Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nursing Mobile Decision Support Systems Adoption: A Developing Country Perspective https://doi.org/10.3991/ijim.v12i2.8081 Mohammed-Issa Riad Mousa Jaradat!!"#$Jehad Mohammed Imlawi#$ Abedalellah Mohammed Al-Mashaqba Al al-Bayt University, Mafraq, Jordan mi_jaradat@aabu.edu.jo Abstract—Health professionals are increasingly using and relying on mo- bile applications to support their decisions in Jordan. Nursing staff have the op- portunity to use a wide variety of already existed mobile applications to support their tasks when providing health services to both inpatients and outpatients. This study investigated the factors that affect mobile applications' adoption by nursing staff to support their health decision making. The proposed factors are perceived usefulness, perceived ease of use, subjective norms, job rele- vance, and perceived external control. In addition, this study intended to inves- tigate the moderating influence of, age, gender, and self-efficacy. The proposed model was tested by collecting data 241 nursing staff in three public and private hospitals in Jordan. Results show that perceived usefulness and perceived ease are the most significant factors that influence the individu- al’s behavioral intention to use and adopt the mobile application in their deci- sion support processes. Keywords—Mobile applications, Health DSS, Behavioral intentions, Nursing staff, TAM 1 Introduction Decision support systems (DSSs) has significantly evolved in the last four decades [1, 2]. Turban [3] defined DSS as "an interactive, flexible, and adaptable computer based information systems (CBIS) that utilizes decision rules, models, and model base coupled with a comprehensive database and the decision makers own insights, leading to specific, implementable decisions in solving problems that would not be amenable to management science optimization models per se". DSSs comprise a large body of research. It aid decision makers in many fields, be- cause of the DSS characteristics; handling large amounts of data, processing data from different sources, and perform sophisticated analysis. DSSs give decision mak- ers a flexibility in solving simple and complex problems, and increasing the quality of iJIM ‒ Vol. 12, No. 2, 2018 113 Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… decisions [4]. The significance of DSSs arises in its ability to support the solution of complex problems that are rapidly changing [5]. Mobile technologies have changed the way we communicate, seek for information, and perform our jobs [6]. This creates new opportunities for DSS research and deci- sion makers in different fields. Location-based services allow mobile applications to offer a personalized and customized decision strategy for managers. Health information systems (HISs) have recently started to provide user-centric health services. HISs can play a significant role in improving the clinical decision- making. Nursing is one of the important roles in hospitals. Nursing staff are promot- ing the overall quality of health-care. However, the nature of nursing job requires timely assistance with decision-making tasks. Prior research, in the field of technology acceptance, argued that the users, of any information system, could only realize and achieve its benefits after adoption of this information system [7]. A plenty of research have investigated the factors that influ- ence information systems' adoption. However, no prior research has investigated the factors that influence nursing staff adoption of mobile decision support systems. Moreover, no prior research has investigated how nursing staff adoption of mobile DSS would be moderated by factors like self-efficacy, age, and gender. The purpose of this study is twofold. First, study investigated the factors that affect mobile applications' adoption by nursing staff to support their health decision making. Second, the study investigates the moderation effects of age, gender and self-efficacy, in the context of nursing mobile decision support systems adoption. 2 Literature Review The proposed model includes the factors: perceived usefulness, perceived ease of use, subjective norms, job relevance, perceptions of external control, and behavioral intention to use NMDSSs, in addition to self- efficacy, age and gender as moderators. These factors were chosen because of their strong agreement as reported in previous studies, and because of their applicability and suitability in the context of NMDSSs. This research utilizes the modified technology acceptance model [8] to test the pro- posed model. 2.1 Mobile DSSs in health care Mobile technology directly influenced mobile users [9], and it can be useful for de- cision support systems. Mobile phones are convenient tools, because of their ubiquity and accessibility, in addition to its usability, network connectivity, and internal smart card readability. Consequently, it is considered as an ideal tool for performing per- sonal tasks. Mobile devices can be used to assist its users when making decisions, by providing more dynamic and precise solutions [10]. The use of mobile technology in DSS em- powers decision makers in their decision-making processes [9]. The decision making 114 http://www.i-jim.org Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… process is improved using mobile phones, and can be carried out faster as it can be used anytime and anywhere [10]. The nature of decision-making process determines that it occurs in time-sensitive environments, and occurs in anywhere in the work field [11]. Traditional tools used in decision-making process might not fit these time and location requirements. Mobile phones can assist its users to solve problems, and make better decisions in anytime and any location. Information Systems in healthcare has increased rapidly in the past ten to fifteen years [12]. Information systems increase users' productivity in different environments, including fieldwork environments. Burstein et al. [9] argued that in the fieldwork environments, where desktop devices are not beneficial, mobile technologies are more appropriate to support the decision-making process. Consequently, the use of mobile technology in health-care field has increased exponentially [14, 15]. Uncertainty environment requires special types of decision support systems. In- formation provided by these special systems must be valuable to facilitate decision- making process. Moreover, the method of providing this information to the decision makers must be an understandable and useful method. 3 Hypotheses Building Technology acceptance model was used in previous research to predict users' ac- ceptance and adoption of new technologies including DSSs [16, 17, 18, 19, 2]. In the present study, we developed a model to investigate and predict nursing staff ac- ceptance of mobile DSS. The study extends the original TAM by adding external variables that determine nursing staff adoption of NMDSSs. The research model for this study is shown in figure 1. Fig. 1. the proposed research model iJIM ‒ Vol. 12, No. 2, 2018 115 Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… 3.1 Perceived ease of use and perceived usefulness. Previous literature in the field of technology acceptance has proposed the factors perceived ease of use (PEOU) and perceived usefulness (PU), and proved their influ- ence on the behavioral intention to use and adopt new technologies [20]. Perceived usefulness is defined as "the degree to which a person believes that using a particular system would improve his/her job performance" [7], while perceived ease of use is defined as "the degree to which a person believes that using a particular system would be free of effort" [7]. The higher the individuals' beliefs about how easy it is to use the information system, the higher the intention to use this information system [21]. Prior research has also proved the effect of perceived ease of use on the perceived usefulness [22]. Therefore, this research proposes the following hypotheses: H1: Perceived usefulness has a positive effect on the behavioral intention to use NMDSSs. H2: Perceived ease of use has a positive effect on the behavioral intention to use NMDSSs. H3: Perceived ease of use has a positive effect on the perceived usefulness to use NMDSSs. 3.2 Subjective norms Subjective norms is defined as "the degree to which an individual perceives that most people who are important to him think he should or should not use the system" [15]. The theory of reasoned action (TRA) has proved the influence of subjective norms on the social influence. Subjective norms has a positive impact on behavioral intentions to adopt new systems [8, 15, 23]. Consequently, the following hypothesis is proposed: H4: Subjective Norm has a positive effect on the behavioral intention to use NMDSSs. 3.3 Job relevance (JR) Job relevance is defined as "the degree to which an individual believes that the tar- get system is applicable to his or her job" [8]. This factor refers to the perception of individuals about the relevance between the proposed technology, and the job tasks [13]. Job relevance is a major variable in TAM2 [8]. Peker [23] found that job rele- vance has a positive effect on behavioral intention to use a technology. Consequently, the following hypothesis is proposed: H5: Job relevance has a positive effect on the behavioral intention to use NMDSSs. 3.4 Perceptions of external control (POEC) The perceptions of external control is defined as "the degree to which an individual believes that organizational and technical resources exist to support the use of the system" [24]. 116 http://www.i-jim.org Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… Prior research has investigated the impact of perceived external control on the per- ceived ease of use a new technologies. Venkatesh and Bala [22] has confirmed this effect. Thus, we expect that the perceptions of external control will positively influ- ence the behavioral intentions to use NMDSSs: H6: Perceptions of external control has a positive effect on the behavioral intention to use NMDSSs. 3.5 Moderating Effect of Self-Efficacy Self-efficacy (SE) is the individuals' self-evaluation of their capability of achieving a goal [23]. In the context of mobile technology, self-efficacy is defined as the degree to which an individual believes that he or she has the ability to achieve a specific task/job using the mobile technology [41]. Previous studies have been conducted to investigate the impact of self-efficacy on the technology adoption intentions [25, 26]. Other studies looked at the moderation impact of self-efficacy factor that moderate the influence on the mobile technology adoption [32]. Peker [23] proposed self-efficacy as an individual characteristic of users. In the present study, we use self-efficacy as a moderator. The moderating effects of self- efficacy, on the study relationships, are investigated with the following hypotheses: Hs1: Self-efficacy moderates PU-BI relationship in a way that it is stronger for nursing staff with a higher level of self-efficacy. Hs2: Self-efficacy moderates PEOU-BI relationship in a way that it is stronger for nursing staff with a higher level of self-efficacy. Hs3: Self-efficacy moderates PEOU-PU relationship in a way that it is stronger for nursing staff with a higher level of self-efficacy. Hs4: Self-efficacy moderates SN- BI relationship in a way that it is stronger for nursing staff with a higher level of self-efficacy. Hs5: Self-efficacy moderates JR- BI relationship in a way that it is stronger for nursing staff with a higher level of self-efficacy. Hs6: Self-efficacy moderates POEC- BI relationship in a way that it is stronger for nursing staff with a higher level of self-efficacy. 3.6 Moderating Effect of Age Prior research concentrated on the importance of understanding how individual characteristics (e.g., age) influence intentions to use a technology. This understanding can help decision makers in introducing new technologies to different users more effectively[28]. Morris et al. [29] claimed that individual differences (e.g., age), among users, are important in understanding how and why users make different choices regarding the technology adoption. For instance, older people typically have had less experience to use new technologies, so that, it is critical for them to be intro- duced to new technologies. A better theoretical understanding of age moderating effect is needed. In the field of NMDSS, user age is an important factor that affects the delivered services. Conse- iJIM ‒ Vol. 12, No. 2, 2018 117 Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… quently, age is expected to affect the intentions to use NMDSS services. Therefore, we examined age as a moderator that moderate the relationship between NMDSS adoption's determinants and the behavior intentions to adopt NMDSS. Thus, we pro- pose the following research hypotheses: Ha1: Age moderates PU-BI relationship in a way that it is stronger for younger nursing staff. Ha2: Age moderates PEOU-BI relationship in a way that it is stronger for younger nursing staff. Ha3: Age moderates PEOU-PU relationship in a way that it is stronger for young- er nursing staff. Ha4: Age moderates SN- BI relationship in a way that it is stronger for younger nursing staff. Ha5: Age moderates JR- BI relationship in a way that it is stronger for younger nursing staff. Ha6: Age moderates POEC- BI relationship in a way that it is stronger for younger nursing staff. 3.7 Moderating Effect of Gender Gender is one of the individual characteristics that affect the intentions of individu- als to adopt and use a new technology [23]. The influence of gender on information systems' adoption has received a considerable attention in the literature. Several stud- ies have investigated the moderating impact of gender on information systems' adop- tion, in a variety of contexts, including mobile payment [30], mobile commerce [31], mobile marketing [32], and mobile learning [33]. In the context of DSS, several stud- ies have examined the impact of gender differences on the adoption and use of DSS, for example, Aldhmour and Eleyan [2] studied the differences in individuals' percep- tions toward adoption of DSS, and they found no significant differences between male and female users towards DSS adoption. In the present study, we believe that, it is necessary to study the moderating effect of gender on the behavioral intentions to use NMDSS, to get a better understanding of the differences between male and female nursing staff. Consequently, we propose the following research hypotheses: Hg1: Gender moderates PU-BI relationship in a way that it is stronger for female nursing staff. Hg2: Gender moderates PEOU-BI relationship in a way that it is stronger for fe- male nursing staff. Hg3: Gender moderates PEOU-PU relationship in a way that it is stronger for fe- male nursing staff. Hg4: Gender moderates SN- BI relationship in a way that it is stronger for female nursing staff. Hg5: Gender moderates JR- BI relationship in a way that it is stronger for female nursing staff. Hg6: Gender moderates POEC- BI relationship in a way that it is stronger for fe- male nursing staff. 118 http://www.i-jim.org Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… 4 Methodology 4.1 Sample and Data collection procedure A survey was used to collect data on nursing staff perceptions of intentions to adopt the NMDSSs. The survey link was distributed on a convenient sample of 350 nurses working in three big hospitals in Jordan. 249 respondents have returned the survey. Eight participants have been removed due to incomplete answers. This left 241 datasets for the statistical analysis, with 68.9 % valid return rate. 4.2 Measurement The researchers conducted the data collection using a survey containing 22 ques- tions. Each question was measured on a 7-point, Likert-type scale, ranging from 1 (strongly disagree) to 7 (strongly agree). The instruments used to measure the con- structs were adopted from previous studies in order to ensure content validity [22]. The questions were then reworded to suit the study setting. Before conducting the survey, a pretest was conducted using sample of 30 nursing staff. They were asked to measure the construct for the face validity. Furthermore, a number of professors, in Information Systems major, reviewed the instruments. There feedback was considered in the final version of the survey. The final scales used in the survey are illustrated in the Appendix 5 Results About 59.8% of the respondents were females, where 40.2% where males. The ma- jority of respondents' age (53.5%) was less than 20. 24.5% of the respondents were 20 less than 30. 12.4% of the respondents were between 30 and less than 35 years old. 9.5% of the respondents were more than 35 years old. The monthly income for the majority of the respondents (43.2%) was 250 less than 500 JD. 29.5% of the respondents' income was between 500 and less than 1000 JD. 25.3% of the respondents' income was less than 250 JD. 2.1% of the respondents' income was more than 1000 JD. The majority of the respondents’ experience (34.9%) was less than 5 years. 22.8% of the respondents were trainees. 25.3% of the respondents' experience was between 5 less than 10 years. 17% of the respondents' experience was more than 10 years. See table 1. WarpPLS 5.0 software was used by this study to to evaluate the proposed model. The study checked for constructs' reliability, items' loading, internal consistency and discriminant validity to evaluate the properties of the measurement model. Cronbach Alpha scores were used to test the constructs' reliability. Cronbach Alpha scores ex- ceeded 0.6 for all of the constructs as demonstrated in table 2, and consequently, con- structs' reliability was considered acceptable [34]. iJIM ‒ Vol. 12, No. 2, 2018 119 Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… Table 1. Demographic profile of respondents (N=241) Percentage Frequency Variable 59.8 144 Female 40.2 97 Male 53.5 129 Less than 20 24.5 59 20 -Less than 30 12.4 30 30 -Less than 35 9.5 23 More than 35 25.3 61 Less than 250JD 43.2 104 250 Less than 500 29.5 71 500 Less than 1000 2.1 5 More than 1000 22.8 55 Trainee 34.9 84 Less than 5 Years 25.3 61 5 Less than 10 Years 17 41 More than 10 Years Table 2. The Alpha coefficients. The study constructs Alpha 1 Perceived Usefulness 0.870 2 Perceived Ease of Use 0.830 3 Subjective Norm 0.871 4 Job Relevance 0.764 5 Perceptions of external control 0.741 6 Behavioral Intention 0.935 All loadings are equal to or greater than 0.5, as explained in the Appendix. The study checked for the internal consistency by looking at the composite reliability scores. Composite reliability scores are recommended to exceed 0.7 [35] to be con- sidered as acceptable. Table 3 shows that the composite reliability scores exceed the suggested threshold for each construct. The study checked the discriminant validity by comparing the correlations among constructs, and the square root of the average variance extracted (AVE). The correla- tions among constructs should be less than the AVE scores to get an acceptable dis- criminant validity [36, 37]. Table 3 shows that AVE scores meet this condition, and consequently, the model has a good discriminant validity. The final results showed a substantial share of the behavioral intention to use NMDSSs (R2=0.70) is identified by the proposed determinants factors in the study. Meaning that 70% of the variance in behavioral intentions toward NMDSS usage is explained by the determinants' set collectively. Figure 2, and table 4 explain the study results. 120 http://www.i-jim.org Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… Table 3. Composite reliability, AVE, and correlation of constructs values. The study constructs Composite Reliability AVE 1 2 3 4 5 6 1 Perceived Usefulness 0.916 0.740 (0.860) 2 Perceived Ease of Use 0.889 0.672 0.558 (0.820) 3 Subjective Norm 0.912 0.722 0.427 0.364 (0.849) 4 Job Relevance 0.868 0.692 0.515 0.424 0.610 (0.832) 5 Perceptions of external control 0.840 0.576 0.535 0.590 0.409 0.437 (0.759) 6 Behavioral Intention 0.958 0.884 0.703 0.631 0.459 0.529 0.579 (0.940) Fig. 2. Results of testing hypotheses Table 4. Result of whole model hypotheses test. Hyp Dependent Variables Independent Variables Moderators Path Coefficient Supported H1 PU BI 0.432*** Yes H2 PEOU BI 0.242*** Yes H3 PEOU PU 0.507*** Yes H4 SN BI 0.009 No H5 JR BI 0.069 No H6 POEC BI 0.099 No Hs1 PU BI Self-Efficacy -0.015 No Hs2 PEOU BI Self-Efficacy -0.159** Yes Hs3 PEOU PU Self-Efficacy -0.136* Yes Hs4 SN BI Self-Efficacy -0.062 No Hs5 JR BI Self-Efficacy -0.043 No iJIM ‒ Vol. 12, No. 2, 2018 121 Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… Hs6 POEC BI Self-Efficacy -0.071 No Ha1 PU BI Age 0.056 No Ha2 PEOU BI Age 0.027 No Ha3 PEOU PU Age 0.001 No Hs4 SN BI Age -0.037 No Ha5 JR BI Age 0.004 No Ha6 POEC BI Age 0.010 No Hg1 PU BI Gender -0.004 No Hg2 PEOU BI Gender 0.069 No Hg3 PEOU PU Gender -0.009 No Hg4 SN BI Gender 0.149* Yes Hg5 JR BI Gender 0.018 No Hg6 POEC BI Gender -0.097 No Significance at p<***: 0.001, **:0.01, *:0.05 6 Discussion and Implications 6.1 Key findings The results show that there are factors that have a significant positive influence on the behavioral intentions to use NMDSSs. On the other hand, there are factors that do not. Results of the first hypothesis show that perceived usefulness has a significant pos- itive impact on the behavioral intentions to use NMDSSs (H1: !=0.432, p < 0.001). Individuals recognized that the use of mobile phone in nursing decision support would be beneficial and useful, and that was a good reason for them to adopt it. The reason could be the advantages associated with NMDSSs usage, such as ability to access to information in real-time and improving the nursing information flow. Ban- derker & Van Belle [38] stated, "Individuals (the doctors) agreed unanimously that the mobile technology device would be very useful and relevant to them, and the mobile technology device being useful by providing information to them". This finding is consistent with prior research. Aldhmour and Eleyan [2] found that perceived usefulness has a positive impact on adoption of decision support systems. Hsiao et al. [18] found that perceived usefulness has a significant impact on pain management decision support systems acceptance. Wu et al. [39] found that perceived usefulness has a positive impact on behavioral intentions to use mobile healthcare systems. Moreover, Ketikidis et al. [13] found that perceived usefulness has a influ- ence on the intentions to use health IT. Perceived ease of use has a significant impact on the behavioral intentions to use NMDSSs (H2: !=0. 242, p < 0.001). Individuals who recognized that the use of mo- bile technology, in nursing decision support, would be free of effort and easy, report- ed their desire to adopt it. Most of the respondents recognized that mobile technology is easy to use; the reason could be their previous usage, experience, and familiarity with the mobile technology. 122 http://www.i-jim.org Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… This finding is consistent with previous research. Sun et al. [40] found that per- ceived ease of use has a positive impact on adoption of mobile health services. Hsiao et al. [18] found that perceived ease of use has a significant impact on pain manage- ment decision support systems acceptance. Wu et al. [39] found that perceived ease of use significantly affected behavioral intentions to use mobile healthcare systems. Moreover, Ketikidis et al. [13] found that perceived ease of use has directly predicted health IT usage intentions. However, this finding is not consistent with Aldhmour and Eleyan [2] who reported contradictory results about the impact of perceived ease of use. Perceived ease of use has a significant impact on the perceived usefulness of NMDSSs (H3: !=0.507, p<0.001). This implies that individuals, who perceived the NMDSS as an easy to use tool, will find it useful to use as well. This finding is con- sistent with Wu et al. [39] who found that perceived ease of use significantly affects perceived usefulness of mobile healthcare systems. Subjective norms do not have any significant impact on the behavioral intentions to use NMDSSs (H4: ! = 0.009, p > 0.05). The reason could be because others do not influence nursing staff in hospitals, when it comes to adopting NMDSSs. Nursing staff might only be affected by the senior management decisions to adopt NMDSS. The study results on the subjective norms' effect is inconsistent with prior studies. Sun et al. [40] found that subjective norms positively influence the mobile health services' adoption. Ketikidis et al. [13] found that subjective norms has directly pre- dicted health IT usage intentions. Job relevance does not have a significant impact on the behavioral intentions to use NMDSSs (H5: !=0.069, p > 0.05). This implies that nursing staff, who believe that the NMDSS is applicable to their job; do not have more intentions to adopt it compar- ing with other nursing staff. Mobile phones consist a variety of mobile applications beside NMDSS; this might affect its relevance to the nursing job in hospitals. This finding is inconsistent with Banderker and Van Belle [38] who found that job relevance significantly affect the adoption of mobile technology by public healthcare doctors. Moreover, the finding is inconsistent with Ketikidis et al. [13] who found that job relevance has directly predicted health IT usage intentions. Perceptions of external control variables do not have a significant impact on the behavioral intentions to use NMDSSs (H6: !=0.099, p > 0.05). The reason could be that individuals believe that the use of mobile phone in decision support is incompati- ble with other applications currently used. Therefore, they do not perceive the control of NMDSSs over previous ways. The study findings demonstrate that self-efficacy does not exert any significant ef- fect in the current model except for the relationship between perceived ease of use and the behavioral intentions to use NMDSSs (Hs2: !=-0.159, p < 0.01), and the rela- tionship between perceived ease of use and the perceived usefulness NMDSSs (Hs3: !=-0.136, p < 0.05). The reason could be that individuals perceive the NMDSS's task more inefficiently, due to challenges related to the mobile phones. Moreover, mobile phone still suffer from usability problems such as limited screen size. Banderker & Van Belle [38] stated, "Doctors expressed an initial concern that the limited screen iJIM ‒ Vol. 12, No. 2, 2018 123 Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… size of the mobile technology devices might make it less useful, the screen on this device would not be able to display information very legibly". The study found no significant moderation impact for age. Meaning that, nursing staff age does not make any difference in the relationship between the study factors, and the intentions to use NMDSS. The reason for that could be the increasing aware- ness of nursing staff about the NMDSSs and other mobile applications, amongst all ages. Finally, the study found only one significant moderation impact for gender, on the relationship between subjective norms and the behavioral intentions to use NMDSSs (Hg4: !=0.149, p<0.05). Indeed, subjective norms do not exert any significant effect on behavioral intentions; however, subjective norms do affect women's behavioral intentions significantly stronger than its impact on men's behavioral intentions. 6.2 Theoretical and Practical Implications The study has many contributions for theory and practice. One theoretical contribu- tion is extending TAM model and testing its validity and applicability in Jordanian health care context. The study determined the variables that influence the user inten- tion to use NMDSS, such as subjective norm, job relevance and perceptions of exter- nal control. The practical contribution of this study is assisting decision makers in health care, and nursing staff, in implementing NMDSS services successfully. Using the study results, decision makers in the health industry can be informed how to encourage nursing staff to adopt mobile DSS in a way that improve the quality of health care. Designers of mobile applications that can be used by nursing staff to support their decisions, can use our results to know more about the importance of usefulness and easiness of their designed applications. 6.3 Limitations and Future Studies The study has some limitations that we recommend future research to resolve. First, in this study, our objective was to investigate the moderating effects of gender, age and self-efficacy, in the context of nursing mobile decision support in Jordan. Although the moderators identified in this study were based on the extant literature, we found a little moderating impact for these factors in the proposed model. Future studies may look at additional moderators that are more related to the context of nurs- ing staff adoption of mobile applications that support nursing staff decisions, such as experience years, education level and income. Other limitation could be the small size of the nursing staff sample from only three hospitals in Jordan. Consequently, they might not represent the various segments of the nursing staff in healthcare in Jordan. Finally, we recommend other researchers, to conduct additional research in this field, in order to identify other factors that can affect the behavioral intentions to adopt NMDSSs in Jordan, such as perceived quality, compatibility, perceived risk and perceived trust. 124 http://www.i-jim.org Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… 6.4 Conclusions This study attempted to investigate the key factors that affect adoption of NMDSSs by nursing staff in Jordan. These factors include perceived usefulness, perceived ease of use, subjective norms, job relevance, perceptions of external control. In addition, this study intended to investigate the moderating effect of self-efficacy, age and gen- der. Perceived usefulness and perceived ease are the most significant factors that influ- ence the individual’s behavioral intention to use and adopt the mobile application in their decision support processes. However, subjective norms, job relevance and per- ceptions of external control do not affect the individuals' intentions to adopt and use NMDSSs. Perceived ease of use has the greatest direct effect on the perceived usefulness, fol- lowed by the effect of perceived usefulness, and the effect of perceived ease of use on the behavioral intention to use NMDSSs. Self-efficacy has no significant moderating impact on the behavioral intention to adopt and use NMDSS, except on the relationship between perceived ease of use and the behavioral intentions to use NMDSSs, and the relationship between perceived ease of use and the perceived usefulness of NMDSSs. Age has no moderating impact on the behavioral intention to adopt and use NMDSSs. Gender has no moderating impact on the behavioral intention to adopt and use NMDSS, except on the relationship between subjective norms and the behavioral intention to use NMDSSs. 7 References [1] Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., & Carlsson, C. (2002). Past, present, and future of decision support technology. Decision support systems, 33(2), 111-126. https://doi.org/10.1016/S0167-9236(01)00139-7 [2] Aldhmour, F. M. and Eleyan, B. M. (2012) “Factors influencing the successful adoption of decision support systems the context of Aqaba special economic zone authority", Interna- tional Journal of Business & Management, Vol. 7 No. 2, p163-178. https://doi.org/10.5539/ijbm.v7n2p163 [3] Turban, E. (1990). Decision support and expert systems: management support systems. Prentice Hall PTR. [4] Tripathi, K. P. (2011). Decision support system is a tool for making better decisions in the organization. Indian Journal of Computer Science and Engineering (IJCSE), 2(1), 112-117. [5] Laudon, K.C., & Laudon, J.P., (2007). Management Information Systems , USA, Prentice- Hall, PTR Upper Saddle River. [6] Kim, Y. H., Kim, D. J., & Wachter, K. (2013). A study of mobile user engagement (MoEN): Engagement motivations, perceived value, satisfaction, and continued engage- ment intention. Decision Support Systems, 56, 361-370. https://doi.org/10.1016/j.dss. 2013.07.002 [7] Davis, F. (1989). 'Perceived usefulness, perceived ease of use, and User Acceptance of In- formation Technology', MIS Quarterly. Vol. 13, No. 3, pp. 318-339. https://doi.org/10.2307/249008 iJIM ‒ Vol. 12, No. 2, 2018 125 Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… [8] Venkatesh, V. and Davis, F. (2000). 'A Theoretical Extension of the Technology Ac- ceptance Model: Four Longitudinal Field Studies', Management Science. Vol. 46, No. 2, pp. 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926 [9] Burstein, F., Cowie, J., Zaslavsky, A., & San Pedro, J. (2008). Support for real-time deci- sion making in mobile financial applications. Information Systems and E-Business Man- agement, 6(3), 257-278. https://doi.org/10.1007/s10257-008-0090-4 [10] Pérez, I. J., Alonso, S., Cabrerizo, F. J., & Herrera-Viedma, E. (2008). A decision support system based on mobile internet. In XIV Congreso Español sobre Tecnologias y Lógica fuzzy(pp. 241-247). [11] Power, D. J. (2013). Mobile decision support and business intelligence: an overview!. Journal of Decision Systems, 22(1), 4-9. https://doi.org/10.1080/12460125.2012.760267 [12] Lin, M. K. (2012). Evaluating the acceptance of mobile technology in healthcare: devel- opment of a prototype mobile ECG decision support system for monitoring cardiac pa- tients remotely (Doctoral dissertation, University of Southern Queensland). [13] Ketikidis, P., Dimitrovski, T., Lazuras, L., & Bath, P. A. (2012). Acceptance of health in- formation technology in health professionals: An application of the revised technology ac- ceptance model. Health informatics journal, 18(2), 124-134. https://doi.org/10.1177/146 0458211435425 [14] Pedro, J. S., Burstein, F., Wassertheil, J., Arora, N., Churilov, L., & Zaslavsky, A. (2005). On development and evaluation of prototype mobile decision support for hospital triage. In System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on (pp. 157-157). IEEE. https://doi.org/10.1109/HICSS.2005.464 [15] Fishbein, M. and Ajzen, I. (1975). 'Belief, Attitude, Intentions and Behavior: An Introduc- tion to Theory and Research', Boston: Addison-Wesley. [16] Jaafreh, A. and Al-abedallat, A. (2011) "The Relationship between National Culture and DSS Usage in Jordanian Banking: A Proposed Conceptual Framework" European Journal of Economics, Finance and Administrative Sciences, ISSN 1450-2275 Issue 42. [17] Dulcic, Z., Pavlic, D., & Silic, I. (2012). Evaluating the intended use of Decision Support System (DSS) by applying Technology Acceptance Model (TAM) in business organiza- tions in Croatia. Procedia-Social and Behavioral Sciences, 58, 1565-1575. https://doi.org/10.1016/j.sbspro.2012.09.1143 [18] Hsiao, J. L., Chen, R. F., & Wu, W. C. (2013). Factors of accepting pain management de- cision support systems by nurse anesthetists. BMC medical informatics and decision mak- ing, 13(1), 16. https://doi.org/10.1186/1472-6947-13-16 [19] Gustavsson, G. G. (2009). Applying the TAM to Determine Intention to Use a DSS. In- formation Systems, 62-67. [20] Dishaw, M.T. and Strong, D.M. (1999) Extending the Technology Acceptance Model with Task-Technology Fit Constructs, Information and Management, 36, 1, 9-21. https://doi.org/10.1016/S0378-7206(98)00101-3 [21] Torres, C. A. (2011). Examining the Role of Anxiety and Apathy in Health Consumers' In- tentions to Use Patient Health Portals for Personal Health Information Management. ProQuest LLC. 789 East Eisenhower Parkway, PO Box 1346, Ann Arbor, MI 48106. [22] Venkatesh, V. and Bala, H. (2008) 'Technology Acceptance Model 3 and a Research Agenda on Interventions', Decision Sciences, Vol. 39, No. 2, pp. 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x [23] Peker, C. (2010). An Analysis of The Main Critical Factors that Affect the Acceptance of Technology in Hospital Management Systems. Unpublished Doctoral Dissertation. Anka- ra: Graduate School of Informatics of Middle East Technical University. 126 http://www.i-jim.org Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… [24] Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. (2003). 'User acceptance of information technology: Towards a unified view', MIS Quarterly. Vol. 27, No. 3, pp. 425- 478. https://doi.org/10.2307/30036540 [25] Mun, Y. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International journal of human-computer studies, 59(4), 431-449. https://doi.org/10.1016/S1071-5819(03)00114-9 [26] Alenezi, A. R., Karim, A. M. A., & Veloo, A. (2010). An Empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in in- fluencing the students' intention to use e-learning: A case study from Saudi Arabian Gov- ernmental Universities. TOJET: The Turkish Online Journal of Educational Technology, 9(4). [27] Islam, M. A., Khan, M. A., Ramayah, T., & Hossain, M. M. (2011). The adoption of mo- bile commerce service among employed mobile phone users in Bangladesh: self-efficacy as a moderator. International Business Research, 4(2), 80. https://doi.org/10.5539/ibr.v4n2 p80 [28] Czaja, S. J., & Sharit, J. (1998). Age differences in attitudes toward computers. The Jour- nals of Gerontology Series B: Psychological Sciences and Social Sciences, 53(5), P329- P340. https://doi.org/10.1093/geronb/53B.5.P329 [29] Morris, M. G., Venkatesh, V., & Ackerman, P. L. (2005). Gender and age differences in employee decisions about new technology: An extension to the theory of planned behav- ior. IEEE transactions on engineering management, 52(1), 69-84. https://doi.org/10.1109/ TEM.2004.839967 [30] Jose Liebana-Cabanillas, F., Sanchez-Fernandez, J., & Munoz-Leiva, F. (2014). Role of gender on acceptance of mobile payment. Industrial Management & Data Systems, 114(2), 220-240. https://doi.org/10.1108/IMDS-03-2013-0137 [31] Li, S., Glass, R., & Records, H. (2008). The influence of gender on new technology adop- tion and use–mobile commerce. Journal of Internet Commerce, 7(2), 270-289. https://doi.org/10.1080/15332860802067748 [32] Karjaluoto, H., Lehto, H., Leppäniemi, M., & Jayawardhena, C. (2008). Exploring gender influence on customer's intention to engage permission!based mobile marketing. Electron- ic markets, 18(3), 242-259. https://doi.org/10.1080/10196780802265793 [33] Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British journal of educational technology, 40(1), 92-118. https://doi.org/10.1111/j.1467-8535.2007.00809.x [34] Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivari- ate data analysis (Vol. 5, No. 3, pp. 207-219). Upper Saddle River, NJ: Prentice hall. [35] Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent vari- able modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information systems re- search, 14(2), 189-217. https://doi.org/10.1287/isre.14.2.189.16018 [36] Kock, N. (2015). PLS-based SEM algorithms: The good neighbor assumption, collinearity, and nonlinearity. Information Management and Business Review, 7(2), 113. [37] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobserv- able variables and measurement error. Journal of marketing research, 39-50. https://doi.org/10.2307/3151312 [38] Banderker, N., & Van Belle, J. P. (2009). Adoption of Mobile Technology by Public Healthcare Doctors: A Developing Country Perspective. International Journal of iJIM ‒ Vol. 12, No. 2, 2018 127 Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… Healthcare Delivery Reform Initiatives (IJHDRI), 1(3), 38-54. https://doi.org/10.4018/ jhdri.2009070103 [39] Wu, J. H., Wang, S. C., & Lin, L. M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International journal of medical infor- matics, 76(1), 66-77. https://doi.org/10.1016/j.ijmedinf.2006.06.006 [40] Sun, Y., Wang, N., Guo, X., & Peng, Z. (2013). Understanding the acceptance of mobile health services: a comparison and integration of alternative models. Journal of Electronic Commerce Research, 14(2), 183. [41] Compeau, D. R. and Higgins, C. A. (1995). 'Application of social cognitive theory to train- ing for computer skills', Information Systems Research, Vol. 6, No. 2, pp.118-143. https://doi.org/10.1287/isre.6.2.118 8 Authors Mohammed-Issa Riad Mousa Jaradat is an Associate Professor in the Depart- ment of Information Systems in the Faculty of Prince Hussein Bin Abdullah for In- formation Technology at Al al-Bayt University, Mafraq, Jordan. He holds a PhD in Management Information Systems. His research interests cover IT innovation ac- ceptance and adoption, learning technology, e-business, mobile technology, e- and m- government and m-commerce. Jehad Mohammed Imlawi is an Assistant Professor at Al al-Bayt University - In- formation Technology School. He received his PhD degree from the University of Colorado Denver. His research interests focus on online user behavior, human com- puter interaction, and online social networks. His research work has appeared in jour- nals like: the international journal of Human Computer Interaction, Computer and Education, and International journal of Interactive Mobile Technologies (iJIM), among others. AbedAlellah Mohammed Al-Mashqbah received his Master in Business Admin- istration from Al al-Bayt University, Mafraq, Jordan. His research interests are infor- mation management and m-commerce. Article submitted 07 December 2017. Final acceptance 21 January 2018. Final version published as submitted by the authors. Appendix (see next page) 128 http://www.i-jim.org Paper—Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nurs… Table A1: Modified survey questionnaire and factor loadings Construct Item Loading Perceived Usefulness Using a mobile phone in decision support system will improve my performance in my job. (0.936) Using a mobile phone in decision support system will increase my productivity in my job. (0.948) Using a mobile phone in decision support system will enhance my effectiveness in my job. (0.557) I find a mobile phone in decision support system will be useful in my job. (0.935) Perceived Ease of Use I think that learn to use a mobile phone to apply deci- sion support system for me is clear and understandable. (0.622) I think that using a mobile phone in support of decision support system does not require a lot of my mental effort. (0.891) I find that the use of a mobile phone in support of deci- sion support system is easy to use. (0.898) I find it easy to get the mobile decision support system to do what I want it to do. (0.838) Subjective Norm I think People who influence my behavior think that I should use the mobile decision support system. (0.871) People who are important to me think that I should use the mobile decision support system. (0.874) I think senior management in hospital has been helpful in the use of the mobile decision support system. (0.859) In general, the hospital I work in has supported the use of the mobile decision support system. (0.792) Job Relevance I think that using mobile decision support system is im- portant for accomplishing the tasks and duties assigned to me. (0.919) I think using mobile decision support system in my job will be appropriate and relevant. (0.924) I think that using of the mobile decision support system is pertinent to my various job-related. (0.616) Perceptions of external con- trol I think that I have control over using the mobile deci- sion support system. (0.870) I think that I have the resources necessary to use the mobile decision support system. (0.840) I think that when resources opportunities and knowledge are necessary it will be easy for me to use the mobile decision support system. (0.510) I think that the use of mobile decision support is in- compatible with other applications that I use. (0.762) Behavioral Intention I intend to use the mobile decision support system more frequently if the service is available. (0.937) I predict that I will use the mobile decision support sys- tem in the future. (0.946) I will deal with the mobile decision support system in the future, if the service is available. (0.939) Note: Measurement items were adapted from Venkatesh and Bala [22]. iJIM ‒ Vol. 12, No. 2, 2018 129 iJIM – Vol. 12, No. 2, 2018 Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nursing Mobile Decision Support Systems Adoption: A Developing Country Perspective