Tacy, J. Northam, S. & Wieck, L. (July, 2016 Understanding the Effects of Technology Acceptance in Nursing Faculty: A Hierarchical Regression. Online Journal of Nursing Informatics (OJNI), 20 (2), Available at http://www.himss.org/ojni
Problem: Technology is widely used in nursing academia, but little is known about the effects of technostress on technology acceptance among nurse educators.
Purpose: This study examined the effects of nurse faculty technostress, perceived usefulness, ease of use, and attitude toward using technology on use, job satisfaction, and intent to leave teaching.
Method: A survey design of 1,017 online nursing faculty tested the Davis’ Technology Acceptance Model adapted with permission to include the variables of technostress, job satisfaction, and intent to leave teaching. Hierarchical regression was used to test the model.
Results: Technostress, perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention to use technology explained 80% (R2) of technology use. Technostress, perceived usefulness, attitude toward using, and use of technology explained 9.8% of the variance in job satisfaction although neither ease of use or behavioral intent made significant contributions to job satisfaction. Perceived usefulness, perceived ease of use, use of technology, and job satisfaction explained 4.2% of the variance in intent to stay in the profession.
Nursing faculty prepare nurses to work in complex, technological environments (Axley, 2008). This creates an urgency to integrate new clinical technology into curricula quickly (VanVooren, Devore, & Ambriz-Galaviz, 2011). Faculty are also expected to use technology in teaching to stimulate and facilitate learning. Pressure for faculty to teach traditional courses in non-traditional ways has increased in response to student demand (Axley, 2008). In 2011, 6.7 million US students, or 32% of the total student population, enrolled in at least one online course (Allen & Seaman, 2011). Substantial enrollment increases in baccalaureate, masters, and doctoral degree programs are attributed to the availability of electronic learning (American Association of Colleges of Nursing, 2011). Thus, increasing enrollments, diverse teaching methods, and rapidly changing technology have outpaced awareness of the factors influencing technology acceptance and use. While many nurse educators use strategies, like electronic learning and simulation, further use of technology is anticipated (Benner, Sutphen, Leonard, & Day, 2010; Institute of Medicine [IOM], 2010) so understanding the impacts of burgeoning technology on nursing faculty is needed.
Technology and its integration can create stress, called technostress, which affects the attitudes and use of technology. Jena and Mahanti (2014) explained that faculty experience technostress when they are unable to adapt and use technology in a healthy manner. Faculty often feel compelled to check work email and online discussion boards while also feeling the need to engage and work quickly. The resulting stress may undermine job satisfaction and result in faculty leaving academia (Khan, Rehman, & Rehman, 2013). It is important to recognize the effects of technostress on nursing faculty and manage it effectively to improve both the quality of work life and retention.
This study was designed to increase our understanding of technology use and technostress among nurse educators in the United States. During the last decade, a call for action has been issued for increased educational quality and requirements for nurses (Benner, Sutphen, Leonard, & Day, 2010; IOM, 2010). The recommendations represent a significant responsibility and task for nursing education. Aging faculty, budget constraints, faculty shortages, and increasing job competition from clinical practice contribute to the problems experienced by nursing faculty (American Association of Colleges of Nursing, 2015a). The average age of nurse faculty continues to climb, narrowing the number of productive years that nurse educators are available to teach. The average ages of doctoral and master’s prepared nurse faculty holding the ranks of professor (61.6 doctoral and 57.1 masters), associate professor (57.6 doctoral and 56.8 masters), and assistant professor (51.4 doctoral and 51.2 masters) reflect an aging faculty workforce. According to a 2010 American Association of Colleges of Nursing (AACN) survey of vacant faculty positions, there was a 6.6% vacancy rate with 803 unfilled positions. In 2014, schools of nursing turned away 68,938 qualified applicants to baccalaureate and graduate programs primarily due to insufficient numbers of faculty (AACN, 2015a). Demands that impact the role of nurse faculty create a need to further examine factors that influence faculty job satisfaction and intent to stay. Nursing cannot afford to lose qualified faculty who are needed to educate and graduate more nurses.
Mitchell, Palacios, and Leachman (2014) explained that higher education funding for most states remains well below pre-recession levels. The large funding cuts have led to tuition increases, spending cuts, eliminated course offerings, closed campuses, and reduced library services. These deficits can diminish the quality of education and compress faculty salaries when a highly educated workforce is needed for the future healthcare of our nation. Competition from higher-paying positions has also eroded the potential pool of nursing faculty. Across the nation, nurse educator annual salaries average $65,240, compared to the median salary for clinical nurse specialists at $81,586, and the median annual salary for nurse executives at $178,824 (“How Much Do”, n.d.). There is a definite competitive, monetary edge for nurse educators to utilize their education and knowledge to branch outside of academia.
Nursing programs are challenged by the retirement of experienced nurse educators, job competition, and role changes. This study explored technostress to gain insight into its effects on technology use, job satisfaction, and the intent to stay in teaching. Little research exists about strategies to delay the retirement of current nurse faculty; strategies to retain, replenish, and expand the future nurse faculty workforce can and must be addressed through research.
Burgeoning technology with varied levels of administrative support poses a challenge to academic stability. Increasing expectations for nursing faculty to embrace and incorporate new technology is occurring at the same time faculty members are teaching growing numbers of students who must be prepared to work with technology in high stakes health care arenas. How much these issues create technostress and influence their attitudes, use of technology, job satisfaction, and intent to stay is unclear. This study aimed to fill that gap. The purpose of this study was to examine the effects of nurse faculty technostress, perceived usefulness, ease of use, and attitude toward using technology on use, job satisfaction, and intent to leave the profession.
Review of Literature
The information age of computers has forever changed the way society functions, and this influence has become the universal constant for change since its inception. The acceptance of technology has become the industry standard for business, education, and daily life. For most colleges, electronic learning has enabled them to serve student populations through non-traditional means such as distance education and hybrid courses. Over the last two decades, conflicting language and definitions of the terms electronic learning, online learning, and distance learning have made it difficult for researchers to perform cross study comparisons (Moore, Dickson-Deane, & Galyen, 2011). Nursing education continues to transition from traditional methods of instruction to the inclusion of technology to accommodate various learning needs and curriculum advances (Nguyen, Zierler, & Nguyen, 2011). Many researchers have sought to explain technostress in varying fields of education and research, but few have examined the effects on nursing education. It is important to understand the impact of technostress on nurse educators.
Technology-based Instruction: Faculty Incentives
Educational researchers have explored how variables such as motivation, perception, skills, training, attitude, stress, and acceptance have influenced electronic learning for students and faculty. Chapman (2011) studied a large southeastern university with over 300 distance education courses and 48 distance education degrees and evaluated the motivations and incentives for two groups (N = 97 tenured/tenure track and N = 45 contingent) who teach at least one distance education (DE) course annually (N = 142; 48% response rate). The online, dichotomous survey contained 23 motivation options and 20 incentive options (survey constructed from literature review and piloted). Chi Square analyses found three significant motivators to teach online courses: to better balance work and family, begin a teaching career, and supplement another job. Significant incentives included free professional development; tuition reimbursement at the institution; program for certification in online instruction; access to campus office space; mentoring from experienced faculty; opportunities to do research; job security; and being part of an online faculty community. Clearly, educators saw many benefits in teaching online which may impact their desire to remain in academia.
The focus on retaining faculty in an era of faculty shortages has led to continuing assessment of how faculty perceive their engagement in online education. Green, Alejandra, and Brown (2009) studied factors that affect faculty decisions about teaching online. Survey responses (N = 135) were used to examine tenured, tenure-track, full-time non-tenured, and part-time/adjunct faculty. Results showed that online faculty as a whole were highly motivated by situational incentives, such as flexible working conditions and the opportunity to use technology. The main factor that discouraged them from teaching distance education was their concern about time commitment. This study recommended further research including evaluation of gender differences, university enrollment, online distance education enrollment, and technology resources.
Technology-based Instruction: Faculty Preparation
Faculty perceptions of online education show paradoxes that may interfere with the ability to sustain an effective teaching-learning environment, but there is an interesting dynamic when comparing faculty and student perceptions. Osborne, Kriese, Tobey, and Johnson (2009) used an online survey of 152 students and 24 faculty members to compare perceptions and experiences with online versus traditional education. Significant perception differences of online courses existed in: student learning, time involvement, faculty-student interactions, internet problems, and course difficulty. Faculty perceived that students learned less, the internet took more time, technology problems were an issue, interactions were less effective, and online courses were easier. Students who had taken an Internet course were less likely to think the course took more time, resulted in less-effective interactions, or encouraged them to procrastinate. This study suggests differences between student and faculty perceptions of online courses, which may diminish both student and faculty satisfaction with the online experience when outcomes seem inconsistent.
Faculty satisfaction with technology may not simply be limited to divergent viewpoints compared to student perceptions about learning situations. Faculty also show ambivalence about technology use for their own education and development needs. Georgina and Olson (2008) conducted an online study among faculty from 15 institutions of higher education. In an online sample of 237 respondents, 95% reported their university offered technology training, but only 7.2% attended the training. Fifty-six percent of the sample preferred training using small faculty groups with a trainer. Faculty technology skills showed strong correlations with both course design pedagogy (r = .65, p < .001) and course delivery pedagogy (r =.64, p < .001) indicating that faculty members with strong technology literacy were more apt to integrate technology into their course assignments and might prefer to deliver the course with more technology. This study recommended more research about effective faculty training strategies and technology assessment tools at the user level. It also supports the idea that faculty vary in their desire and readiness to prepare for teaching in technology rich environments.
Preparing faculty for online teaching has been an on-going challenge. Herman (2012) used an online survey to investigate the types and frequency of faculty development programs for online instruction at institutions (N = 821) with an established teaching and learning development unit. Results showed the most common faculty development programs offered were: 1) websites (90.4%); 2) technical services (89%); 3) printed materials (87.8%); and 4) consultation with instructional design experts (84.2%). Findings showed that faculty development programs for online instruction are offered frequently. Discussion with faculty using a qualitative approach provided insight into what faculty need and expect when moving to a more technology-based teaching situation. Lackey’s (2011) qualitative study (N = 6) interviewed three experienced and three non-experienced online faculty to identify how higher education institutions are preparing their faculty to teach online. Analysis of the interviews revealed that faculty found collaborating with colleagues, more one-on-one assistance from university personnel, and the offering of online courses and resources that support technical and pedagogical training to be the most beneficial for online instructional preparation. The study author recommended more research into the challenges faculty identify in transition to the online learning environment to facilitate change effectively and identify best practices.
Technology-based Instruction: Faculty Engagement
While universities can provide opportunities for faculty to learn how to use technology and incentives to integrate technology into courses, the task of actually gaining faculty engagement in online teaching delivery systems remains a challenge. A study of 400 randomly selected faculty teaching at least one lecture, lab, or seminar explored the important factors influencing faculty members’ decision to use or not use online course management applications (OCMA) (Zhen, Garthwait, & Pratt, 2008). Polynomial logistic analysis showed self-efficacy and philosophy had strong impacts on the probability of use of OCMA while teaching experience, peer pressure, and class innovation had no statistical impacts. They concluded that when faculty believe online education is useful and on par or better than traditional teaching, they are willing to invest the time and energy necessary. Thus, attitude is critical.
Attitude is a component of several models tested in studies of online education. Teo and Schaik (2012) compared the Theory of Planned Behavior, the Theory of Reasoned Action, and the Technology Acceptance Model (TAM) and found that, “across all models, the most dominant direct effect on intent to use was attitude” (p. 185). As computer literacy, information literacy, and the use of information technologies are fundamental to nursing education, faculty must be adept in their use (National League for Nursing, 2008). So attitude assessment must be considered when introducing technology into nursing curricula in order to gain faculty engagement and acceptance of new ways of teaching. The authors suggested further research to include additional and mediating factors of the intention to use technology in educational contexts.
Technology-based Instruction: Faculty Acceptance
Park, Lee, and Cheong (2008) examined factors that influence the adoption of course management systems in higher education by using the Technology Acceptance Model (TAM). In the study, 191 instructors were surveyed with a 35% response rate. Findings validated the TAM model in that perceived ease of use had a significant impact on perceived usefulness (β.63, p < .001) and behavioral intent (β.44, p < .05). The researchers identified the need to compare the perception of users versus non-users of electronic courseware to explore factors involved in technology acceptance.
The TAM model was also used in a study of 152 faculty (54% response rate) from the University of Hong Kong to determine acceptance of electronic learning (Yuen & Ma, 2008). Intention to use was predicted by perceived ease of use (β.39, p = .010) and computer self-efficacy (β.30, p < .01). Perceived usefulness was predicted by perceived ease of use (β.22, p < .05) and subjective norm (β.54, p < .001). Sixty-eight percent of the variance in the intent to use electronic learning was explained by the TAM model components of subjective norm, computer self-efficacy, and perceived ease of use. This study investigated the perceptions of instructors using electronic learning technology. Teacher acceptance of electronic learning was explained by the use of the TAM model thus enhancing teaching and learning in their studies.
Using the TAM model, Ball and Levy (2008) examined computer self-efficacy, computer anxiety, and experience with technology use as factors influencing the acceptance and use of information systems. The findings indicated that computer self-efficacy was the only significant predictor of intent to use. Limitations of this study included a small sample size (N = 56) from a small private university with questionable generalizability of the findings based on this sample.
To understand student teacher’s intent to use technology, Wong, Osman, Goh, and Rahmat (2013) distributed 302 questionnaires to student teachers from a Malaysian university with a 64.2% response rate yielding 194 female participants. Results indicated that perceived usefulness had a significant influence on attitude towards computer use (β = .65, p < .00) and behavioral intent (β = .48, p < .00). In addition, perceived ease of use influenced perceived usefulness (β = .69, p < .00), and attitude towards computer use influenced behavioral intent (β = .19, p < .01). The study by Wong et al. (2013) supports that the TAM model variables explain faculty acceptance of technology-based instruction. However, other factors that might inhibit acceptance of technology need exploration, such as stress arising from innovation.
To determine the incidence of technological stress among nurse faculty, Burke (2009) surveyed 311 baccalaureate nurse educators with a 55% response rate. This study measured stress using the Nurse Educator Technostress Scale (NETS). ANOVA results showed a significant difference in perceived administrative support among nurse educators based on their stress levels (F = 14.941 [1, 113], p < .001). Burke (2009) used regression analysis to understand the influence of administrative support. Results of this analysis showed that administrative support (F = 14.157, p < .001) explained 12% of the overall variance in nurse educator technostress. Nurse faculty with lower technostress reported higher administration support. Given the significance of this variable, research was recommended to further clarify the role of administrative support in causing or ameliorating technostress.
Al-Fudail and Mellar (2008) conducted a qualitative study to determine teacher technology stress among nine instructors using interviews and galvanic skin response (GSR) readings totaling 32 hours of observed readings. Since GSR rises during stressful situations, the study produced a laboratory measure for the presence of stress. Encountering Internet access problems or instrumentation difficulties tended to increase GSR levels generally with one subject registering a 60+mm increase (-32m to +30m). The lack of fit between the instructor and the environment causing the stress related to instructor ability, training, and technology. The use of the teacher-technology environment interaction model of classroom technostress enabled managers to identify possible environmental factors that can reduce technostress and indicated a need to examine teachers’ coping strategies. Agbu and Simeon (2011) also found that computer issues were related to stress reaction (r =.19 p <.01) in academic faculty with higher levels found in older versus younger subjects. These studies indicate the need for further research to determine if improved training or better mentoring with coping strategies would be effective in reducing stress.
Stress management is perceived as a way to help faculty manage anxiety related to incorporating technology into courses. La Paglia, Caci, and La Barbera (2008) reported computer expertise, computer self-efficacy, and internet attitude explained 69% of computer anxiety (R2 = .69, F(3, 77) = 54.48; p < .0001) among primary school teachers in Palermo Italy (N = 77). Positive Pearson’s correlations were found between computer expertise and computer self-efficacy (r = .45, p < .01), computer expertise and internet attitude (r = .40, p < .01), and computer self-efficacy and internet attitude (r = .36, p < .01). Negative correlations were found between computer anxiety and computer expertise (r = -.52, p > .01), computer anxiety and computer self-efficacy (r = -.64, p < .01), and computer anxiety and internet attitude (r = -.55, p < .01). The researchers recommended that training programs should focus on improving individual teachers’ trust of technology as opposed to just developing technology skills. Trusting the technology and gaining self-confidence can defuse the presence of tension which manifests in aberrant ways, such as abnormal stress or technology addiction.
Salanova, Llorens, and Cifre (2013) studied 1,072 information and communication technology (ICT) users in a cross-sectional design study and found that non-intensive technology users had significantly more anxiety (F(1,1072) = 15.73, p < .001), skepticism (F(1,1072) = 5.04, p < .05), and inefficiency (F(1,1072) = 26.01, p < .001) than intensive users of technology. The researchers pointed to demographic and occupational characteristics as fertile areas for studying the differences in stress related to technology. Since nursing faculty shortages are a growing problem, occupational comparisons might be insightful in seeking ways to decrease technostress and improve faculty retention.
Beam, Kim, and Voakes (2003) conducted a national study on job satisfaction in journalism and communication faculty members comparing their responses to technology-induced stressors. A selected random sample of 595 members of the Association for Education in Journalism and Mass Communication yielded 403 respondents who completed the telephone survey (77% response rate). This study found that technology stressors had a negative effect on job satisfaction (r = -.206, p < .05), were related to job dissatisfaction (r = .172, p < .05), and contributed to job-related exhaustion (r = .225, p < .05). Beam et al. (2003) found that in most instances, technology stressors stood out and mattered more than course load, tenure status, or rank. It is clear that faculty members are not immune to job stress, and this stress increased with the introduction of technology into the teaching environment. There is no reason to think that nurse educator stress with the introduction of new technology differs from that experienced by faculty in other academic areas.
This research study examined nurse educator technology stress (technostress) relating to instructional technology. The review of literature found that administrative support, age, training, trust, inefficacy, and classroom stress influence faculty technostress. Studies using the Technology Assistance Model showed that goal orientation, self-efficacy, and recurring use support technology acceptance. Measuring the influence of technostress on nurse educators’ perceptions of usefulness, ease of use, attitude towards use, behavioral intent to use, job satisfaction, and intent to stay fills a gap in the movement to improve job satisfaction and intent-to-stay among the dwindling numbers of nursing faculty. Although many nurse-related studies have looked at job satisfaction, none relate it to technostress. With the technology sophistication of hospital environments and increasing patient complexity, nurse educators will continue to need higher levels of technology proficiency. The expectations of Millennial students from the technology generation make early adoption and frequent use of technology by nursing faculty inevitable and mandatory. This review found that the use and acceptance of electronic instructional technology is predicted to be an essential part of achieving a work/life/family balance for future educators. Understanding how technostress impacts the use of instructional technology provides insight into strategies that promote the essential and effective use of technology within nursing education; further, it may improve the job satisfaction and quality of life for nurse educators. Studies reveal a continual call for research regarding theoretical and scholarly development of the technostress phenomenon, in particular the context of technology, role, and tasks (Ayyagari & Purvis 2011; Shu, Tu, & Wang, 2011; Tarafdar et al., 2015;). Thus, research to examine the effects of nurse faculty technostress on technology acceptance will provide insight into the nurse faculty role and technology use which will impact the future of nursing education.
Meeting the generational expectations of future generations of nursing students has pushed technology to the forefront of nursing education. Understanding communication technology has been one of the most challenging issues when studying new and emergent technologies (Park et al., 2008). Among various theories used to understand the acceptance of information technology, the Technology Acceptance Model (TAM) is one of the most cited theoretical frameworks in this area of research. Critical assessment of factors that may promote or impede the use of technology acceptance among nurse educators is essential to plan for and effect change in the educational system.
Davis, Bagozzi, and Warshaw’s (1989) Technology Acceptance Model theorizes that perceived usefulness and perceived ease of use determine an individual’s intention to use a system, with intention specifically being the mediator for system use (Figure 1). TAM addresses perceived usefulness, ease of use, attitude, behavioral intention, and system usage as variables that predict the acceptance of a new technology (Davis, 1989). The Technology Acceptance Model was used to examine the influences of nurse faculty technostress, perceived usefulness, ease of use, and attitude toward using technology on use, job satisfaction, and intent to leave teaching. A premise of the TAM model is the assumption that given time and knowledge about a particular behavioral activity, an individual's preference to perform the activity will begin to resemble the way they behave (Han, 2003).
Technostressed people have negative attitudes and feelings toward technology (Weil & Rosen, 1997). Therefore, it is hypothesized that the use of technology, job satisfaction, and intent to stay is influenced by the degree to which nurse educators are experiencing technostress as well as perceived usefulness, ease of use, and attitude toward using technology (Figure 1). Davis’s model postulates that technology use is determined by two leading beliefs: perceived usefulness and perceived ease of use. Attitude towards use and behavioral intention to use technology affects how nursing faculty respond to technological experiences; therefore, attitude and behavior are inferred to partially affect the perceived ease of use and perceived usefulness of technology. This theory suggests that if people believe that technology is useful, but at the same time believe that it is too difficult to use, the effort outweighs the benefits and thereby undermines use (Davis, 1989). Academic institutions require instructional electronic courseware to enhance instruction in higher education. Examination of the influences of nurse faculty technostress, perceived usefulness, ease of use, and attitude toward using technology on use, job satisfaction, and intent to leave teaching can assist in understanding future use of electronic learning and can predict the job satisfaction of aging faculty as a factor in their retention and intent to stay.
The variable definitions will be discussed using the TAM as an organizing framework with the electronic learning system (technology) considered to be the external variable context for the study followed by definitions of technostress, perceived usefulness, perceived ease of use, attitude toward using, behavioral intention to use, actual system use, job satisfaction, and intent to stay.
Electronic Learning Technology/System Use
Use of technology/electronic learning is defined as, “broadly inclusive of all forms of educational technology in learning and teaching; …synonymous with multimedia learning, technology-enhanced learning (TEL), computer-based instruction (CBI), computer-assisted instruction or computer-aided instruction (CAI), internet-based training (IBT), web-based training (WBT), online education, virtual education, virtual learning environments (VLE), information and communications technology (ICT), and digital educational collaboration” (“Electronic learning”, 2013, para. 1). The use of technology in nursing education is the use of software and/or hardware to supplement instructional methodologies. Examples of software technology include operating systems, nursing software, Graphical User Interfaces (GUI), learning management systems such as Blackboard Learn©, electronic medical records, and simulation. Examples of hardware technology include computers, tablets, hand-held devices, projectors, smart boards, simulation and audio-visual equipment.
Weil and Rosen (1997) define technostress as a problem of adaptation where individuals are unable to cope with adjustments to and use of technology. Specifically, technostressed people have negative beliefs and feelings toward technology.
Perceived Usefulness (PU)
Perceived usefulness is defined as the degree to which an individual believes that using a particular technology will enhance job performance (Davis et al., 1989).
Perceived Ease of Use (PEU)
Perceived ease of use is considered to be the extent to which an individual believes that using technology/system would be free of effort (Davis et al., 1989).
Attitude Toward Using (AT)
Attitude toward using is defined as an “Individual's positive or negative feeling about performing the target behavior” (Venkatesh, n.d., para. 5).
Behavioral Intent (BI) and System Use (U).
Behavioral intent to use is “the degree to which a person has formulated conscious plans to perform or not perform some specified future behavior” (Venkatesh, n.d., para. 5).
Job Satisfaction (S) and Intent to Stay (I)
Job satisfaction is defined as the positive feelings workers have about their jobs (Brodke et al., 2009). Intent to stay is the variable to measure retention within a current position.
Ha1: Among nursing faculty using technology in education, technostress, perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention to use technology explain variation in technology use.
Ha2: Among nursing faculty using technology in education, technostress, perceived usefulness, perceived ease of use, attitude toward using, behavioral intention to use, and use of technology explain variation in job satisfaction.
Ha3: Among nursing faculty using technology in education, technostress, perceived usefulness, perceived ease of use, attitude toward using, behavioral intention to use, use of technology, and job satisfaction explain variation in intent to stay in the profession.
This descriptive, correlational study design was undertaken using nursing faculty invited to complete a 195-item survey online via Qualtrics. This study examined data derived from demographics and survey items to examine the relationships between seven predictor variables and one dependent variable. Hierarchical regression was used to evaluate the three hypotheses.
A purposive, non-probability sampling of Southern Regional Education Board (SREB) member nursing schools was done using a list of member schools provided on the SREB website. One hundred and twenty schools of nursing located across the south eastern United States were included in this study and associate, baccalaureate and graduate nurse faculty (N = approximately 4,511) were invited to participate. Potential participants were contacted personally via email. Email lists were created using school websites and obtaining each faculty’s email address. For those schools without faculty email readily available on the internet (n = 12), the school dean or department head was contacted via email, informed of the study, and asked to disseminate an email invitation to their nursing faculty. The email invitation asked faculty who self-identify as teaching with technology to participate by accessing the electronic link to the questionnaire. Included in the invitation was a letter explaining the purpose of the study, consent, and assurance of confidentiality. To encourage participation, incentives were given via a random drawing to win one of the following: iPad 2, $100 dollar gift card to Wal-Mart, $50 dollar gift card to Amazon.com, $50 gift card to Lowes. One follow up email reminder was sent to encourage participation.
Of the 4,511 emails sent, 1161 faculty participated (26% response rate). Data were cleaned and missing data reduced the sample size to 1017. Table 1 displays the demographics of the study participants. The mean ages of doctoral and master’s prepared nurse faculty were as follows: holding ranks of Professor (doctoral 61, SD = 6.6 and masters 51, SD = 12.8), Associate Professor (doctoral 57, SD = 7.1 and masters 53, SD = 10.1), and Assistant Professor (doctoral 51, SD = 9.8 and masters 51, SD = 9.3). Study participant ages (Table 1) were similar to national nurse faculty data (AACN, 2015a). This reflects an aging workforce demographic reflective of the national population.
The email study invitation included a link to the online questionnaires via Qualtrics©, a secure web server. The survey was live for three weeks, and an email reminder was sent 2 weeks after the initial email. Results were downloaded, stored, and analyzed on a password-protected computer.
A survey methodology was used and included five combined instruments: demographic information, Nurse Educator Technostress Scale (NETS), Technology Acceptance questionnaire which includes scales for perceived usefulness, perceived ease of use, behavioral intent, and actual system use), the Attitudes Toward E-Learning tool (ATEL), Job in General, and the Job Descriptive Index. The demographic survey gathered the following data: age, gender, race and ethnicity, marital and family status, employment characteristics, education level, years of experience, and experience with technology.
Burke’s (2009) Nurse Educator Technostress scale (NETS) was used to measure technostress. It is a 35-item Likert-type survey questionnaire that asks subjects to think about technology stressors experienced in the last 6 months and rate them on a five-point scale: 1, not at all; 2, little stress; 3, moderate stress; 4, stressful; 5, very stressful. The NETS scale was reviewed by an expert panel for content validity after initial development and then pilot tested to evaluate internal consistency and performed well with reliability coefficient of α = .96 from a sample of 115 nurse educators (Burke, 2005). In this study, the first 22 items of the NETS pertaining to technology issues exhibited an internal consistency of α = .94 (N = 961).
The Technology Acceptance Model (TAM) Scales measured technology acceptance variables of perceived usefulness, perceived ease of use, behavioral intent, and actual system use. Perceived usefulness and perceived ease of use items were adapted from Davis’ (1989) original research examining technology acceptance (N = 107). Previous reliability coefficients are listed with each scale. The perceived usefulness scale contains six items resulting in an α = 97 (Davis, 1989). The perceived ease of use scale also contains six items with an α = .91 (Davis, 1989). Both of these variables were measured using a seven-point scale of “extremely likely” to “extremely unlikely”. The behavioral intent scale contains three items (N = 101; α = .95) and the actual system use measure contains one item (N = 101; α = .86), two additional variations of the same use question were added to the study survey. Scales utilized a seven-point scale ranging from “strongly agree” to “strongly disagree” (Kim, Chun & Song., 2009). For the current study, the scales had high internal consistency reliability: (N = 1003) perceived usefulness α = .96, (N = 1003) perceived ease of use α = .97, (N = 1011) behavioral intent to use α = .92, and (N = 1008) actual systemuse α = .96.
The nurse educator attitudes toward E-learning (ATEL) by Mishra and Panda (2007) contains 22 items. The items are scaled in a 5-point-Likert type format ranging from ‘5’ (strongly agree) to ‘1’ (strongly disagree). Seven statements on the ATEL are negatively worded and were reverse coded. Validity was supported by the survey authors utilizing a literature review to construct the survey statements and content validation by nine expert reviewers. Mishra and Panda (2007) indicate an α = 81 from a sample of 78. This study had an internal consistency reliability with an α = .89 (N = 938).
Nurse educator job satisfaction was measured with the Job in General (JIG) adapted from Brodke et al. (2009). This instrument contains 18 items to measure job satisfaction using a “yes”, “no”, and “?” (means the respondent cannot decide) response to each word or phrase. Eight items of the JIG are negatively worded and were reverse coded and scored. Brodke et al. (2009) indicate an an alpha of α = .92 for the JIG. This instrument is available free for use in scholarly research through the JDI Research Group at Bowling Green University. The Job in General (JIG) was used to measure job satisfaction (N = 877) and had an internal reliability in this study of α = .90.
Study data were converted to an electronic data set and analysis of variables was performed using the Statistical Package for the Social Sciences (SPSS) Version 23 (International Business Machines Corporation, 2015). Recoding was done per instructions on each instrument as directed for relevant variables. Exploratory data analysis was done using histograms, skew, and kurtosis analysis to evaluate normality and Levene’s test to evaluate homogeneity of variance. Transformations were done for data that were not normally distributed but did not yield better results.
Descriptive statistics such as age, gender, educational level, and academic rank were used to characterize the sample (Table 1). Hierarchical regression analysis was used to test three hypotheses with variable entry based upon the model (Figure 1). For hypothesis one technostress, perceived usefulness, perceived ease of use, attitude, and behavioral intent to use were used in hierarchical regression to predict technology use (Figure 2).
For hypothesis two technostress, perceived usefulness, perceived ease of use, attitude, behavioral intent to use, and system use were used in hierarchical regression to predict job satisfaction (Figure 3).
For hypothesis three technostress, perceived usefulness, perceived ease of use, attitude, behavioral intent to use, system use, and job satisfaction were used in a forced entry hierarchical regression to predict intent to stay in the profession (Figure 4).
Missing data was managed using mean substitution for all three hypotheses as noted in the Tables 2 to 4.
The correlations of the variables are shown in Table 2.3. Technostress, as expected, was inversely related to all model variables. The first prediction model contained five predictors tested in five steps with no variables removed. Mean substitution was performed via recoding missing data with the average instrument mean (N = 1017). The model was statistically significant, R2 = .80, F(5,1011) = 815.81, p < .000. Thus, the hypothesis was accepted, which demonstrates the five variables explain 80% of the variation in technology use indicating a strong model.
Technology use was predicted by lower levels of technostress and higher levels of perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention to use (Table 2.2). Inspection of the structure coefficients show that behavioral intent, perceived usefulness, perceived ease of use, and attitude were strong predictors of system use, and technostress was a moderate predictor that negatively impacts system use. Technostress entered as step 1 had the best chance of explaining variance yet only accounted for 4.3% of the variation in use (Table 2.3).
The minor role of technostress in the model was further evaluated to determine if technostress functioned as a mediator or moderator to ease of use and actual use. Using the steps recommended by Field (2013), technostress was not a significant linear mediator or moderator of ease of use and actual use.
The prediction model for hypothesis two contained six predictors and was reached in six steps with no variables removed. The correlations of the variables are shown in Table 3.1. Mean Substitution was performed by recoding missing data with the average instrument mean (N = 1017). The model was statistically significant, R2 = .10, F(6,1010) = 19.460, p < .000, which demonstrates that the six variables explain 10% of the variation in job satisfaction.
Job satisfaction was predicted by lower levels of technostress and higher levels of perceived usefulness, behavioral intent, and system use (Table 3.2). Neither attitude nor perceived ease of use were significant predictors of job satisfaction. This model was rerun without perceived ease of use and attitude, and the model did not perform well. Inspection of the structure coefficients suggest that system use, perceived usefulness, and attitude toward using were strong predicators of job satisfaction, and technostress was a moderate indicator that negatively impacts job satisfaction.
The third and final prediction model contained seven predictors reached in seven steps. The correlations of the variables are shown in Table 4.1. Mean substitution was performed via recoding missing data with the average instrument mean (N = 1017). The model was statistically significant, R2 = .04, F(7,1009) = 7.383, p < .000, which demonstrates the seven variables explain 4% of the variation in intent to stay (Table 4.2).
Intent to stay in the profession was primarily predicted by higher levels of perceived usefulness, perceived ease of use, and job satisfaction (Table 4.2). Neither technostress, attitude, behavioral intent, nor use were significant predictors of job satisfaction. This model was rerun without technostress, attitude, behavioral intent, and use; the model did not perform well.
The sample included 1,017 nurse faculty from states across the southeastern United States. Table 1 shows the sample demographics. Gender differences showed 93% female and 7% male. The average age of participants was 53 with a range from 25 to 80. Sample racial makeup was 90% white, 6% black and 4 percent other. The study demographics were not surprising compared to the national nursing workforce profession made up of 9% male, 75% white and 10% black (Health Resources and Services Administration Bureau of Health Professions, 2013). The nursing profession is aware of this bias and is continually working to enhance diversity. The American Association of Colleges of Nursing (AACN) (2015b) on behalf of the profession and discipline states an objective to “implement initiatives to increase diversity among nursing students, faculty, and the workforce” (“goal three,” para. 3).
Study results validated the TAM model (Figure 2) with the addition of technostress and explained 80% of the variation in system use (Table 2.1). The large sample size of 1,017 far surpasses prior TAM studies with samples ranging from N = 56 (Ball & Levy, 2008), N=152 (Yuen & Ma, 2008), N = 191 (Park, Lee & Cheong, 2008), and N = 194 (Wong, Osman, Goh, & Rahmat, 2013). The explained variance was large and impressive.
The second model (Figure 3) added job satisfaction as an outcome variable after technology use. The majority (86.2%) of the sample were satisfied (somewhat satisfied, satisfied, and very satisfied) which is good news, but the job satisfaction scores failed the assumption of normality making it less amenable to regression. Transformation did not improve its performance. The model started with technostress and then added the traditional TAM variables of perceived usefulness, perceived ease of use, attitude toward using, and behavioral intent to use. In this model, the use of technology became an independent variable with job satisfaction as the dependent variable. While the goal was to see if the strong TAM model fostered better understanding of job satisfaction, it did not perform well; and perceived ease of use and attitude toward using technology were not significant predictors of job satisfaction.
Thus, perceived usefulness, attitude toward using, and system use positively predicated job satisfaction, while technostress negatively impacted job satisfaction. Although the TAM model has been widely used, adding a dependent variable of job satisfaction undermined the model. This study found that attitude and perceived ease of use, historically strong TAM variables, were not significant predictors of job satisfaction. The model was re-run excluding non-significant predictors but predicted only 10% of job satisfaction (Table 3.3). Thus technology use plays only a minor, but significant role, in job satisfaction.
The third and final model (Figure 4) sought to use the strong TAM model to evaluate whether it fostered understanding of nursing faculty intent to stay in the job. On average the faculty intended to stay nine years with a SD of 6.81 and a range from 0 to 40 years. Forty percent intended to stay five years or less. The model predicted that technostress, perceived usefulness, perceived ease of use, attitude toward using, behavioral intent to use, use of technology, and job satisfaction did explain variance in intention to stay in the profession. The hypothesis was partially accepted, but technostress, attitude toward using, behavioral intention to use, and use of technology were insignificant predictors of intent to stay. Therefore, perceived usefulness, perceived ease of use, and job satisfaction predicted intent to stay in the profession.
This model was the lowest performing of the three studied with only 4% of prediction (Table 4.3). The model was also re-run without non-significant predictors but did not yield better results. Intent to stay in the profession was measured using only a single item, and future research is suggested with a stronger measure. Historically, research using the TAM model has shown that perceived ease of use and perceived usefulness generally are the strongest predictors (Yuen & MA, 2008). As seen in this model, both were significant, yet the other TAM variables were not significant.
Technology use does not have a strong influence on intent to stay in the profession, yet job satisfaction does predict intent to stay in the profession, as expected (Table 4.2). Recoding was done creating two groups: those who intend to retire in 5 years or less (N = 293) and those who intend to stay 6 years or more (N = 461). Analysis of differences in job satisfaction showed a significant difference (U = 55268, z = -4.43, p < .000) with those intending to retire soon less satisfied (M = 46.86, SD = 11) than those planning to stay (M = 50.30, SD = 5.98). Technostress was not significantly different in the two groups (retiring <6 years; staying) t = 1.043 (df 1, 841), p = .30.
The assumption driving this study was that technostress would be a strong predictor of technology use, job satisfaction, and intent to stay in the profession. Surprisingly, technostress was found to be a weak predictor for technology use and job satisfaction and irrelevant with intention to stay in the profession. Although surprising, the large sample size and addition of technostress did provide strong study results with 80% explained variance in the TAM model as noted earlier. However, the study was not as strong in filling gaps in what is known about job satisfaction and intent to stay using the TAM model.
The TAM model is strong, and continued research using the model is recommended. Technostress plays a role in augmenting the model, and the use of other technostress measures may do more to advance science in this area. Non-linear statistical analysis may also augment insight into the role of technostress. Technostress matters, and nursing programs can examine the negative effects of technostress and positive influence of perceived usefulness, perceived ease of use, attitude, and intent to use electronic learning technology in educational pedagogy. Technology is burgeoning while academic financial constraints may undermine provision of updated equipment and adequate administrative support. Future research can evaluate the impact of equipment and administrative support on technostress, perceived usefulness, perceived ease of use, attitude toward using, intention to use technology and technology use. Since this was the first study using technostress, job satisfaction, and intent to stay with the TAM model, more studies are needed.
The strength of the TAM model was evident with technostress added, but it did not perform traditionally with job satisfaction and intent to stay added as outcome variables. Perceived usefulness and behavioral intent for using technology were positive predictors of job satisfaction, and technostress negatively impacted job satisfaction. Longitudinal studies are needed measuring the traditional TAM variables with interventions to reduce technostress, provide technology support, and increase use while evaluating job satisfaction and intent to stay. It would be interesting to know if interventions could improve job satisfaction enough to delay retirement of eligible faculty. Currently nursing is experiencing a severe shortage in the profession in all areas and specifically education (AACN, 2015a).
Study Strengths and Limitations
The use of an electronic questionnaire and email recruitment fostered a larger sample size than prior TAM studies with more explained variance than ever reported using the TAM model. The method employed for direct personal email recruitment and the incentive helped get a large sample size. The study was representative of US nurses (Table 1).
All study instruments had strong internal consistency reliability results except the single item intent to stay. The limited contributions of the three added variables of technostress, job satisfaction, and intent to stay may be the result of the instruments which were general measures rather than ones targeted to nursing faculty.
Guided by Davis’ (1989) Technology Acceptance Model, this study added to the science of nursing by identifying factors that influence technology system use, job satisfaction, and intent to stay. Specifically, for hypothesis one, technostress, perceived usefulness, perceived ease of use, attitude toward using, and behavioral intent to use technology explained 80% (R2) of technology use. This impressive variance created a strong model to explain technology use among nurse faculty. Technostress, although a weak variable added to the model, did negatively influence technology use among nurse faculty. For hypothesis two, technostress, perceived usefulness, perceived ease of use, attitude, behavioral intent to use, and system use explained 10% (R2) of job satisfaction. In this model job satisfaction was only predicted by lower levels of technostress and higher levels of perceived usefulness, behavioral intent, and system use. For hypothesis three technostress, perceived usefulness, perceived ease of use, attitude, behavioral intent to use, system use, and job satisfaction explained 4% (R2) of intent to stay in teaching. Thus this model only derived that perceived usefulness and perceived ease of use of technology as well as job satisfaction predicted intent to stay in the profession.
This study examined the effects of technology acceptance in nurse faculty. Findings revealed that technostress undermines job satisfaction and technology use in nurse faculty, while supporting many other variables that positively influenced technology use, job satisfaction, and intent to stay in teaching. This study along with future research should propel administration and nursing programs toward engagement to create support of faculty who struggle with technology issues to reverse technostress and recognize key variables that promote job satisfaction and influence faculty intent to say in academia.
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Joseph W. Tacy PhD, RN
Assistant Professor, CNL/NA Program Coordinator
James Madison University School of Nursing, Burruss Hall
Harrisonburg, VA 22807
The goal of the proposed research was to investigate the adaption of technology among nurse faculty and the impact technology has on system use, job satisfaction, and intent to stay in the profession. My background and interest in informatics and post-graduate coursework in nursing and research enabled me to successfully carry out the study.
Sarah (Sally) Northam, RN, PhD
Professor, College of Nursing and Health Sciences
The University of Texas at Tyler
- Preterm, late term births
- Prenatal Care
- Secondary data analysis
K. Lynn Wieck, RN, PhD, FAAN
Professor, College of Nursing and Health Sciences
The University of Texas at Tyler and is CEO of Management Solutions for Healthcare in Houston, Texas
Professor in online doctoral nursing program; teaching responsibilities: Philosophy of Science; Nursing Theory and Research; Publishing Scholarly Papers; Chair of dissertation committees; Chair of Editorial Review Board