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Workplace health interventions can produce beneficial health- and business-related outcomes. However, such interventions have traditionally focused on lifestyle behaviors of individuals, mostly not considering the role of working conditions. The wecoach intervention is an internet-based tool that combines both a digital and a participatory team development approach aimed at addressing critical job demands and resources as key aspects of health-promoting working conditions. Nursing staff are particularly affected by challenging working conditions and could potentially benefit greatly. Understanding the acceptance of novel workplace health promotion approaches is a critical precursor to their successful implementation and use.
This study aims to examine the factors influencing the acceptance of a digitally supported team development tool among nurse managers.
A sample of 32 nurse managers from 3 German-speaking countries tested wecoach and completed our online questionnaire. Hypotheses were based on the unified theory of acceptance and use of technology (UTAUT) and the organizational health development (OHD) model and were tested using multiple regression analyses.
Our analyses found that merely capacities on the team level (CapTeam) significantly contributed to the acceptance of wecoach, although only after the other variables were excluded in the stepwise multiple regression analysis. The UTAUT predictors were unable to add significant variance explanation beyond that, and their inclusion masked the contribution of CapTeam.
For the acceptance of a digitally supported participatory tool, the fit with the team, its culture, and its motivation are of critical importance, while aspects proposed by traditional acceptance models, such as the UTAUT, may not be applicable.
Workplace health programs can produce beneficial health- and business-related outcomes [
Teams are optimal units for workplace health promotion [
At the same time, the ongoing megatrend of digitization has led to an increase in the delivery of interventions in digital format. The most common forms are health apps, wearables, and health portals [
This presents a highly innovative form of workplace health intervention. Previous intervention research has focused on the effectiveness of workplace health interventions, with aspects of acceptance and implementation receiving little attention. This, however, is changing. Attention is now directed towards
Health care is one of the industrial sectors with the highest levels of health risks. The sixth European working conditions survey [
In this study, we examine what affects the acceptance of wecoach, an internet-based tool that combines both a digital and a participatory team approach and guides team leaders through a health-oriented team development process. It is currently only available in German. When logging into wecoach, the team leader interacts with a chatbot, which advises the leader on which training session to complete next and presents training materials on work and health, self-assessments, and online tools to conduct team surveys and workshops, as well as self-evaluation of progress and effectiveness (see
Screenshot of the wecoach main page.
Screenshot of a wecoach interactive form.
The aim of this study is to examine the factors determining the acceptance of wecoach among nurse managers in 3 German-speaking countries in order to make a contribution to the understanding on what factors can help promote the use of participatory, digitally supported workplace interventions that can help address working conditions in a challenging work environment such as health care. We based our hypotheses regarding its acceptance on 2 models: the unified theory of acceptance and use of technology (UTAUT) and the organizational health development (OHD) model to capture the complexity of wecoach, which is simultaneously a technological innovation as well as an innovative participatory team approach.
UTAUT [
UTAUT proposes 4 predictors [
UTAUT has been applied in different contexts, especially to study the acceptance of online banking [
A meta-analysis of 74 studies by Khechine et al [
As our study is exploratory in nature and we want to focus on the initial impressions potential users have of wecoach, we included only behavioral intention as an indicator of acceptance in this study. We expect all 4 predictors to be relevant to the acceptance of wecoach as a new technology. Our first hypothesis (H1) thus states:
H1a: Performance expectancy contributes to the intention to use wecoach.
H1b: Effort expectancy contributes to the intention to use wecoach.
H1c: Social influence contributes to the intention to use wecoach.
H1d: Facilitating conditions contribute to the intention to use wecoach.
The wecoach tool is a complex intervention approach that is not only a new technology but also an innovative participatory approach that affects different organizational levels. For this reason, we considered it necessary to include additional predictors in our study. Attitudes or beliefs relating to the affected organizational levels may serve as the gateway to considering using such a tool, even before considering aspects such as usefulness or user-friendliness.
We included 3 variables from the OHD model [
H2a: CapSelf contributes to the intention to use wecoach.
H2b: CapTeam contributes to the intention to use wecoach.
H2c: CapOrg contributes to the intention to use wecoach.
Proposed study model with predictors from UTAUT and the OHD model. OHD: organizational health development; UTAUT: unified theory of acceptance and use of technology.
The participants in our study were nurse managers and nurse executives working in hospitals or nursing homes in Switzerland, Austria, and Germany. Nurses without leadership responsibilities were not included in the study. The rationale for this decision is that wecoach empowers team leaders to conduct a team development process, together with their staff, who do not directly interact with wecoach. Since nurse managers would be the primary users of wecoach, we were particularly interested in their acceptance of it.
Participants were identified by searching databases or publicly available lists of hospitals and nursing homes in all 3 countries. In some cases, an email address for the nursing director was directly available. In other cases, organization websites were listed, which were then searched for contact information of nursing directors, nurse managers. or other staff, such as human resource personnel, who might be in charge of team development or occupational health in nurses.
We contacted all the largest hospitals and nursing homes in all 3 countries. Additionally, using an online random generator, we also selected subsets of small and medium-size organizations in each canton or state. This varied slightly, based on the databases available for hospitals and nursing homes in each country; however, great efforts were undertaken to ensure that organizations of different sizes, from urban and rural areas and from all regions of each of the 3 countries, were included. The identified contacts were invited by email to participate in our study, and a flyer with further information was included. Participation in the study involved completing several modules of wecoach and then answering our online questionnaire (all in German; total time approximately 60-90 minutes). We sent out emails to 2269 recipients working in more than 500 organizations, deemed suitable for participating in or sharing the information about the study, such as nursing unit managers, nursing directors, or persons in charge of human resource development or occupational health and well-being. Persons interested in participating contacted the first author. To be included in the study, participants had to be working in a nursing leadership role with staff supervision responsibilities in either a hospital or a nursing home. Participants were asked to complete 4 modules of wecoach. The first module acquainted them with the technical interface, such as the chatbot and interactive forms. It also introduced them to general information about work and stress and asked them about their current level of confidence in undergoing health-oriented team development. The second module deepened the understanding of work, stress, and engagement; introduced users to the Job Demands-Resources model [
As an incentive, participants retained access to their fully active wecoach account, which allowed them to conduct an entire team development process free of charge. In total, 105 persons registered to participate in the study; however, many did not complete the wecoach modules or the questionnaire. The emails we received indicated that this was mainly due to time constraints. To encourage participation, we later provided an incentive of a raffle of 5 gift certificates for an online store worth €50 each (approximately US $54).
No ethical review of the study was necessary under federal, state, university, or departmental rules. The study was conducted under strict observation of ethical and professional guidelines.
We assessed the variables of UTAUT by modifying the items used by Venkatesh et al [
Items used to measure variables from UTAUTa and the OHDb model.
Variable | Itemsc |
Behavioral intention |
I intend to use wecoach within the next 6 months. I plan to use wecoach in the next 6 months. I mean to use wecoach in the next 6 months. |
Performance expectancy |
I find wecoach useful for conducting team development. I think that wecoach would make it easier for me to conduct team development. I think that wecoach can enable me to enhance the quality of team development. I think that wecoach can enable me to more efficiently conduct team development. I think that wecoach can convey the knowledge that I need to conduct team development. |
Effort expectancy |
I find that wecoach does what I want it to without problems. Operating wecoach is clear and easy to understand. Using wecoach does not require a lot of mental effort. I think that wecoach has intuitive user navigation. Learning to operate the system is easy. |
Social influence |
In general, I think that my organization would support the use of wecoach for team development. My fellow managers would support the use of wecoach for team development. My team would support the use of wecoach for team development. I think upper management would endorse the use of wecoach for team development. I would be more likely to use wecoach if my colleagues did so as well. |
Facilitating conditions |
I have the resources necessary to use wecoach. I have the technological know-how to be able to use wecoach. The wecoach tool is compatible with other systems I use. Assistance for using wecoach is available if I need it. Using wecoach fits with my way of working. Using wecoach fits with the human resource development strategy of our organization. |
CapSelfd |
I have the necessary competencies to do such team development. I am motivated to do such team development. This team development approach fits with my leadership style. |
CapTeame |
Our team has the competences necessary to undertake such team development. Our team is motivated to do such team development. Such team development fits with our team culture. |
CapOrgf |
The necessary resources (time, finances) are available, so one can conduct such team development. Conducting such team development is in line with our organizational goals. Such team development fits well with our organizational culture. |
aUTAUT: unified theory of acceptance and use of technology.
bOHD: organizational health development.
cRated on a scale from 1 to 7.
dCapSelf: capacities on the individual leader's level.
eCapTeam: capacities on the team level.
fCapOrg: capacities on the organizational level.
All statistical analyses were performed using SPSS Statistics version 24. To test our hypotheses, we conducted multiple linear regression analyses with all variables that were significantly correlated with our outcome variable, behavioral intention. First, we used the enter method, followed by another analysis using the stepwise method.
In total, 36 participants reviewed wecoach and completed our questionnaire. Of these, 4 (11%) were removed from the analysis for the following reasons: not having a leadership role, not working in a hospital or long-term care setting, or not registering for the study. Our final sample consisted of 32 persons. Descriptive data on our sample are presented in
Sample characteristics (N=32).
Characteristic | Respondents |
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Age (years), mean (SD) | 40.56 (7.76) |
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Female | 23 (71.9) |
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Male | 9 (28.1) |
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Hospital | 28 (87.5) |
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Long-term care | 2 (6.3) |
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Other | 2 (6.3) |
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Switzerland | 12 (37.5) |
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Austria | 12 (37.5) |
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Germany | 8 (25.0) |
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Upper | 6 (18.8) |
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Middle | 22 (68.8) |
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Lower | 4 (12.5) |
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Own motivation | 24 (75.0) |
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Were advised to | 8 (25.0) |
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Minutes spent in wecoach, mean (SD) | 137.75 (103.21) |
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The internal reliabilities of our scales ranged from .72 to .92. All variables were examined for outliers based on 2.2 IQRs [
We assessed group differences on the predictor and outcome variables based on sex, age, country, leadership level, and voluntariness of testing wecoach. No significant group differences were found in any of these. Note that no group comparisons were performed for work setting, since 28 (87.5%) of our final 32 participants worked in hospitals, while only 2 (6.25%) worked in long-term care and 2 (6.25%) in psychiatric acute care.
The variable of greatest interest to us was behavioral intention as an indicator of acceptance. Its mean level can be described as moderate. Of all assessed variables, it showed the highest degree of variability among participants.
Scores on UTAUTa and OHDb variables, correlations, and internal reliabilitiesc (N=32).
Variable | Meand (SD) | Correlation ( |
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Behavioral intention | Performance expectancy | Effort expectancy | Social influence | Facilitating conditions | CapSelfe | CapTeamf | CapOrgg |
Behavioral intention | 4.40 (1.94) | .92 | N/Ah | N/A | N/A | N/A | N/A | N/A | N/A |
Performance expectancy | 5.57 (0.99) | .49i | .92 | N/A | N/A | N/A | N/A | N/A | N/A |
Effort expectancy | 5.57 (0.88) | .36j | .25 | .81 | N/A | N/A | N/A | N/A | N/A |
Social influence | 4.78 (1.03) | .53i | .50i | .48i | .79 | N/A | N/A | N/A | N/A |
Facilitating conditions | 4.65 (0.98) | .52i | .59k | .53i | .78k | .72 | N/A | N/A | N/A |
CapSelf | 5.81 (0.67) | .29 | .39j | .50i | .37j | .37j | .81 | N/A | N/A |
CapTeam | 4.80 (1.15) | .61k | .52i | .46i | .67k | .60k | .44j | .93 | N/A |
CapOrg | 4.45 (1.36) | .34 | .38j | .32 | .65k | .57i | .40j | .63k | .87 |
aUTAUT: unified theory of acceptance and use of technology.
bOHD: organizational health development.
cInternal reliabilities are reported in the diagonal.
dRated on a scale from 1 to 7.
eCapSelf: capacities on the individual leader's level.
fCapTeam: capacities on the team level.
gCapOrg: capacities on the organizational level.
hN/A: not applicable.
i
j
k
To test our hypotheses, the predictors that were significantly correlated with the outcome variable, behavioral intention, namely performance expectancy, effort expectancy, social influence, facilitating conditions, and CapTeam, were entered into a multiple regression model. The assumptions for linear regression were tested and all met, with the possible issue of multicollinearity between social influence and facilitating conditions, which correlated at .776 (
We began by simultaneously including all 5 predictors using the enter method. This allowed us to examine the overall predictive power of the model as well as examine the respective β weights of the predictors in conjunction. The model explained 43.9% of the variance in behavioral intention (adjusted R2=.331). As
Contributions to behavioral intention: multiple regression analysis using the enter method.
Variable | Unstandardized coefficient B | SE | Standardized β | 95% CI | |
(Constant) | –.3013 | 2.240 | N/Aa |
.19 | –7.617 to 1.591 |
Performance expectancy | .441 | 0.345 | .226 | .21 | –.268 to 1.150 |
Effort expectancy | .015 | 0.450 | .007 | .97 | –.817 to .847 |
Social influence | .405 | 0.525 | .215 | .45 | –.674 to 1.484 |
Facilitating conditions | .129 | 0.554 | .065 | .82 | –1.011 to 1.268 |
CapTeamb | .485 | 0.349 | .288 | .18 | –.232 to 1.203 |
aN/A: not applicable.
bCapTeam: capacities on the team level.
Overlaps in explained variance may have caused the lack of any of the individual predictors reaching significance. To identify the most useful one(s), we also conducted stepwise multiple regression analysis. CapTeam was retained as the only predictor that uniquely contributed to behavioral intention with a standardized β of .582 (
Contributions to behavioral intention: stepwise multiple regression analysis.
Variable | Unstandardized coefficient B | SE | Standardized β | 95% CI | ||
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(Constant) | –.313 | 1.235 | N/Aa | .80 | –2.835 to 2.209 |
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CapTeamb | .981 | 0.250 | .582 | <.001 | 0.469-1.492 |
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Performance expectancy | .283 | N/A | N/A | .094 | N/A |
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Effort expectancy | .093 | N/A | N/A | .582 | N/A |
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Social influence | .324 | N/A | N/A | .105 | N/A |
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Facilitating conditions | .285 | N/A | N/A | .130 | N/A |
aN/A: not applicable.
bCapTeam: capacities on the team level.
A post hoc power analysis using G*Power (Faul, Erdfelder, Buchner, and Lang) [
Our findings were not able to confirm any of our hypotheses regarding the predictors of UTAUT (H1). None of the four predictors (ie, performance expectancy, effort expectancy, social influence, and facilitating conditions) significantly contributed to the acceptance of wecoach, indicated by behavioral intention. Of the 3 levels of capacities derived from the OHD model (H2), only CapTeam was found to be a significant predictor, although only after the other variables were excluded in the stepwise multiple regression analysis. Neither CapSelf nor CapOrg significantly contributed to behavioral intention. In summary, only H2b was partially confirmed.
The aim of our study was to examine factors that predict the acceptance of wecoach. In total, 32 nurse managers in Switzerland, Austria, and Germany tested several introductory modules of wecoach and completed our questionnaire, which assessed predictors from UTAUT [
The level of behavioral intention to use wecoach was moderate, while both performance expectancy and effort expectancy were quite high. This suggests that although users perceived wecoach as rather useful, they also perceived it as requiring some effort.
Our findings raise the question of whether UTAUT was an appropriate model to determine acceptance in our study. We see findings similar to ours in a study by Apolinário-Hagen et al [
This critique has been brought up repeatedly against models of technology acceptance. The use of a technology is not an end of its own, determined by how useful and user-friendly, but also by the perceived need for it, that is the task-technology fit [
Given the participatory nature of wecoach, it makes sense that factors relating to the fit on the team level strongly contributed to its acceptance. CapTeam was the strongest contributor to acceptance in our regression analysis and reached significance in the absence of other predictors. The availability of resources on the organizational level and alignment with organization goals, as indicated by CapOrg, however, did not seem immediately relevant for the acceptance of wecoach, although it could be speculated that those, alongside the facilitating conditions, might gain salience in the actual implementation.
The 3 items of the CapTeam scale assessed competence, motivation, and identity, and a closer inspection of the items revealed that 2 of them (
Consisting of not only a novel technological approach but also a novel approach to leadership and team development, wecoach may be too complex a tool to be suitably assessed with technology-related variables of UTAUT alone. Indeed, the intervention aspect of wecoach may have been more salient to the participants than the technology aspect of it. It would be interesting to further examine how users perceive and frame wecoach along these 2 dimensions.
As interventions become more sophisticated and more complex, especially in the work context, it is important to acknowledge the limitations of UTAUT and to recommend careful and deliberate selection of variables matched to the level at which innovations occur in order to better understand acceptance. Such fit-related aspects, informed by implementation science and intervention research, may serve as a gateway that determine acceptance before aspects such as usefulness or user-friendliness are even relevant. UTAUT may thus still be a suitable, although not a sufficient model, to understand the acceptance of complex technologies, and enhancing models with carefully selected variables can support researchers and practitioners in detecting the appropriate level to address facilitators of and barriers to their acceptance.
Several limitations need to be considered in interpreting our findings. First, our sample of 32 was small and represented only a tiny fraction of the persons we invited to participate. This means that our findings are difficult to generalize to a broader population of nurse managers, despite the satisfactory post hoc power analysis. This also increases the likelihood that our sample was biased and already interested in or open to workplace health promotion or digital interventions. Furthermore, although substantial efforts were undertaken to include staff working in nursing homes, only 2 (6.25%) participants did, limiting the conclusions that can be drawn about that setting. As with any study attempting predictions, longitudinal data would have allowed us to strengthen causal assumptions between the assessed variables. The inclusion of moderators might have also enhanced the predictive power of our model. However, although they contributed substantial variance explanation in the original UTAUT publication study [
Our study found that CapTeam is the only significant predictor of the intention to use wecoach. This implies that for successful dissemination of such a digitally supported participatory tool, the fit to the team, its culture, and its motivation are of much greater relevance than its technological aspects.
UTAUT has previously been 1 of the dominant models to determine acceptance of new technologies. Our findings suggest that in the case of complex technologies, this may not be the most appropriate model. As new technologies and digital interventions become more complex, it is important to supplement acceptance models through the careful selection of variables matched to the level at which the innovations occur. This can help researchers and practitioners identify the appropriate level to more fully understand acceptance and to address related barriers and facilitators to implementation and use of innovations.
capacities on the organizational level
capacities on the individual leader's level
capacities on the team level
organizational health development
unified theory of acceptance and use of technology
This work was supported by the UZH Foundation (ie, the foundation of the University of Zurich).
The data analysis and manuscript were prepared by SB, with support from GFB and GJJ. All authors critically reviewed and contributed to the manuscript and approved the final version.
GFB and GJJ are board members of the company that distributes wecoach.