Physicians acceptance of mobile communication technology: an exploratory study. Pekka Mustonen, Matti Seppänen and Markku Kallio

Transcription

1 Int. J. Mobile Communications, Vol. X, No. Y, xxxx 1 Physicians acceptance of mobile communication technology: an exploratory study Shengnan Han Turku Centre for Computer Science (TUCS) Institute for Advanced Management Systems Research (IAMSR)/ Åbo Akademi University Lemminkäinengatan 14 B, 20520, Åbo/Turku, Finland shengnan.han@abo.fi Corresponding author Pekka Mustonen, Matti Seppänen and Markku Kallio The Finnish Medical Society Duodecim, PL713 Kalevankatu 11A, 00101, Helsinki, Finland pekka.mustonen@duodecim.fi matti.seppanen@duodecim.fi markku.kallio@kolumbus.fi Abstract: The study aims to contribute to the research on technology acceptance behaviour of professional physicians by extending its theoretical validity and empirical applicability in a new mobile communication technology context. We propose and empirically examine a research model using data collected from 151 physicians working in the healthcare sector in Finland; with the aid from previous research into the adoption of information systems, mainly TAM, UTAUT and PIIT. The results suggest that our proposed model could provide adequate explanations for physicians intentions to use the mobile system (Nagelkerke R 2 = 0.654). The important determinants of physicians behavioural intentions in early exposure to the mobile system are: perceived usefulness, the interaction effects of PIIT and age on ease of use, and of age on compatibility. Gender and hands-on experience of the system have different effect on physicians perceptions of the system. Keywords: adoption of mobile communication technology; physicians; mobile medical information system; behavioural intentions. Reference to this paper should be made as follows: Han, S., Mustonen, P., Seppänen, M. and Kallio, M. (xxxx) Physicians acceptance of mobile communication technology: an exploratory study, Int. J. Mobile Communications, Vol. X, No. Y, pp Biographical notes: Shengnan Han is a researcher at Turku Centre for Computer Science (TUCS)/Institute for Advanced Management Systems Research (IAMSR) at Åbo Akademi University in Finland. She will complete her doctoral degree in MIS in May Her research interests are on mobile commerce, user adoption of mobile products and services and industry foresight for mobile commerce. She has published 15 scientific articles and international conference papers on these topics. Copyright 2005 Inderscience Enterprises Ltd.

2 2 S. Han, P. Mustonen, M. Seppänen and M. Kallio Pekka Mustonen is Managing Director of Duodecim Medical Publications Ltd. in Finland. He received his MD and PhD from University of Helsinki. He has served as Sigrid Juselius Research Fellow at the Institute Jacques Monod in Paris, France. He was Associate Editor of the Duodecim Medical Journal from 1996 to He has been author/co-author of 18 research papers in international journals and a large number of articles in domestic medical journals. Matti Seppänen is Product Manager of Mobile Solutions at Duodecim Medical Publications Ltd. in Finland. He received his MD, Licentiate in Medicine from University of Tampere. He has a ten-year career in Duodecim, including several years as a member of the editorial team of the Evidence-Based Medicine Guidelines (Finnish edition) and as Deputy Editor. Since 1997, he has been a Member of the Finnish Association of Science Editors and Journalists. He is a part-time practicing physician. His current research interest is focused on mobile medical solutions. Markku Kallio is Senior Consultant in Duodecim Medical Publications Ltd. in Finland. He received his MD and PhD from University of Helsinki. He also received international MBA from Helsinki School of Economics and California University. He has started medical practice in 1982 as a full time medical doctor in several hospitals, mostly in Children Hospital at University of Helsinki. He has published over 40 papers in international journals, mostly on of cholesterol metabolism and infectious diseases. Since 1998, he has been intensively involved in developing professional databases (ex. Evidence-Based Medicine Guidelines) for medical doctors, which run both in the internet and on mobile platforms. 1 Introduction Mobile communication technology is transforming the communication among human beings. It has a big effect on societal and business changes around the world. Although it is clear that mobile communication technology has not been able to fulfil all its expectations to change the basic nature of business and our daily life, its profitable innovation opens up new business opportunities and offers which add value for both the customer and the company (Siau and Shen, 2003; Tarasewich et al., 2002). Healthcare is the largest service industry in the world. In the recent years, mobile communication technology has been adopted by the healthcare industry. It has the potential to slowly become an integral part of healthcare practice, management, and processes. Goldberg and Wickramasinghe (2003) have argued strongly that mobile e-health services offer a panacea for healthcare problems in the 21st century (see also Wickramasinghe and Goldberg, 2004). Their views direct our attention to new phenomena, e.g., design and use of mobile communication technology in healthcare settings. Existing mobile services, for physicians available on the market, range from simple medical dictionaries to sophisticated patient data systems that are capable of handling digital images and lab test results. A recent review by Fischer et al. (2003) has indicated that mobile services have become valuable in various fields of medicine. The systematic review summarised possible mobile services to access medical literature,

3 Physicians acceptance of mobile communication technology 3 electronic pharmacopoeias, patient tracking, medical education, research, business management, and e-prescribing etc. They focused on Personal Digital Assistance (PDA) as a mobile device. Physicians efficient use and mass adoption of mobile communication technology has emerged as a critical technology implementation and management issue as investment in mobile communication technology by the healthcare industry continues to grow. It has been widely acknowledged that users perceptions of and intention to adopt an Information System (IS) and the rate of diffusion and penetration of technology within and across organisations are two important foci of IS research (e.g., Straub et al., 1995; Taylor and Todd, 1995). They are understood to represent the essential aspect, property or value of the information technology (Orlikowski and Iacono, 2001). It is generally accepted that the usage of information systems at work could increase employees productivity in their working time, and improve individual and organisation performance. System usage is an important outcome to measure IS success (DeLone and McLean, 1992; 2003). The IS research community has encouraged researchers to develop and diffuse IS theory in healthcare settings (Chiasson and Davidson, 2004; Wilson and Lankton, 2004). This study is conducted as a response to more empirical examinations of IS theories/models in the healthcare context, and new issues regarding physicians behavioural reactions towards using mobile communication technology in their daily work. Consequently, the study aims to contribute to the research on technology acceptance behaviour of professional physicians by extending its theoretical validity and empirical applicability to in the new mobile communication technology context. The physicians acceptance of mobile communication technology can suggest effective management tips by healthcare organisations. It also seems to provide lessons in promoting the development and practicing of mobile communication technology in healthcare settings. In the next section, we briefly review the relevant literature on technology acceptance. The research model and research hypotheses are then described. The methodology issue is followed by a review of our measuring instruments, our study context and statistical analysis. The next section presents the important results found in the study. The paper ends with a summary of the study s findings and its implications. 2 Theoretical background: a literature review Technology Acceptance Model (TAM) is tailored to study user acceptance of computer technology (e.g., Lee et al., 2004; Legris et al., 2003). According to TAM, Behavioural Intention (BI) is a major determinant of usage behaviour since it can be predicted by measuring BI. BI is determined in terms of a person s consideration of the Perceived Usefulness (PU) and Ease of Use (EU) of the studied systems. PU and EU are postulated a priori, and are meant to be fairly general determinants of user acceptance (Davis et al., 1989,p.988). TAM emphasises the importance of how external variables affect the individual internal decision process.

4 4 S. Han, P. Mustonen, M. Seppänen and M. Kallio Recently, Venkatesh et al. (2003) proposed a unified model the Unified Theory of Acceptance and Use of Technology (UTAUT) based on studies of eight prominent models (in particular TAM) in IS adoption research. UTAUT is formulated with four core determinants of intentions and usage: performance expectancy, effort expectancy, social influence, and facilitating conditions, together with four moderators of key relationships: gender, age, experience, and voluntariness of use. The model was empirically examined and found to outperform the eight individual models (adjust R 2 = 0.69), including TAM. According to UTAUT, examination of the effects of the four moderators has contributed to a better understanding of the complexities of technology acceptance by individuals. This model gives us greater insights into the individual s adoption of an information system, especially the role played by important moderators in the key relationships between beliefs and behavioural intentions, because of its outstandingly strong theoretical premises and explanatory power. Personal Innovativeness in the domain of Information Technology (PIIT) is also an important moderator of behavioural intentions on technologies that UTAUT did not examine. PIIT examines the willingness of an individual to try an innovation in the domain of IT. Two studies by Agarwal and Prasad (1998a b) have proven that PIIT might serve as a key moderator for the antecedents as well as the consequences of perceptions. Conceptualised as a personal trait, Lewis et al. (2003) found that it was significantly related to individual perceptions of perceived usefulness and ease of use. Some studies have examined TAM or its extended models to explain physicians technology acceptance, e.g., physicians use of microcomputers (Jayasuriya, 1998), and their decisions to accept telemedicine technology (Chau and Hu, 2002a b; Hu et al., 1999), and paediatricians adoption of the internet and internet-based health applications (Chismar and W-Patton, 2003). Results from these and other studies suggest that TAM and its extensions should provide a fairly convincing explanation or prediction of physicians acceptance of technology. Perceived usefulness has been identified as a dominant determinant of physicians behaviour, while perceived ease of use has, to some extent, exerted a limited (not significant) effect on physicians decisions regarding adoption and use of technology. There are two explanations. One is that physicians, as professional users of technology, might exhibit considerable differences compared with ordinary end-users in general competence and adaptability to new technologies. The other is that because of their pragmatic work practice, physicians cannot use technology just because of its ease of use. Berg (1999) claimed that using supportive IT in healthcare is dependent on the meticulous interrelation of the system s functioning with the skilled and pragmatically oriented work of healthcare professionals (p.87). Such an interrelation could be measured by a compatibility construct developed by conventional innovation diffusion research (Rogers, 1995). Compatibility is the degree to which adopting the IT innovation is compatible with the existing value, needs, and past experiences of potential adopters (Karahanna and Straub, 1999; Morre and Benbasat, 1991). It is believed that this will act as a motive for adopting a particular IS. Obviously, the increasing compatibility of technology to meet physicians needs and work might encourage them to use the technology to achieve good performance. A number of researchers have studied user acceptance of mobile technology and services such as the mobile internet, text messaging, contact services, mobile payment, mobile gaming and mobile parking services based on IS adoption models (e.g., Pederson, 2002; Pedersen and Nysveen, 2003; Pedersen et al., 2003). They found that usefulness and ease of use are very important factors that determine user acceptance

5 Physicians acceptance of mobile communication technology 5 of mobile technology. The results of these studies confirm that in the mobile technology context, traditional adoption models, such as TAM, could be applied but they need to be modified and extended to increase their prediction and explanation power (e.g., Amberg et al., 2004). 3 Research model and research hypotheses Figure 1 depicts the research model employed in the study. We used behavioural intention, instead of actual usage, as the dependent variable, since TAM asserts that intention is a proper proxy to examine and predict a user s behaviour regarding information systems (Davis et al., 1989). In this study, behavioural intention refers to a physician s intention to use a mobile medical information system in his future practice. Behavioural intention is predicted by four beliefs/perceptions: Perceived Usefulness (PU), Perceived Ease of Use (EU), Social Influence (SI), and Compatibility (COMP or CP) with two moderators Personal Innovativeness (PIIT) and age. Gender and hands-on experience of the system are conceptualised as external variables that influence the formulation of relevant perceptions, i.e., PU, EU, and COMP in determining behavioural intention. Figure 1 Research model Gender Experience Perceived usefulness Perceived ease of use Social influence Compatibility Behavioural intention Personal innovativeness Age 3.1 Interaction effects In the study, Perceived Usefulness (PU) refers to a physician s belief that using the mobile system will help him or her to improve performance in patient care and management. According to TAM, perceived usefulness is positively associated with behavioural intention. It is the dominant determinant of technology acceptance by individuals and the single most important factor that determines a physician s adoption of telemedicine technology (Chau and Hu, 2002a b).

6 6 S. Han, P. Mustonen, M. Seppänen and M. Kallio The second key determinant of intention is the ease of use, referring to a physician s belief that using the mobile system will be effort-free. In a professional context, ease of use might not have a significant effect on behavioural intention (Chau and Hu, 2002a b). However, in our study context, a mobile system was introduced into physicians work practice as a trial. Perceived ease of use might still have fundamental influence on intention. However, its effects might be weaker when users are exposed to the system for a relatively long period. The third user perception examined social influence which refers to the degree of a physicians perception on the way other people, e.g., peers, colleagues, and family members, think, on whether he or she should use the mobile system. Physicians exert considerable influence over managers and patients, which in turn, makes the opinions of other important actors increasingly important. Understandably, physicians might adjust their behavioural intention in using new technology by considering those opinions. Compatibility refers to the degree of adopting the mobile system which is compatible with a physician s existing values, needs and practice style or preference. Physicians work styles involve high local mobility (Ammenwerth et al., 2000) and it is likely that a mobile system might help them to cope with it. Following this reasoning, the more physicians perceive mobile systems to be compatible with their current work practice, the greater the likelihood those physicians will accept it. Agarwal and Prasad (1998b) define PIIT as a moderator. PIIT indicates that individuals with a higher level of innovativeness, with respect to IT, are expected to formulate more positive perceptions about the innovation in terms of usefulness, ease of use and compatibility, etc., and therefore have higher intentions toward use of a new IT/IS. Age is integrated into the present model as another moderator because of its strong theoretical root in the UTAUT. Arguments from some anecdotes on the use of mobile technology also suggest that early adopters of mobile technology are commonly thought to be young the young mobile generation. The mobile system is new and only in its infancy in healthcare practice as compared with PC-based medical information systems. More innovative physicians may be more willing to adopt it and likely to think it useful for managing patient care in comparison with less innovative ones. Moreover, more innovative physicians might be enthusiastic on using it without involving too much mental effort in using the new system. As innovative users, they might be easily persuaded to try the new technology without any very convincing arguments. Similarly, innovative users may strongly intend to use the new innovation, which may not be compatible with their current work style, or life-style. Therefore, PIIT might positively moderate the relationship between perceptions of new mobile innovations and behavioural intention. Age is an important variable in the individual adoption of IS. Obviously, younger people are expected to be more exposed to mobile technology than the older generation. Therefore, they easily have positive perceptions on the usefulness and ease of use of mobile technology. Furthermore, they might consider a value-added mobile system more compatible with their work practice or daily life. Understandably, they are easily encouraged to use new mobile innovations. In contrast, older physicians would regard a mobile system as difficult to use and, consequently, unlikely to be convinced by others.

7 Physicians acceptance of mobile communication technology 7 Taking all those arguments together, we hypothesised the following: Hypothesis 1 Hypothesis 2 Hypothesis 3 Hypothesis 4 The impact of perceived usefulness on behavioural intention will be moderated by personal innovativeness in IT and age; such an effect will be stronger for innovative younger physicians. The impact of perceived ease of use on behavioural intention will be moderated by personal innovativeness in IT and age; such an effect will be stronger for less innovative older physicians. The impact of social influence on behavioural intention will be moderated by personal innovativeness in IT and age; such an effect will be stronger for less innovative older physicians. The impact of compatibility on behavioural intention will be moderated by personal innovativeness in IT and age; such an effect will be stronger for innovative younger physicians. 3.2 Effects of external variables External variables are one of the components represented in TAM. Davis et al. (1989,p.985) demonstrated: A key purpose of TAM is to provide a basis for tracing the impact of external factors on internal beliefs, attitudes, and intentions. External variables might consist of various individual differences, situational constraints, organisational characteristics and system characteristics, etc. In this study, we focus on gender and experience of previous usage of the target system. Gender is defined as an external variable that influences physicians beliefs on the mobile system. Gefen and Straub (1997) found that women and men differ in their perceptions, e.g., PU and EU. Doll et al. (1998) reported that gender does not affect the invariance of the PU instrument, but it does affect the EU instrument. Gender plays a vital role in shaping the initial beliefs of today s knowledge workers (Venkatesh et al., 2000). Therefore, the following hypotheses will be tested: Hypothesis 5 Hypothesis 6 Gender will have significant effects on physicians perceived usefulness of the mobile system, such that men will rate it higher than women. Gender will have significant effects on physicians perceived ease of use of the mobile system, such that women will rate it higher than men. Hypothesis 7 Gender will have significant effects on physicians perceived compatibility of the mobile system, such that men will rate it higher than women. Experiences from past or direct usage of IS help in formulating positive beliefs and performing behaviour. For example, it is no surprise that an individual who has general experience on computer technology or specific experience on a particular IS will be more likely to take a positive attitude and intention to use newly introduced or new IS in organisations (Agarwal and Prasad, 1999). Users with a different experiential background differ in their perception of beliefs, e.g., PU toward IS (Doll et al., 1998). Accordingly, we tested the following hypotheses:

8 8 S. Han, P. Mustonen, M. Seppänen and M. Kallio Hypothesis 8 Hypothesis 9 Hypothesis 10 The level of physicians usage experience of the mobile system will positively affect their perceived usefulness of the system. The level of physicians usage experience of the mobile system will positively affect their perceived ease of use of the system. The level of physicians usage experience of the mobile system will positively affect their belief in compatibility of the system. 4 Research design and method 4.1 Study context The Finnish Medical Society Duodecim is a leading provider of medical knowledge and information in Finland. It has adapted new technologies to distribute knowledge to physicians, i.e., CD-ROM, intranet, and the internet (Jousimaa, 2001). In 2002, a mobile medical information system with a set of medical information and knowledge databases was designed to disseminate medical knowledge. Its contents are similar to those in the databases and are also available in conventional printed books, via hospital intranets and the Finnish national internet portal, Terveysportti (Han et al., 2004). The mobile medical information system, so called mobile package by the developer (Duodecim Medical Publications Ltd., a publishing company owned by the Finnish Medical Society Duodecim), is built on an XML database and can be modified easily to work in most mobile devices with different operating systems e.g., Symbian, Palm OS and Windows CE. In Finland, the device most commonly used as a platform is the Nokia 9210 Communicator. Currently, the updates are delivered as physical memory cards with the users returning the older ones. In the near future, the system will be able to update itself partly or completely through the wireless network. For instance, a new drugs price list was updated successfully through the GSM network in autumn of From spring 2003, the developer has, with support from Pfizer Finland Ltd., started a pilot trial where 800 physicians were given free Nokia 9210 Communicators equipped with the mobile system. The physicians were selected randomly with a balance between general practitioners and specialists, i.e., 400 in each group. 4.2 Instrument development and data collection Items assessing various constructs presented in the research model were adapted from past research, with changes in wording to make them appropriate for the mobile medical information system (mobile package) and the healthcare context. In particular, items on perceived usefulness, ease of use and social influence were adapted from Davis et al. (1989) and Venkatesh et al. (2003); items such as behavioural intention and items such as compatibility came from Morre and Benbasat (1991) with reference to Teo and Pok (2003), and items of PIIT from Agarwal and Prasad (1998a b). Most constructs were measured using a five-point Likert-type scale, ranging from (1) strong agreement to (5) strong disagreement. Demographic data, e.g., working places (hospital, healthcare centres, private doctors, and research institutes), gender, age and hands-on experience of

9 Physicians acceptance of mobile communication technology 9 using the system were also recorded. The Finnish version of the questionnaires was sent out to those who had participated in training sessions organised by the Developer from November 2003 to January A total of 350 questionnaires were distributed; of which 151 were returned, resulting in a 43.1% response rate. 4.3 Statistical analysis We used 151 returned replies for the data analysis. They were considered valuable because they contained fewer than 30% uncompleted answered items (Gagnon et al., 2003). Missing values were replaced by the sample mean for that item. The analysis was conducted in four stages. First, the data were screened for outliers. Four cases were deleted because of possible undue influence on the results of the analysis. The second step assessed the measurement validity (see Appendix). Poor reliability meant that some items had to be left for subsequent analysis, i.e., Items 3 and 4 of the social influence construct, Item 5 of the compatibility construct, and Item 3 of behavioural intention. We performed factor analysis using oblique rotation to allow for possible correlations among the constructs to study the discriminant validity of the items. Then we examined convergent validity and estimated the factor scores of each construct one by one. In the third step, we examined the overall explanation power of the proposed models. As the distribution of the dependent (behavioural intention) was obviously bimodal (Figure 2), it was recorded as a dummy variable (1 = high behavioural intention and 0 = low behavioural intention). A logistic regression approach was adopted in the analysis. Age and PIIT were treated as continuous variables initially in the logistic analysis to examine the strength of the interaction effect. Then we split the sample into four groups according to the mean value for age (i.e., 44.8) and PIIT (i.e., factor score = 0). They were labelled as innovative young, less innovative young, innovative old and less innovative old. Logistical analyses were conducted for each sub-sample to examine the nature of the effects for each of the four groups. Figure 2 Behavioural intention Frequency

10 10 S. Han, P. Mustonen, M. Seppänen and M. Kallio Finally, the external effects of gender and experience on perceptions of the system were studied by examining the whole dataset. They were analysed by MANOVA; Bonferroni was adopted for the post hoc test. The sample size for MANOVA analysis was 135 as a result of some missing responses of gender and experience. The sample size for the logistic regression analysis was 146 (four outliers and one were deleted listwise) out of 151 returned answers. Of these, 84 were in the category of high behavioural intention; 62 were in the low category. As an exploratory study, we set the significance level at 0.10 (Straub et al., 2004). 5 Results The mean age of physicians in the sample was years old. There were more males (54.7%) who participated in the study. About 53.1% worked in healthcare centres, 33% in hospitals; others worked as private doctors or medical researchers. On average, the responding physicians had over 11 months of experience of using the mobile information system. Only seven physicians had not used the system before. Out of 141 completed answers, 41 (27.2%) had used the system for less than six months, about 46.8% (that is 66) had used it for less than one-year, and 34 had used it for 1 2 years. 5.1 Analysis of measurement validity The reliabilities of the latent constructs were measured using Cronbach s alpha. As shown in Table 1, the values were above 0.70, which are the common threshold values recommended by the literature (Straub et al., 2004). Table 1 Construct Descriptive statistics Number of items Reliability Mean Standard Correlations deviation BI PU *** EU *** 0.36*** SI * 0.32*** COMP *** 0.76*** 0.55*** 0.26** PIIT ** 0.29*** 0.47*** 0.18* 0.40*** 1.00 Notes: Cronbach s alpha is reported for reliability. All constructs are measured on a 1 5 scale, strongly agree to strongly disagree. Pearson correlation coefficients are reported. *p < 0.05; **p < 0.01; ***p < 0.001

11 Physicians acceptance of mobile communication technology 11 Construct validity of the instruments was evaluated by computing discriminant and convergent validity by using factor analysis. Discriminant validity is summarized in Table 2. The factor analysis showed that only five factors could be extracted (eigenvalues > 1). The items expected to measure compatibility were also loaded to the factors measuring perceived usefulness and perceived ease of use. Obviously, the main reason for this was that all three constructs seem to be highly correlated with correlations between and (See Table 1). Of course, this should also be considered when interpreting the results of the subsequent analysis. However, since all three constructs are important parts of the proposed model, we shall keep them as separate variables in the model regardless of the measurement problems. Finally, the factor scores were estimated separately for each construct using the items indicated in Table 2 and the Anderson-Rubin method. After performing convergent validity analysis of each construct in the proposed model, we found out that those items measuring compatibility did not extract two dimensions; they converged to measure one common underlying construct. Therefore, the convergent validity is satisfactory. Table 2 Factor analysis- discriminant validity Factor PU PU PU PU PU PU EU EU EU EU EU SI SI PIIT PIIT PIIT PIIT CP CP CP CP BI BI Notes: Extraction method: maximum likelihood Rotation method: promax with Kaiser normalisation

12 12 S. Han, P. Mustonen, M. Seppänen and M. Kallio 5.2 Hypothesis testing The research model was evaluated by running the logistic regression procedure in SPSS The analysis results (Table 3) suggested that the model was adequate to explain the variance in physicians intentions to use the mobile information system (Nagelkerke s R 2 = 0.654). Significance tests of Goodness of Fit proved the good fit of the model (model χ 2 Sig. = 000). The fact that the Hosmer and Lemeshow Test was not significant implied that the model s estimates fit the data at an acceptable and significant level. The classification table also revealed the success of the model, showing an overall 83.6% correct percentage of estimates. The percent correct percentage was approximately the same for both high and low behavioural intentions. Here, we could claim that our proposed model successfully predicts physicians behavioural intentions concerning the mobile medical system regardless of the highness or lowness of behavioural intention. Table 3 Hypotheses test-1 Model summary 2Log likelihood Cox&Snell R 2 Nagelkerke R 2 Hosmer and Lemeshow test (Significance) Classification table (percentage correct) Dependent = 1 Dependent = 0 Overall D ONLY D+I PU 0.35 (0.005)** 0.29 (0.008)** EU 0.47 (0.024)* 0.30 (0.009)** SI 0.72 (0.207) 0.72 (0.400) COMP 0.59 (0.189) 0.48 (0.139) PIIT 0.97 (0.907) 0.63 (0.229) AGE 0.58 (0.026)* 0.36 (0.003)** PU*PIIT 0.92 (0.830) EU*PIIT 1.55 (0.354) SI*PIIT 0.79 (0.600) COMP*PIIT 1.55 (0.332) AGE*PIIT 0.61 (0.294) PU*AGE 0.78 (0.615) EU*AGE 1.06 (0.899) SI*AGE 0.61 (0.225) COMP*AGE 3.96 (0.021)* PU*PIIT*AGE 1.46 (0.434) EU*PIIT*AGE 2.21 (0.092) SI*PIIT*AGE 2.10 (0.116) COMP*PIIT*AGE 0.51 (0.302) Notes: Dependent variable: behavioural intention (N = 146); 1 = high behavioural intention; 0 = low behavioural intention 1. D ONLY: direct effects only; D+I: direct effects and interaction terms; 2. Odds ratio EXP(B) (p value) is reported; 3. p < 0.10, *p < 0.05, **p < 0.01.

13 Physicians acceptance of mobile communication technology 13 Table 3 contains the logistic results used to address Hypotheses 1 4. The analysis showed that PIIT and age were important moderators, influencing the strength of ease of use on behavioural intention, and marginally significant at the 0.10 level only. Certain other significant odds ratios were observed, e.g., EU and age; the presence of the high-order interaction effects meant that they were not interpretable. The odds ratio of COMPAGE and PU might be meaningful because of the absence of significant high-order interaction effects. The effect of age moderated the relationship between compatibility and behavioural intention while the relationship between compatibility and behavioural intention varied with the age level. A unit change in Perceived Usefulness (PU) was associated with a decrease in the odds of the dependent variable being high behavioural intention. Further analyses of the results were performed in the sub-samples of the four groups: innovative young, less innovative young, innovative old, and less innovative old (Table 4). Table 4 Hypothesis test-2 Group Model summary 2Log likelihood Cox&Snell R 2 Nagelkerke R 2 Hosmer and Lemeshow test (Significance) Classification table (percentage correct) Dependent = 1 Dependent = 0 Overall Variables PU EU SI CP Notes: Innovative young (N = 38) 23.40*** Less innovative young (N = 27) 18.93*** Innovative old (N = 45) 25.76*** Less innovative Old (N = 36) (0.804) 0.29 (0.127) 2.67 (0.238) 0.08 (0.040)* (0.308) 0.29 (0.193) 0.32 (0.177) 0.28 (0.253) Dependent = 1 (high behavioural intention) Dependent = 0 (low behavioural intention) Odds ratio EXP(B) (p value) is reported for variables p < 0.10, *p < 0.05, ***p < (0.056) 0.17 (0.083) 0.65 (0.518) 0.92 (0.941) (0.168) 1.57 (0.466) 0.73 (0.503) 0.61 (0.550)

14 14 S. Han, P. Mustonen, M. Seppänen and M. Kallio Intentions were regressed on the four perceptions for each sub-sample to examine the nature of the effects of EU and compatibility for each of the four groups. Ease of use had a positive coefficient for three groups, the exception being the less innovative old group, but was statistically significant only for the innovative old group at the level of Recalling that we used the 1 (strongly agree) to 5 (strongly disagree) scale for the measurement models, the results therefore indicated that any unit changes in perceptions concerning ease of use would result in a negative effect on behavioural intention. Compatibility had a positive coefficient for four groups but the result from the innovative young group was significant at the level of If the level of significance had been stronger, i.e., p < 0.05, then we might argue that PIIT did not present a strong but only very marginally significant moderating effect. However, age did have a significant interactive effect on compatibility. To sum up, Hypothesis 1 was not supported and PU had a significant effect on behavioural intention regardless of the physician s PIIT and age at the level of Hypothesis 2 was partly supported; while the marginally significant effect was from old innovative physicians, not less innovative old ones. Hypothesis 3 was not supported at all. Hypothesis 4 was partly supported; only age exhibited the interaction effect. The significant contribution of the effect was from innovative young physicians. At the level of 0.05, we could claim that perceived usefulness, ease of use, and compatibility moderated by age were important determinants of physicians decisions to use or not use the mobile system. Results of the direct effect only model were also reported. The effects of perceived usefulness, perceived ease of use and age on intention were significant. Hypotheses 5 to 7and 8 to 10 were tested by means of MANOVA (Table 5). The results showed that gender had a significant effect on a physician s perception of ease of use, but only marginal effects on perceived usefulness and compatibility of the mobile system. However, hands-on experience of the system had significant effects on perceptions of ease of use and compatibility, marginally for perceived usefulness. The post hoc test indicated significant differences among the less experienced who had used the system for less than six months, and the more experienced, who had used it more than one year. Therefore, at the level of 0.10, Hypotheses 5 to 7 and 8 to 10 were all supported; some of them at a stronger level of significance. Table 5 MANOVA results Pillai s test Perceived usefulness Perceived ease of use Compatibility F = p = Gender Experience R 2 = p = (0.073) (0.067) Notes: p < 0.10, *p < 0.05, **p < 0.01, ***p < Beta (p value) is reported R 2 = p = (0.003)** (0.000)*** R 2 = p = (0.050) (0.001)**

15 6 Discussion Physicians acceptance of mobile communication technology 15 This paper aims to explore physicians behavioural intention regarding a mobile medical information system. A research model was proposed and then empirically examined by using responses from 151 physicians practising in the Finnish healthcare sector. The results obtained from the logistic regression analysis showed that the model was able to provide adequate explanation of variance (Nagelkerke R 2 = 0.654) in an individual physician s behavioural intentions with regard to the system. Two of four interaction term hypotheses specified by the model were partly supported and were statistically significant at the 0.10 level. It might appear that these provide some moderation evidence of PIIT and age when it comes to a user who adopts an innovative mobile technology. When the interaction effect was taken into account, the model provided more explanation than the case with direct effect only (Nagelkerke R 2 = 0.527). Perceived usefulness had a strong direct effect on physicians intentions to use mobile technology regardless of their PIIT and age. It confirmed that professional physicians, as a special user group, will only use a certain IS if it is perceived as useful in their work (Berg, 1999; Chau and Hu, 2002a b). Here, ease of use appears to be the determinant of intention in most situations. The effect would seem to vary with PIIT and age, being statistically only marginally significant for innovative old physicians. Ease of use is usually the secondary determinant on individual adoption of technology, as has been shown in past research. In a physician s professional context, Chau and Hu (2002a b) found that ease of use had limited effects on behavioural intention. They argued that physicians, as professionals with a high user competence, were unlikely to consider using a technology simply because it was easy to use. Our findings differ from those results and indicate that ease of use is very important, at least for old and innovative physicians. There are several possible explanations. The mobile medical system studied here is completely different from those PC-based systems used in previous studies. The operation of the mobile device itself, e.g., Nokia Communicator 9210, might be not easy especially for physicians who shift from desktop computers with a large screen to a mobile device with a small screen and small keyboard. Another reason is that the mobile system delivers medical information similar to that from conventional PC-based systems. Physicians who still use their desktop computers very often might expect the mobile system to be equally easy to use. The third is that physicians work is still not very mobile, particularly for those working in primary healthcare. The nature of their work causes them to regard the mobile system as a complement rather than a competing tool or replacement for their old computer system. The strong effect of EU on old physicians is reasonable; as seen in the results of previous research. The strength of social influences was not found to be significant and varied in interaction with PIIT and age. The result suggested that physicians do not seem to be easily influenced by their peers and family s opinions. Evidence to support the effect of compatibility of PIIT and age on behavioural intention was lacking in our study. Only age exerted a possible interaction effect, which was statistically significant in the case of young physicians. Agarwal and Prasad (1998a,p.213) argued that compatibility requires an essential change in the work practice of a potential adopter. Our finding indicated that young physicians are ready to make the same adoption decision as those who are old only at significantly high levels of perceived compatibility. We might also argue that old, at least innovative old physicians might adopt the mobile system at relatively low perceptions of compatibility. Hence, they could

16 16 S. Han, P. Mustonen, M. Seppänen and M. Kallio be agents of change to promote mobile technology. We notice that PIIT did not actually exhibit a significant moderating effect at the 0.05 level, and only marginally at the 0.10 level. Neither did it have any direct significant effects on physician s behavioural intention towards the system (direct effect model). It might indicate that the effect of PIIT on behavioural intention is mediated by physicians other perceptions of the system, e.g., perceived usefulness, rather than the moderating effects we hypothesised. Interestingly, external variables proposed in the model have shown different effects of physicians perceptions on the system, i.e., female physicians seem to more importantly consider ease of use. Experience seems to affect perceptions of compatibility and ease of use. Their effects on perceived usefulness were rather marginal at the 0.10 level. It seems that perceived usefulness, as the most dominant determinant of physicians behaviour towards the mobile system, is not influenced by individual differences, i.e., gender and experience. Physicians constitute a heterogeneous user group in terms of individual differences; nevertheless, they formulate a rather coherent belief on the usefulness of the mobile system. Before discussing the implications of this study, it is worth pointing out its limitations. One limitation concerns the methods we adopted for data analysis. The results of the logistic regression are parallel with the results of conventional linear regression, but not directly comparable. We found that two measures of behavioural intention failed to formulate it as a continuous variable, which invalidated the data for an OLS analysis. This might be due to unknown response bias of the data. We distributed 350 questionnaires among 800 physicians who participated in the pilot trial. The sample might not be entirely representative of the whole study population but only reveal a few categories of behaviour regarding the mobile medical system. Thereby, it is also difficult to generalise the results. Hence, future study should target the whole population. It is also necessary to conduct a survey on physicians who were not involved in the pilot trial. A comparison might reveal more insights into the physicians behaviour towards adoption of the system and a fuller understanding will also help in effectively incorporating it into the Finnish healthcare sector. Another limitation results from the scales adopted to measure the core constructs and the loading of items of compatibility to PU and EU. Therefore, the measurement models lacked strong discriminant validity. The interpretation of the effect of PU or EU on physicians behaviour might have to take the effect of compatibility into account. Therefore, future research might target more appropriate scales with the emphasis on discriminant validity with other core constructs in the model. Finally, perhaps the fact that some of the hypothesised interaction effects did not prove to be significant might be attributed to the size of our sample. Obviously, a larger sample would have been needed to get statistically more convincing results. 7 Implication and conclusion Our study contributes to the research on technology acceptance behaviour of professional physicians by extending its theoretical validity and empirical applicability in the new mobile technology context. Theoretically, our results suggest the importance of studying moderation effects on IS adoption research in general, and among professional users behaviour in particular. We confirmed the existence of such effects only at the 0.10 level. However, we used two key moderating variables, the PIIT and age of an adopter. Future work might investigate the effect of other individual variables. The examination of PIIT

17 Physicians acceptance of mobile communication technology 17 as an antecedent of perceived usefulness and ease of use is a necessary in a similar context. The inability to use OLS regression to analyse the data has provided some hints for designing a similar survey. In order to avoid the dependent variable the behavioural intention being a categorical variable we might include more items in the measurement model. The adoption of a seven-point instead of a five-point Likert scale might also help eliminate the problem. Our study also has managerial implications. The findings suggest that among physicians as special professional group and because of the nature of their work perceived usefulness is a strong determinant of usage. Initially, the developer has to improve the usefulness of the system. Our results suggest that individual differences, e.g., gender and experience, do not seem to have a strong influence on a physician s perception of usefulness regarding the mobile system. Since the perception of compatibility correlates highly with perceived usefulness, possible efforts by management might focus on this aspect, especially for young physicians. Mobile communication technology has the potential to change the fundamental way of how people organise their work and leisure activities. Assessing the compatibility between physicians work and the mobile system would enhance the usefulness of the system. Ease of use with the interaction effects of PIIT and age exerts a marginal effect on physicians intention. The developer should put more effort in making the system user-friendly and design special training programmes for different innovative and age groups, not only for old physicians but also for female and less experienced physicians, in order to mitigate the different levels of mental stress they feel when using the system. Igbaria and Iivari (1995) postulated that Finland is a more feminine and slightly more collective society, so that individuals abilities, experience, and organisational support, rather than perceived usefulness, are likely to play a major role in affecting usage. Thus, as much as possible, organisational encouragement should be done; as hands-on experience might alleviate the mental effort necessary and make the system more compatible with physicians practice. The system has to seriously consider a possible mobile device effect on physicians perception of ease of use. Different mobile devices have various embedded menus, functions, screen sizes and interfaces; users have to learn to operate mobile devices to use a mobile system running on it. The mobile device in the study, the Nokia 9210, did not seem to be particularly easy to use, especially for older physicians. Since mobile devices are becoming an integral part of healthcare information systems (Lin and Vassar, 2004), the adoption of some other type of mobile devices, e.g., a PDA might be a solution to the mobile device effect (e.g., Liang et al., 2003). It is very important that the developer can deliver the system with a user-friendly device. Our results provide suggestions for improving mobile communication technology in healthcare settings. The physicians in this study have rather positive intentions to adopt mobile communication technology in their daily work. Duodecim should exploit this positive momentum and continue its distribution of medical information and knowledge with mobile technology. The mobile medical system studied here is a crucial starting point for mobilising medical information and knowledge. The possibility to update contents through wireless networks in the near future will enable physicians to work in real time and will feed them up-to-date medical information and knowledge. The study also highlighted the importance of understanding physicians individual characteristics, e.g., age, gender, PIIT, experience. These would help Duodecim to create user profiles to match physicians individual needs. Knowledge freedom is one of the main values that

18 18 S. Han, P. Mustonen, M. Seppänen and M. Kallio could be created by mobile technology (Keen and Mackintosh, 2001). It adds value to the organisation and its workers through knowledge mobilisation, which brings information, communication, and collaboration to them. The mobile medical information system studied here offers a traditional push technology to search for and retrieve information. It is only one step further to make wired information contents available wirelessly. It would make sense to develop the system to give personalised information through a pool approach, i.e., the relevant information comes to the user. To conclude, the results of this study demonstrated that the proposed model is useful in predicting physician usage behaviour on mobile communication technology. Greater understanding is accounted for the presence of the interaction effects of PIIT and age between the relationships of perceptions and behavioural intention. The findings also provide guidance to those organisations that are improving mobile communication technology and promoting a mass adoption of it in healthcare. Acknowledgment An earlier short version of this article was published in the Proceedings of the 10th Annual Americas Conference for Information Systems (AMCIS), 5 8 August 2004, New York City, NY, USA. Results from this paper are based on the earlier version, but a more accurate analysis technique is adopted. Our warmest thanks reserve to Professor Leif Nordberg for his constructive suggestions for statistical methods for the data analysis. The authors would like to thank the Editor, Dr. Binshan Lin, and the two anonymous reviewers for their invaluable comments and suggestions on the earlier version of this paper. The corresponding author would also like to thank Nokia Foundation for partly supporting her research in year References Agarwal, R. and Prasad, J. (1998a) A conceptual and operational definition of personal innovativeness in the domain of information technology, Information Systems Research, Vol. 9, No. 2, pp Agarwal, R. and Prasad, J. (1998b) The antecedents and consequents of user perceptions in information technology adoption, Decision Support Systems, Vol. 22, No. 1, pp Agarwal, R. and Prasad, J. (1999) Are individual differences germane to the acceptance of new information technologies?, Decision Sciences, Vol. 30, No. 2, pp Amberg, M., Hirschmeier, M. and Wehrmann, J. (2004) The compass acceptance model for the analysis and evaluation of mobile services, International Journal of Mobile Communications, Vol. 2, No. 3, pp Ammenwerth, E., Buchauer, A., Bludau, B. and Haux, R. (2000) Mobile information and communication tools in the hospital, International Journal of Medical Informatics, Vol. 57, No. 1, pp Berg, M. (1999) Patient care information systems and healthcare work: a sociotechnical approach, International Journal of Medical Informatics, Vol. 55, No. 2, pp Chau, P.Y.K. and Hu, P.J. (2002a) Examining a model of information technology acceptance by individual professionals: an exploratory study, Journal of Management Information Systems, Vol. 18, No. 4, pp