loading and cross-loading AI1 AI2 AI3 PEOU1 PEOU2 PEOU3 PEOU4 PU1 PU2 PU3 PU4 ATT1 ATT2 ATT3 ATT4 PRVC1 PRVC2 PRVC3 PSN1 PSN2 PSN3

AI 0.890 0.905 0.911 0.335 0.344 0.448 0.406 0.454 0.487 0.496 0.466 0.452 0.427 0.550 0.506 −0.219 −0.213 −0.225 0.418 0.409 0.351

PEOU 0.397 0.444 0.402 0.832 0.863 0.857 0.830 0.538 0.479 0.445 0.474 0.443 0.478 0.475 0.437 −0.074 −0.044 −0.065 0.309 0.319 0.282

PU 0.506 0.489 0.473 0.399 0.420 0.527 0.490 0.868 0.892 0.895 0.853 0.618 0.646 0.609 0.581 −0.250 −0.180 −0.204 0.499 0.478 0.444

ATT 0.482 0.495 0.485 0.365 0.377 0.502 0.455 0.615 0.600 0.587 0.597 0.881 0.913 0.913 0.882 −0.221 −0.128 −0.183 0.517 0.476 0.504

PRVC −0.207 −0.232 −0.223 −0.048 −0.027 −0.044 −0.113 −0.202 −0.210 −0.207 −0.225 −0.115 −0.176 −0.214 −0.226 0.899 0.882 0.905 −0.193 −0.189 −0.186

PSN 0.384 0.400 0.396 0.230 0.243 0.351 0.293 0.450 0.462 0.491 0.442 0.510 0.503 0.498 0.479 −0.195 −0.171 −0.194 0.905 0.910 0.888

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Table 5 Construct correlation and discriminate PEOU PU ATT

AI PRVC PSN AI 0.902 PEOU 0.460 0.846 PU 0.543 0.550 0.877 ATT 0.540 0.511 −0.23 0.897 PRVC −0.245 −0.070 −0.241 −0.204 0.896 PSN 0.436 0.337 0.526 0.554 −0.210 0.901 Notes: Diagonal elements are the square roots of AVEs and off-diagonal elements are correlations. Table 6 PLS results 2 Path PRVC→ATT PSN→ATT PP→ATT PRVC→PU PSN→PU PEOU→PU PP→PU PU→ATT ATT→AI PU→AI PEOU→AI R Square ATT PU AI

β −0.093 0.534

M1 t 1.661 9.761

−0.136 0.355 0.422

2.929 6.365 7.691

0.276 0.256 0.179

3.665 3.601 2.485 0.315 0.452 0.370

Sig. ∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

∗∗∗ ∗∗∗ ∗∗

β −0.031 −0.290 0.933 −0.016 −1.077 −0.160 0.841 0.273 0.258 0.178

M2 t 0.584 2.303 8.976 0.522 10.59 3.189 20.2 3.437 3.450 2.552 0.480 0.794 0.370

Sig. NS ∗∗

∗∗∗

NS

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗

β −0.017 0.116 0.224 −0.016 −1.073 −0.158 0.835 0.422 0.273 0.258 0.178

M3 t 0.343 0.937 1.243 0.525 10.89 2.999 20.10 3.934 3.472 3.506 2.532 0.523 0.784 0.369

Sig. NS NS NS NS ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗

Note 1: PP: PRVC*PSN. Note 2: when |t value| > 2.58, P < 0.01; when |t value| > 1.96, P < 0.05; when |t value| > 1.645, P < 0.1. Note 3: ∗∗∗ p < 0.01; ∗∗ p < 0.05; ∗ p < 0.01; NS: not significant.

3.2. Structural model Our structural model was also tested by PLS. The model analysis is conducted in a three stage criteria. At the first stage, we tested the basic model (M1) without the nonlinearities and the path from PU to ATT. M2 included the nonlinearities. And then M3 included the path from PU to ATT compared to M2. The results are shown in Table 6. The results show that in M1 the relationships between perceived usefulness and adoption intention (β = 0.256, t = 3.601), perceived ease of use and adoption intention (β = 0.179, t = 2.485), and perceived ease of use and perceived usefulness (β = 0.422, t = 7.691) are significant. That is to say, H1-3 are supported, so the basic assumptions of TAM are tested. The relationships between privacy and attitude (β = −0.093, t = 1.661), personalization and attitude (β = 0.534, t = 9.761), privacy and perceived usefulness (β = −0.136, t = 2.929), personalization and perceived usefulness (β = 0.355, t = 6.365), and attitude and adoption intention (β = 0.276, t = 3.665) are significant, leading to support H4 and H6-9. In M2 the relationships between PP and perceived usefulness (β = 0.841, t = 20.2), PP and attitude (β = 0.933, t = 8.976) are significant, leading to support H10 and H11. In M3 the relationship between perceived usefulness and attitude (β = 0.497, t = 3.784) is significant, leading to support H5. A summary of all the hypotheses tests are provided in Table 7.

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X. Zhang et al. / Nonlinearities in personalization-privacy paradox in mHealth adoption Table 7 Summary of findings

H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11

Description PEOU will positively affect the intention to adopt mHealth. PU will positively affect the intention to adopt mHealth. PEOU will positive affect PU of mHealth services. Attitude will positively affect the intention to adopt mHealth. Attitude will positively affect PU. Personalization will positively affect PU. Personalization will positively affect Attitude. Privacy concerns will positively affect PU. Privacy concerns will positively affect Attitude. The nonlinearities between PSN and PRVC will positively affect PU, indicating substitutability. The nonlinearities between PSN and PRVC will positively affect Attitude, indicating substitutability.

Tested model M1 M1 M1 M1 M3 M1 M1 M1 M1 M2

Result Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported

M2

Supported

4. Discussion This study aims to investigate how personalization and privacy influence mHealth adoption. Furthermore, it examines the roles of the two middle variables: perceived usefulness and attitude. 4.1. Key findings Four key findings can be concluded from the study. First, personalization is found to significantly influence perceived usefulness and attitude. Thus, personalization can indirectly influence adoption intention via the enablers [9,15]. Privacy is also found to significantly influence perceived usefulness and attitude in a negative way. Thus, the inhibitor privacy can indirectly influence adoption intention via the enablers [9,15]. Second, when comparing M2 to M1, we can come to a conclusion that when incorporating the nonlinearities between personalization and privacy, the nonlinear model explains a significantly greater proportion of the variance than the linear model (45.2% vs. 79.4% for perceived usefulness, 31.5% vs. 48.0% for attitude), indicating that the nonlinear model predicts behavior better [12]. Third, the nonlinearities between personalization and privacy are found to significantly influence perceived usefulness and attitude, suggesting that the Edgeworth-Pareto substitutability between personalization and privacy exists in the mHealth context. So when a potential user believes in mHealth and that it could provide him/her with highly personalized services, it increases the privacy concerns and has a decreasing marginal impact on perceived usefulness and attitude [54], and vice versa. Fourth, in M2 we found out that the factors of personalization and privacy paradox could influence adoption intention via perceived usefulness; when the impact of perceived usefulness on attitude is considered in M3, their effects become non-significant (PP→ATT, PSN→ATT) or less-significant (PRVC→ATT). Thus, the impacts of personalization and the nonlinearities between personalization and privacy on attitude are fully mediated by perceived usefulness and the effect of privacy on attitude is partially mediated by perceived usefulness [59]. So we come to the conclusion that the personalization privacy paradox factors can influence behavior intention through perceived usefulness and attitude successively.

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4.2. Limitations and future research Some limitations still exist in the present study. The first is related to participant samples. We conducted our sampling in Heilongjiang province, China. While we believe that although mHealth is a new health industry, it will have a bright future [60]. So the geographic restrictions of our respondents make it impossible to extend the findings to the general population, especially considering regional character factors or healthcare conditions. Future study should consist of samples from different areas, especially from different cultures to extend our results. Another limitation is that we found that the interaction of personalization and privacy was EdgeworthPareto substitutability, but we did not figure out the inherent reasons. In future research we would develop a suitable model to study the relationship between personalization and privacy. In addition, when addressing the benefits and risks in mHealth service, we only focused on personalization and privacy. Other factors such as convenience, promptness, economic, and unknown responsibility may also influence mHealth adoption. Future study is needed to involve these factors in a broader theoretical background. 4.3. Contributions In spite of the limitations above, our study also makes several theoretical contributions. First, we extended the nonlinearities theory by analyzing the interaction between two antagonistic independent variables in structural equation models. Personalization and privacy are antagonistic and their interaction positively influences perceived usefulness and attitude. This theorization can remind IS scholars to consider the directions’ effect when studying the nonlinearities between independent variables. Second, we found Edgeworth-Pareto substitutability between personalization and privacy. By incorporating the product of personalization and privacy we found out that it influences perceived usefulness and attitude in a positive way. This theorization can help us gain a better understanding of the relationship between personalization and privacy and also enrich the theory of personalization-privacy paradox. Third, we found that the effects of the personalization and privacy paradox factors on attitude are mediated by perceived usefulness. Though the mediating roles of perceived usefulness and attitude on these effects have been studied [15,18], our findings show that the impact path is first mediated by perceived usefulness and then by attitude. This theorization gives us new knowledge about the role of attitude in mHealth. Therefore attitude plays an important role in mHealth adoption when studying it from the side of personalization and privacy paradox. On practical grounds, the findings of this study have application values for mHealth service providers. The Edgeworth-Pareto substitutability between personalization and privacy can help vendors develop different marketing strategies for different potential customers. For example, if someone harbors concerns about privacy problems, the increases of personalization may have little effect, thus other methods should be used to urge a potential customer to become a registered customer. In addition, our findings identify the important role of attitude in mHealth adoption. Therefore vendors should promote their own image more to generate a higher positive attitude towards them and their services from potential users. 5. Conclusions Although mobile health services have drawn considerable worldwide research attention, studies on the personalization and privacy paradox in mHealth contexts are rare. Incorporating the nonlinear relationships between personalization and privacy, we take the personalization and privacy paradox factors

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X. Zhang et al. / Nonlinearities in personalization-privacy paradox in mHealth adoption

as independent variables, and perceived usefulness and attitude as middle variables to study mHealth adoption intention. The findings show that personalization and privacy substitution variables indirectly influence mHealth adoption, and the influence is first mediated by perceived usefulness and then by attitude, indicating the important mediating role of attitude. Our study also expand the the nonlinearities theory by two antagonistic independent variables in IS-contexts, especially in SEM research. For practitioners, they can gain a better understanding of the personalization and privacy paradox and the role of attitude in mHealth service generalization. Acknowledgments This study was partially funded by the National Science Foundation of China Grant (71101037, 70890082), the Hong Kong Scholars Program and the Program for New Century Excellent Talents in University. References [1]

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Appendix A Adoption intention [61] 1. I intend to use the [mobile health services] in the next 3 months. 2. I predict I will use [mobile health services] in the next 3 months 3. I plan to use mHealth in the next 3 months. Perceived Ease of Use [62] 1. Learning to operate the [mobile health services] will be easy for me. 2. I can easily become skillful at using the [mobile health services]. 3. I can get the [mobile health services] to do what I want it to do. 4. Overall, the [mobile health services] are easy to use. Privacy Concern [29] 1. My use of [mobile health services] would cause me to lose control over the privacy of my information. 2. Signing up for and using [mobile health services] would lead to a loss of privacy for me because my personal information could be used without my knowledge. 3. Other people might take control of my information if I used [mobile health services]. Personalization Concern [63] 1. By disclosing my information, [mobile health services] provider can understand my needs. 2. By disclosing my information, [mobile health services] provider can know what I want. 3. By disclosing my information, [mobile health services] provider will take my needs as its own preferences. Perceived Usefulness [62] 1. Using the [mobile health services] will improve my life quality. 2. Using the [mobile health services] will make my life more convenient. 3. Using the [mobile health services] will make me more effective in my life. 4. Overall, I find the [mobile health services] to be useful in my life. Attitude [64] 1. Using the [mobile health services] is a bad/good idea. 2. Using the [mobile health services] is a foolish/wise idea. 3. I dislike/like the idea of using the [mobile health services]. 4. Using the [mobile health services] is unpleasant/ pleasant.

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Nonlinearities in personalization-privacy paradox in mHealth adoption: the mediating role of perceived usefulness and attitude.

Personalization in healthcare refers to individualizing services and products based on patients' health conditions and interests. In order to deliver ...
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