Published on in Vol 2, No 4 (2021): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32336, first published .
Information Technology Ambidexterity, Digital Dynamic Capability, and Knowledge Processes as Enablers of Patient Agility: Empirical Study

Information Technology Ambidexterity, Digital Dynamic Capability, and Knowledge Processes as Enablers of Patient Agility: Empirical Study

Information Technology Ambidexterity, Digital Dynamic Capability, and Knowledge Processes as Enablers of Patient Agility: Empirical Study

Authors of this article:

Rogier van de Wetering1 Author Orcid Image ;   Johan Versendaal1 Author Orcid Image

Original Paper

Department of Information Sciences, Open University of the Netherlands, Heerlen, Netherlands

*all authors contributed equally

Corresponding Author:

Rogier van de Wetering, PhD

Department of Information Sciences

Open University of the Netherlands

Valkenburgerweg 177

Heerlen, 6419 AT

Netherlands

Phone: 31 45 576 22 22

Email: Rogier.vandeWetering@ou.nl


Related ArticlesPreprint (medRxiv): https://www.medrxiv.org/content/10.1101/2021.07.20.21260841v1
Preprint (JMIR Preprints): https://preprints.jmir.org/preprint/32336
Peer-Review Report by Ibrahim Adeleke (Reviewer AE): https://med.jmirx.org/2021/4/e34107/
Peer-Review Report by Joseph Walsh (Reviewer AO): https://med.jmirx.org/2021/4/e34110/
Peer-Review Report by Laura Taraboanta (Reviewer BQ): https://med.jmirx.org/2021/4/e34113/
Authors' Response to Peer-Review Reports: https://med.jmirx.org/2021/4/e34106/

Background: There is a limited understanding of information technology’s (IT) role as an enabler of patient agility and the department’s ability to respond to patients’ needs and wishes adequately.

Objective: This study aims to contribute to the insights of the validity of the hypothesized relationship among IT resources, practices and capabilities, and hospital departments’ knowledge processes, and the department’s ability to adequately sense and respond to patient needs and wishes (ie, patient agility).

Methods: This study conveniently sampled data from 107 clinical hospital departments in the Netherlands and used structural equation modeling for model assessment.

Results: IT ambidexterity positively enhanced the development of a digital dynamic capability (β=.69; t4999=13.43; P<.001). Likewise, IT ambidexterity also positively impacted the hospital department’s knowledge processes (β=.32; t4999=2.85; P=.005). Both digital dynamic capability (β=.36; t4999=3.95; P<.001) and knowledge processes positively influenced patient agility (β=.33; t4999=3.23; P=.001).

Conclusions: IT ambidexterity promotes taking advantage of IT resources and experiments to reshape patient services and enhance patient agility.

JMIRx Med 2021;2(4):e32336

doi:10.2196/32336

Keywords



Background

In the age of digital transformation, modern hospitals need to simplify their current care delivery processes and sustainable business models to contain the rising health care costs and address the needs of the more engaged and informed patient. At the same time, hospitals need to adequately address the confluence of dynamic and unpredictable market forces in which they operate, optimally deploy, and enable their information technology (IT) assets; resources; and organizational, IT, and knowledge capabilities and focus on the state-of-the-art patient service delivery [1-6]. Physicians and other health care providers can use innovative IT solutions and the available exponential volumes of patient-generated data—including the patient’s medical history in a single, easy-to-find location—to enhance the quality of care delivery [7-9]. As a result, hospitals today need to deal with a myriad of substantial organizational, political, and technological challenges over the coming years, also in the process of fully leveraging digital technologies [10,11]. Emerging technologies like big data analytics, the Internet of Things, distributed ledger technologies, social media, artificial intelligence (AI), and cloud-based solutions are, in essence, more than promising. These innovative technologies can truly disrupt the quality of processes and services, the effectiveness of medical outcomes, and the productivity of employees, and ultimately change lives [12-16]. Hospitals can now redefine their role in the hospital ecosystem so that the patient service quality and value might ultimately translate into substantial societal benefits [17].

Despite a wealth of attention for IT adoption and IT-enabled transformation in health care research [6,18-25], there is still a limited understanding of the role of IT as a crucial enabler of organizational sensing and responding capabilities to address the needs, wishes, and requirement of patients adequately [26-29]. Moreover, the extant scholarship has contended that IT could also hamper the process of gaining organizational benefits [30-33]. Understanding the facets that drive IT investment benefits is valuable in clinical settings [34]. As can be gleaned from this, substantial gaps remain in the extant literature. This paper, therefore, responds to two crucial limitations in the extant research. First, this paper tries to unfold how hospital departments can develop the ability to simultaneously pursue exploration and exploitation in their management of IT practices (ie, IT ambidexterity [35,36]) by practitioners often referred to as bimodal IT (eg, [37,38]) to drive a hospital departments’ digital dynamic capability. This technical-oriented dynamic capability, in essence, represents the degree to which qualities and competencies are developed to manage innovative digital technologies for new, exceptional, and effective patient service development [39]. As such, this capability requires substantial undertakings toward embracing new digital technologies [39,40]. Second, this study tries to unfold the complementary effect of IT ambidexterity and digital dynamic capability on hospital departments’ knowledge processes and their ability to adequately sense and respond to patient needs and wishes (ie, patient agility). Health care processes require close collaboration between different clinical departments and disciplines, and IT is crucial in facilitating effective knowledge processes between key stakeholders (eg, physicians, nurses, and health information management professionals) [8,41-43]. Hence, IT-driven knowledge processes can enhance patient treatment processes and patient agility.

Gaining these insights is essential, as hospitals are actively exploring their digital options and innovations, and transforming their clinical processes and interactions with patients using digital technologies [43,44]. For instance, clinicians who use digital innovations in their clinical practice (eg, mobile handheld devices and apps) can increase error prevention and improve patient-centered care [45-48]. In addition, digital options and innovations provide ways for clinicians to be more agile in their work, improve clinical communication, remotely monitor patients, and improve clinical decision support [49-52], and hence improve the patient treatment process and quality of medical services [51,53]. Moreover, recent scholarship advocates the deployment of knowledge assets, processes, and digital-driven sense and responding capabilities as a way of achieving higher quality and patient-centered care and financial performance benefits in hospitals [46,54,55]. Moreover, Fadlalla and Wickramasinghe [56] argue that patient-centered (care that is respectful of and responsive to individual patient preferences, needs, and values) sensing, responding, and digital capabilities are crucial in facilitating high-quality care.

These insights are also important for hospitals in the Netherlands, as Dutch hospitals are bound to care production agreements (ie, so-called turnover ceilings) between hospitals and health insurers. The Dutch Healthcare Authority, an autonomous administrative authority falling under the Dutch Ministry of Health, Welfare, and Sport, oversees that these agreements focus more on patient quality and value creation than production. Therefore, more contract negotiations will be driven by focusing on the quality of care and patient value, achieving patient agility seems a valuable endeavor. Thus, this research tries to extend existing work on IT-enabled transformation in health care and does so by sufficiently capturing clinicians’ attitudes toward IT ambidexterity, digital dynamic capability, knowledge processes, and patient agility of their hospital departments. In doing so, we adopt a practitioner-based approach [57,58].

Throughout this study, the dynamic capabilities framework is embraced [40,59,60]. As such, this study distinguishes between IT resources, a lower-order technical dynamic capability, and higher-order dynamic capabilities (ie, knowledge processes and patient agility) [40,61-63].

To summarize, the study’s main research questions are:

  • How does IT ambidexterity lead to perceived patient agility of the hospital department?
  • What is the role of digital dynamic capability and knowledge processes in the process of converting the contributions of IT ambidexterity on the department’s perceived patient agility enhancements?

This study’s IT business value approach aligns with the industries’ focus on operational and clinical excellence, patient-centered value, and a streamlined patient journey [64,65].

Hence, this paper proceeds as follows. First, it reviews the theoretical development by highlighting key literature on IT resources and ambidexterity, the dynamic capabilities view (DCV), and organizational agility. Second, section 3 highlights the study’s research model and associated hypotheses. Third, section 4 details the methods used in this study, after which section 5 outlines key results. Finally, this study discusses the outcomes, including theoretical and practical contributions, and ends with concluding remarks.

Theoretical Background

IT Resources and IT Ambidexterity

Organizations need to pursue and make trade-offs in practice between two seemingly opposing paths (ie, the ability to adapt existing IT resources to the current business environment and demands, and their focus on developing IT resources that contribute to long-term organizational benefits) [36,66]. The balance between these two objectives is referred to, in the literature, as ambidexterity [67-70]. Organizations’ simultaneous engagement of exploration and exploitation will likely provide them with superior business benefits [67-70]. IT exploration concerns the organization’s efforts to pursue new knowledge and IT resources [35,66], for instance, thinking about acquiring new IT resources (eg, potential IT applications and critical IT skills) and an organization’s ability to experiment with new IT management practices. On the other hand, IT exploitation is typically conceptualized as a construct that captures the degree to which organizations take advantage of existing IT resources and assets (eg, the reuse of existing IT applications and services for new patient services and the reuse of existing IT skills) [71,72].

Digital Dynamic Capability

Digital dynamic capabilities can be considered the “organization’s skill, talent, and expertise to manage digital technologies for new product development” [39]. Hence, it can be conceived as an organization’s ability to master digital technologies, drive digital transformations, and develop new innovative patient-centered services and products. Our study embraces a hierarchical capability view [35,40,59,60]. Thus, the digital dynamic capability is conceptualized as a lower-order technical dynamic capability that organizations could embed and leverage in the process of developing higher-order dynamic organizational capabilities such as innovation ambidexterity, absorptive capacity, and organizational adaptiveness [40,61,62]. This current conceptualization is also in line with the previous scholarship. However, the digital dynamic capability is tough to mimic and establish within organizations as it requires specific, idiosyncratic, and heterogeneous competencies to develop [73,74]. As such, this capability requires substantial undertakings toward embracing new digital technologies [39,40].

Dynamic Capabilities and Knowledge Processes

As its definition and conceptualizations suggest, digital dynamic capability builds upon a rich foundation of the DCV [74-78]. The DCV is a foundational strategic framework within the management and information system (IS) field [79,80], and is built from a multiplicity of theoretical roots [81]. The DCV claims that under conditions of high economic turbulence, traditional resource-based capabilities do not provide organizations with a competitive edge [82-84]. Instead, within this framework, organizations seek a balance between strategies to remain stable in the process of delivering current business services distinctively and mobile so that they can anticipate and effectively address market disruptions and business changes [84].

These dynamic capabilities have been defined and conceptualized as sets of measurable and identifiable routines that have been widely validated through empirical studies [78,85,86]. In general, these capabilities can be conceived as the organizations’ routines to integrate, build, reconfigure, gain, and release internal competences and resources to address changing market and business ecosystem demands [74,76]. In short, these capabilities can represent an organization’s ability to act under changing circumstances [59,87], a first derivative of traditional resource-based capabilities: the ability to contribute to maintaining a competitive edge continuously.

Although the knowledge-based view of organizations strongly builds upon the organizational learning theories and literature [88,89], recent studies converged both strategic management streams toward the core idea of knowledge-related dynamic capabilities. Knowledge processes represent the crucial operations for the input of knowledge assets [90]. They focus on generating, analyzing, and distributing customer information for strategy formulation and implementation [55,91,92]. In addition, hospital knowledge processes are important for patient care, as acquiring new medical knowledge and insights can substantially impact patients’ treatment [41]. Knowledge processes foster clinicians and medical staff to exchange and share medical and patient knowledge, and as such, these processes can be regarded as an effective way to integrate medical knowledge, enhance knowledge flow, and cultivate the use of evidence-based care that will likely have a positive impact on the quality of care [93,94]. As conceived in this study, knowledge processes are conceptualized as a dynamic capability [95,96].

The Concept of Organizational and Patient Agility

The DCV argues that organizations can respond to changing conditions while simultaneously and proactively enacting influence in the environment. Organizational agility has been considered a critical capability for sustained organizational success under the DCV [84]. This particular capability has been defined and conceptualized in many ways and through various theoretical lenses in the IS literature [26,97,98]. For instance, Park et al [99] ground their conceptualization and operationalization in the information-processing theory [100] and argue that information processing capabilities strengthen the organization’s sense-response processes to adapt to changing environmental conditions. Lu and Ramamurthy [101] embrace a complementarity perspective and perceive agility as the organization’s ability to seize market opportunities and operationally adjustment capacity. Chakravarty et al [26] adopt a contingency factors perspective and operationalize the multidimensional concept of agility through the organization’s ability to anticipate and proactively respond to market dynamics (ie, entrepreneurial agility and the organization’s ability to react to events without needing substantial strategic changes, that is, adaptive agility). A multidimensional view is also adopted by Lee et al [35], who likewise perceive organizational agility as a higher-order multidimensional dynamic capability that allows organizations to effectively and efficiently sense and respond to environmental conditions. Roberts and Grover [102] synthesized that, although there seems to be ambiguity in definitions as reflected by the concepts’ operationalized capabilities, a set of high-level characteristics can be devised from the extant literature. Hence, to a certain degree, all studies show two high-level organizational routines: deliberately sensing and responding to business events in the process of capturing business and market opportunities. These two organizational capabilities are crucial for organizations’ success [31]. Hence, our paper perceives patient agility as a higher-order manifested type of dynamic capability that allows hospital departments to adequately sense and respond to patient needs, demands, and opportunities within a turbulent and fast-paced hospital ecosystem context [43,84,102,103].

By addressing these crucial questions, this paper contributes to the medical informatics and IS literature by unfolding the mechanisms through which the dual capacity of IT exploration and IT exploitation simultaneously drives patient agility in hospital departments.

Research Model and Hypotheses

IT ambidexterity as a core organizational IT resource is expected to enhance hospital departments’ level of patient sensing and responding capability (both conceptualized as higher-order dynamic capability) through digital dynamic capability (as a lower-order technical dynamic capability) and knowledge processes. Figure 1 demonstrates the research model and the associated hypotheses that will be clarified further in the paper. For the sake of simplicity, the figure does not demonstrate included control variables.

Figure 1. Research model. H: hypothesis; IT: information technology.
View this figure

IT can be a transformative force in hospitals and contribute to enhanced patient services, efficiency and effectiveness gains, and clinical care [10,104]. However, IT implementations in hospitals are often exposed to cultural, organizational, and social challenges and inertia forces [10,104,105]. Therefore, an ambidextrous IT implementation strategy should be embraced, whereby short-term contributions (exploitation of current IT resources) and continuous progress of the IT resource portfolio (exploratory mode) drive IT-driven business transformation simultaneously [106]. When both short-term goals and ambitions are synchronized with the longer-term objectives, hospital departments are better equipped to develop digital capabilities and knowledge options, and to frame the hospital’s business strategy and clinical practice [39,98,107].

IT exploration can be considered an enabler of digital dynamic capability. This mode promotes the use of and experimentation with new IT resources (eg, new IT platform implementation, decision support functionality, big data and clinical analytics, and social media) as a basis to reshape existing patient services. On the other hand, IT exploitation focuses on using, enhancing, and repackaging existing IT resources (eg, reuse or redesigning current electronic medical record for new patient service development and ensuring hospitalwide accessibility to clinical patient data and information). Therefore, digital dynamic capability relates well to the dual capacity to aim for two disparate modes of operandi in managing the department’s skills, qualities, and competencies to manage digital technologies and developments—like mobile, social media, big data analytics, robotic process automation, AI, cloud computing, and Internet of Things—for new patient service delivery. However, in practice, many organizations struggle to reach IT ambidexterity results because of resource constraints and conflicting ambitions and motives [108]. As the individual qualities of IT ambidexterity may, to some extent, strengthen hospital departments’ digital options, they will likely not enhance the hospital department’s digital dynamic capabilities in isolation [98]. The simultaneous engagement of IT exploration and IT exploitation will enhance the qualities and competencies to manage innovative digital technologies for new patient service development, as they depend primarily on the organization’s investment decision to deploy simultaneous short-term improvement activities and long-term innovations [109].

Thus, IT resources play a substantial role in acquiring, processing, organizing, and distributing knowledge, and providing digital processes and knowledge options as enablers of agility [35,98,110]. This study argues that departments that can simultaneously exploit and explore their current IT resources portfolio will be better equipped to integrate existing and leverage new patient information sources, ensuring hospitalwide accessibility to clinical data and driving effective knowledge processes [111,112]. By leveraging the two modes of IT management practices, hospital departments can effectively integrate and analyze patient knowledge, use it for interdepartmental meetings, and identify new health service development needs.

In line with this reasoning, this study defines the following hypotheses:

  • Hypothesis 1: The greater the hospital department’s IT ambidexterity, the higher the degree of its digital dynamic capability will be.
  • Hypothesis 2: The greater the hospital department’s IT ambidexterity, the more effective its knowledge processes will be.

Digital dynamic capability is a crucial dynamic capability necessary to innovate and enhance business operations [39,61,113,114]. Various prior studies investigated the benefits that result from developing a digital dynamic capability. Wang et al [115] argue that digital dynamic capability allows leveraging IT and knowledge resources to deliver innovative services that customers value and that contribute to organizational benefits. Coombs and Bierly [116] empirically showed that a sophisticated digital dynamic capability enables competitive advantages. Thus, the extant literature shows that digital dynamic capability drives organizations to learn from experience in turbulent economic and competitive environments actively. Hence, in such an environment, it is essential to search continuously, identify, and absorb new knowledge and technological innovation such that they can be used to respond to changing customer behavior, demands, and wishes timely, adequately, and innovatively [28,113]. These claims are likewise consistent with results from Westerman et al [117], Khin and Ho [39], and Ritter and Pedersen [118], who showed that digital dynamic capability is crucial to deploy new innovative business models, enhance customer experiences, and improve business agility. Organizations can succeed in their digital options, products, and services by actively managing the opportunities provided by innovative technologies and responding to digital transformation [39].

A technological-driven capability is crucial for hospital departments that want to strive for patient agility in clinical practice because the process of achieving new digital patient service solutions is exceedingly dependent on its ability to manage digital technologies [39]. It requires proactively responding to digital transformation, mastering the state-of-the-art digital technologies, and deliberately developing innovative patient services using digital technology. Such a capability goes well beyond the notion of IT capabilities (ie, aggregation of IT resources and IT competencies in the vast majority of empirical studies) [119-121]. The development of a digital dynamic capability is tough to mimic and establish within hospital departments, as it requires specific, idiosyncratic, and heterogeneous competencies to develop [73,74].

The digital dynamic capability allows hospital departments, for example, to absorb and process sensitive patient information better, support clinicians in their decision-making processes, exchange clinical data, and facilitate patient health data accessibility [43,122]. As such, developing this capability makes the department more receptive to new patient data and information. The accumulation and storing of knowledge necessary to develop these new technologies also improve a firm’s ability to engage in transformation processes through its evaluation, use, and implementation. Finally, as a firm engages more in developing and mastering new technologies, they become more efficient in deploying the existing knowledge and, thus, generate more exploitative activities [123].

Hence, hospitals that actively invest and develop such a capability are likely to anticipate their patients’ needs (of which they might be physically and mentally unaware) and respond fast to changes in the patient’s health service needs using digital innovations and assessments of clinical outcomes [39,102,103]. Therefore, such a strategically significant capability is crucial for the departments’ focus on quality, efficiency, essential patient information, and enhancing the patient’s clinical journey. Based on the aforementioned arguments and building upon the DCV, the following two hypotheses are defined:

  • Hypothesis 3: The more developed the hospital department’s digital dynamic capability, the higher the hospital department’s patient agility.
  • Hypothesis 4: The more developed the hospital department’s digital dynamic capability, the more effective the hospital department’s knowledge processes will be.

Previous scholarship demonstrated that knowledge-based capabilities and agility are two crucial capabilities that mediate the impact of IT resources and capabilities on business benefits [98,124]. In the context of hospital departments, substantial investments in processing and analyzing patient data and information, and adequate interdepartmental knowledge and information flow will drive the department’s ability to anticipate the patients’ current and future needs [92]. In clinical practice, the diagnosis and treatment processes are composed of a multitude of interactions and coordination between care activities in different activity levels and multiple types of knowledge [53]. Moreover, departments that are more aware of their patient needs through information knowledge processes are likely to harness new patient knowledge more effectively, make better clinical practice decisions, and support the treatment process [53,92,124]. Thus, through knowledge processes, the department can develop and redesign its core processes and capabilities. Mature knowledge-based processes drive transfer of knowledge across and within the department, uniquely deploy knowledge resources, and allow hospital departments to enhance business processes and services, and better sense and seize business and patient service opportunities that ultimately can enhance business performance [55,84,87]. Recently, scholars showed that data and knowledge-driven capabilities, as intermediate constructs, contribute to hospital performance enhancements [125,126]. Moreover, in hospital departments, patient agility as a crucial capability describes the competence of the health care providers’ ability to create patient value and drive patient satisfaction in a way that uniquely uses knowledge resources and processes [46,55].

In sum, this study argues that knowledge processes are crucial in the process of reconfiguring its existing patient sensing and responding capabilities [96] and that these capabilities, to a great extent, rely on the integration of knowledge processes in the department [55,88,112]. Hence, this study defines the following:

  • Hypothesis 5: The more effective the hospital department’s knowledge processes, the higher the hospital department’s patient agility.

A deductive and quantitative approach was used to address the study objectives. Hence, hypothesized relationships among key constructs are analyzed by first cross-sectionally collecting field data and then analyzing the obtaining survey data.

Data Collection Tool and Procedure

An online survey was developed to capture clinicians’ attitudes toward IT ambidexterity, digital dynamic capability, knowledge processes, and patient agility of their hospital departments. Hence, we adopted a practitioner-based approach that used subjective measures because hospitals are typically more willing to provide subjective data than sensible objective performance metrics (eg, [57,58]). In practice, perceptual measures on processes and practices positively correlate with objective data [127].

This survey was pretested on multiple occasions by 5 master’s students and 6 medical practitioners and scholars to improve the survey items’ content and face validity. The medical practitioners all had sufficient knowledge and experience to assess the survey items effectively to provide valuable improvement suggestions. Within the survey, comprehensible construct definitions were provided, and the survey followed a logical structure. In one of the final questions, the participants were asked if they wanted to receive critical insights from the study. Various controls were also built during the data collection process so that each department completed the survey only once.

Study Population

The target population was (clinical) department heads and managers, team leads, and physicians under the assumption that, at the hospital department level, these health care providers are actively involved in contact with patients or at least have intelligible insight into the department’s patient interactions and the use of IT. Moreover, these are the foremost stakeholders who can provide insights into the unique and sometimes complicated situations where medical knowledge is exploited, enabling a unique treatment course [55]. This approach is a similar approach taken by many other key publications in the field surveying clinicians to obtain insights into how patient-based information affects the diagnosis, therapy, patient safety, and overall clinical practice and care to patients [55,128-130]. Therefore, these providers were considered to be the most important subject in this survey. In addition, our single informant strategy is consistent with prior literature on specialized not diversified units and departments [131].

Sampling Techniques

Data were conveniently collected between November 10, 2019, and January 5, 2020, sampled from Dutch hospitals through the 5 master’s students’ professional networks within Dutch hospitals. Convenient sampling is a nonprobability sampling method where the sample is taken from a group of people easy to contact or reach and fit the profile [132].

Sample Size and Inclusion and Exclusion Criteria

The survey software registered 230 active and unique participants. However, 101 cases had to be removed because of unreliable data entries or no entries at all. Additionally, 1 respondent (administrative function) did not belong to our target population and had to be removed from the sample. In a final step, 21 additional participants had to be removed due to substantial missing values (ie, more than 15%). Therefore, this study used 107 complete survey responses for final analyses.

Constructs and Items

The selection of constructs and measures was made following previous empirically validated work. Additionally, this study includes only measures that were suitable for departmental-level analyses. Since this research was done in a health care setting, some original items had to be slightly reworded to fit the particular context. IT ambidexterity is operationalized using the item-level interaction terms of IT exploration and IT exploitation [35,69]. Items were adopted from Lee et al [35]. This study used three measures from Khin and Ho [39] for digital dynamic capability to represent the department’s capability to manage innovative digital technologies for new patient service development. Patient knowledge processes refer to critical activities within the department that focus on generating, analyzing, and distributing patient-related information for strategy formulation and implementation. Six items based on the work of Jayachandran et al [92] are adopted. Patient agility concerns the departments’ ability to sense and respond to patient needs adequately and is modeled as a higher-order (second) dynamic capability comprising the first-order dimensions patient sensing capability and patient responding capability [28,31,98]. Hence, this study used 10 empirically validated measures from Roberts and Grover [28] (see Multimedia Appendix 1 for a complete overview of the construct and their associated items with their respective item-to-construct loadings [λ], mean values [μ], and the SDs). All of the aforementioned items were measured using a 7-point Likert scale.

This study controlled the outcomes for both size, measured as full-time employees (log-normally distributed), and age of the department (5-point Likert scale, 1: 0-5 years; 5: >25 years).

Ethics Considerations

Participants were allowed to complete the survey anonymously, and we did not log anything in the survey system that could trace participants. The participants could withdraw their entries if they wanted to. In addition, reusable personal data was not requested, and the survey did not include questions about personal or sensitive topics [132]. Furthermore, before starting the survey, the participants had to sign a consent form. This approach is in line with the General Data Protection Regulation. Finally, participants were given the option to leave their email addresses to receive a research report. These email addresses were removed from the data set after this report was sent.

Data Analyses and Management

Model Estimation Procedure

The research model’s hypothesized relationships are tested using partial least squares (PLS) structural equation modeling (SEM). To estimate and model parameters, SmartPLS version 3.2.9. was used [133]. In essence, PLS-SEM allows assessing both the measurement model (ie, outer model) [134] and the structural model (ie, inner model) of the research model so that hypotheses can be tested [135]. The PLS algorithm establishes latent constructs from the factor scores. It, thereby, seemingly avoids factor indeterminacy [136] so that these scores then be applied in the following analyses [137]. A fundamental justification for using PLS-SEM is that its use is appropriate in exploratory contexts and for the objective of theory development [136]. In this research, the focus is on prediction as to which the PLS algorithm assesses the explained variance (R2) for all dependent constructs [136]. Additionally, PLS is less strict in terms of particular data distributions [134].

Sample Justification

Another reason to justify the variance-based approach is that the current sample is relatively small [138]. However, the sample size does exceed minimum threshold values to obtain stable PLS outcomes [139]. A power analysis was done using G*Power [140]. Hence, this study assumes the conventional 80% statistical power and a 5% probability of error as input parameters, while the maximum number of predictors in the research model is three (when including the nonhypothesized direct effect of IT ambidexterity on agility). Based on G*Power’s output parameters, a minimum sample of 38 cases were needed to detect an R2 of at least 24%. The current sample of 107 far exceeds this minimum requirement. The estimation procedure makes use of the general recommended path weighting scheme algorithm [133].

Nonresponse Bias

This study accounts for possible nonresponse bias by using a t test to assess whether or not there is a significant difference between the early participants (n=66) and the late subsample (n=41 participants) on the responses on the Likert scale questions. This assessment is crucial as nonresponse bias can significantly impact the study outcomes and requires careful examination [141,142]. Hence, this study included various elements, including department age, the number of patients, and all construct items in the assessment. Furthermore, no significant difference could be detected after running the analyses and assessing the Levine equality test (of variances) and the t test for equality of mean values. Hence, this confirms the absence of nonresponse bias. Finally, per suggestions of Richardson et al [143] and Podsakoff et al [144], Harman single-factor analysis was applied using exploratory factor analysis (using SPSS Statistics v24, IBM Corp) to restrain possible common method bias [143,144]. Hence, this study sample is not affected by method biases, as no single factor is attributed to the majority of the variance.


Sample Demographics

Within the final sample of 107 participants, 36 (33.6%) work for a university medical center, 41 (38.3%) work for a specialized top clinical (training) hospital, and the final 30 (28%) work for general hospitals. Table 1 shows the demographics of the participating hospital department (see also Multimedia Appendix 2 for an overview of the survey responses per medical department).

Table 1. Demographics of participating hospital departments.
Element and categoriesParticipants, n (%)
Hospital type

University medical center36 (33.6)

Top clinical training hospital41 (38.3)

General hospital30 (28)
Department age (years)

0-528 (26.2)

6-1020 (18.7)

11-2020 (18.7)

20-258 (7.5)

>2531 (29.0)
Experience at this particular department (years)

0-549 (45.8)

6-1018 (16.8)

11-2028 (26.2)

20-256 (5.6)

>256 (5.6)
Amount of patients

<400025 (23.4)

4000-650021 (19.6)

6500-900012 (11.2)

9000-11,50012 (11.2)

11,500-14,00011 (10.3)

≥14,00026 (24.3)
Assessment of the Measurement Model

Various analyses were done to determine the reliability and validity of the study constructs. This is a crucial step before testing the study’s hypotheses and evaluating the quality of the research.

In the first step, the internal consistency reliability is investigated using both the Cronbach alpha measure and the composite reliability estimation value. In a subsequent step, this study assessed the convergent validity—using the average variance extracted (AVE)—of the first-order latent constructs [133]. All the AVE values exceeded the lowest recommended mark of 0.50 [145]. Construct-to-item loadings were likewise investigated to determine the degree to which a variable contributes to explaining the variance of a particular construct while considering the other measurements. These loading also exceeded minimum thresholds. In a final step, discriminant validity was established through the assessment of three tests. First, cross-loadings were investigated [146]. Analyses show that all items load more strongly on their intended latent constructs than they correlate on other constructs (see also Multimedia Appendix 3). Second, the well-known Fornell-Larcker criterion is used [145]. In doing so, the square root of the AVE (see the diagonal entries in italics in Table 2) is compared with cross-correlation values. With this, each square root value should be larger than the cross-correlations [135]. As can be gleaned from Table 2, all Fornell-Larcker values (ie, square root of the AVE) are higher than the shared variances of the constructs with other constructs in the model. In a final step, a newly developed measure, the heterotrait-monotrait ratio of correlations (HTMT), was used [147]. In general, acceptable outcomes of this analysis are HTMT values that are below 0.85 (upper bound). Discriminant validity is established between constructs. The HTMT analyses show that all values are well below the threshold value of 0.85. Table 2 summarizes the entire assessment. The higher-order (formative) construct of patient agility was assessed using variance inflation factor (VIF) values for the constructs patient sensing and patient responding capability. These VIF values were well below the conservative threshold of 3.5. Hence, no multicollinearity was present within the research model [148].

As the reliability and validity of the model are now established, the model’s associated fit indices can be assessed as well as the hypothesized relationships using the structural model.

Table 2. Convergent and discriminant validity assessment.
ConstructsAVEaCAbCRcEXPLRdEXPLOeDDCfPSCgPRChKPi
EXPLR0.888.8670.9190.942j




EXPLO0.7900.9370.9600.5020.889



DDC0.7830.8620.9160.5840.6310.885


PSC0.7230.9040.9290.3750.5020.5880.850

PRC0.7920.9340.9500.3130.3410.4520.5080.890
KP0.6160.8750.9060.4630.5120.5520.7130.3930.785

aAVE: average variance extracted.

bCA: Chronbach alpha measure.

cCR: composite reliability estimation.

dEXPLR: information technology exploration.

eEXPLO: information technology exploitation.

fDDC: digital dynamic capability.

gPSC: patient sensing capability.

hPRC: patient responding capability.

iKP: patient knowledge processes.

jThe square root of the AVE was compared with cross-correlation values.

Model Fit Assessments

This study used three metrics, that is, (1) standardized root mean square residual (SRMR; note, however, that the first two metrics for model fit should be interpreted with caution as these metrics are not fully established PLS-SEM evaluation criteria), (2) Stone-Geisser test, and (3) the variance explained by the model (R2) to assess the model’s goodness-of-fit. We tried to get insights into how well the research model fits with the data obtained with these analyses.

First, the newly developed SRMR metric was calculated. The SRMR metric calculates the difference between observed correlations and the model’s implied correlations matrix [135,149]. The obtained SRMR of 0.059 is well below the conservative threshold mark of 0.08, as proposed by Hu and Bentler [149]. Second, the Stone-Geisser test (Q2) was calculated using the blindfolding procedure to assess the model’s predictive relevance. Hence, the current model’s Q2 values (for endogenous constructs) all far exceed 0, indicating the overall model’s predictive relevance.

Finally, R2 values were analyzed. The structural model explained 47% of the variance for digital dynamic capability (R2=0.47). The explained variance for patient knowledge processes is 36% and for patient agility 51%. These R2 outcomes are considered moderate to substantial effects [150]. Based on the assessed four metrics, it can be concluded that the research model performs well compared with the base values and that sufficient model fit was obtained to test the hypotheses.

Assessment of the Structural Model and Hypotheses Testing

Following the model fit assessments and the assessment of the measurement model, we tested the hypotheses of the study that we developed in the section Research Model and Hypotheses.

Based on the outcomes of the nonparametric bootstrap resampling procedure [135], this study found support for the first hypothesis, that is, IT ambidexterity positively impacts digital dynamic capability (β=.69; t4999=13.43; P<.001). Thus, our results showed that IT exploration and exploitation’s simultaneous engagement enhances the qualities and competencies to manage innovative digital technologies for new patient service development. Likewise, this study found support for hypothesis 2 (ie, IT ambidexterity → knowledge processes: β=.32, t4999=2.85; P=.005). Therefore, the outcomes showed that hospital departments that exploit and explore their current IT resources portfolio integrate and leverage patient information sources and drive effective knowledge processes.

Digital dynamic capability was positively associated with patient agility (β=.36; t4999=3.95; P<.001), providing support for hypothesis 3. The outcomes proved that digital dynamic capability is crucial for hospital departments that strive for patient agility in clinical practice. In addition, the structural model results support hypothesis 4 (ie, digital dynamic capability → knowledge processes: β=.33; t4999=3.23; P=.001). Hence, hospital departments that engage in developing and mastering new technologies are more efficient in deploying the existing knowledge and, thus, generate more exploitative activities and effective knowledge processes. The results also showed that digital dynamic capability partially mediates the effect of IT ambidexterity on knowledge processes [135,151]. Finally, the results support hypothesis 5 (ie, knowledge processes are positively associated with patient agility: β=.45; t4999=5.35; P<.001). Therefore, these outcomes suggested that patient agility relies on the integration of knowledge processes in the department to a great extent. Furthermore, it can be concluded that also partial mediation characterizes the triangular relationship between digital dynamic capability, knowledge processes, and patient agility.

The bootstrapped PLS results showed nonsignificant effects for the included control variables: size (β=–.10; t4999=0.79; P=.86) and age (β=–.01; t4999=0.17; P=.43). Figure 2 summarizes the structural model assessment results.

Figure 2. Structural model results. IT: information technology.
View this figure

General Discussion

The digital transformation brings about an unprecedented challenge for modern day hospitals [152,153]. Decision makers and stakeholders across the hospital need to make sure that digital resources and technological innovations are aligned and deployed with care to enhance efficiencies, decision-making, and quality of services so that personalized and patient-centered care can be delivered [154]. Thus, it is needless to say that digital innovations can improve existing processes and medical procedures for diagnostics and patient treatment.

Using data from 107 hospital departments in the Netherlands, this study showed that the simultaneous engagement of IT exploration and IT exploitation within hospital departments (ie, IT ambidexterity) enhances the qualities and competencies to manage innovative digital technologies for new patient service development (ie, digital dynamic capability). In addition, the outcomes showcase that the greater the hospital departments’ IT ambidexterity, the more effective are its knowledge processes. Furthermore, our results show that actively investing in digital dynamic capability is essential when departments want to enhance quality and patient clinical journeys. In particular, our study demonstrates that the more developed the hospital department’s digital dynamic capability, the more effective will be the hospital department’s knowledge processes. This outcome is important, as knowledge processes facilitate transforming clinical data into patient-related insights, thereby supporting clinicians within hospitals to make informed decisions concerning diagnosis and treatment. Our study shows that these data-driven processes allow clinicians to improve the patient treatment process and medical quality services and be more agile in their work, which is in line with the theory by Li et al [53]. Finally, our results show that digital dynamic capability is a crucial driver of patient agility, conceptualized as a dynamic capability. This capability provides the hospital department with adequate responsiveness by enabling the flexibility to sense and respond to patient demands and needs.

This study makes substantial theoretical and practical contributions, which will be discussed next.

Implications for Theory and Practice

The process of digitizing existing patient services and developing new digital solutions remains time-consuming and challenging in many ways. In addition, from a research perspective, there is still a limited understanding of how IT resources and the digital capability-building processes can facilitate patient agility and contribute to the much needed insights on obtaining value from IT at the departmental level [35,155,156]. This study aims at addressing these particular gaps in the literature. Notably, this study designed and tested a research model, using a sample of 107 hospital departments from the Netherlands, arguing that IT ambidexterity would drive a department’s patient agility by first enabling digital dynamic capability and knowledge processes. Outcomes of this study found support for these foundational claims. Furthermore, this study’s structural model analyses unfolded that IT ambidexterity, which is a crucial antecedent of digital dynamic capability and knowledge processes. These crucial capabilities and processes, in turn, substantially impact the departments’ ability to adequately sense and respond to patient needs and wishes (ie, patient agility).

Evidence unfolded that digital dynamic capability partially mediates the relationship between IT ambidexterity and knowledge processes. Similarly, a partial mediation characterizes the triangular relationship between digital dynamic capability, knowledge processes, and patient agility. These outcomes corroborate existing IT-enabled agility and dynamic capability studies [102,124,157,158]. The results also support the core idea that the hospital department’s capacity to obtain value from its knowledge assets is a crucial success factor in achieving patient agility [110,158].

This study embraces the dynamic capabilities and knowledge-based view of IT resource deployments and advances the current insights on the resource and capability-building perspective [74,98,101,155]. It does so by unfolding the nomological path from resources to the IT-enabled value perspective [21].

Outcomes of this study suggest that hospitals—that are committed to the process of ambidextrously managing their IT resources—are more proficient in promptly sensing and responding to patients’ medical needs and demands. These theoretical contributions are valuable as these particular insights remained unclear in the extant literature, and future research can take these insights into account when investigating the IT benefits in hospitals. Likewise, unfolding the benefits of hospital departments’ dual capacity to aim for two disparate things at the same time using empirical data is relevant from a practical perspective, as the business value of IT and the preceding IT investments can be justified [35,159,160]. The outcomes corroborate with the “theory of swift and even patient flow” [44] in that digital capabilities support the process of optimizing current hospital assets and help adequately respond to patient’s needs by improving hospital operations (eg, better diagnoses, scheduling, and coordination of patient care). Hence, it supports the call for researchers to demonstrate the best ways to optimize digital health care solutions [21].

This study provides hospital department managers and decision makers with valuable practical implications. Hospital departments must direct IT investments to bring about the highest IT business value, given the many substantial challenges to ensure high quality across the patient care delivery continuum. This research shows that IT ambidextrous departments can adequately develop new innovative digital opportunities and patient services to enhance the hospital department’s knowledge processes and patient agility levels. This development path is crucial for successful hospital departments that strive to enhance the patient’s clinical journey and provide patients with fitting health services. However, it is important to note that IT ambidexterity can help hospital departments indirectly obtain high levels of patient agility. However, this development might be hindered if departments do not fully leverage their dual IT exploration and exploitation capacity to drive digital dynamic capability and knowledge processes and enhance patient agility.

Digital dynamic capability is crucial in the development of knowledge processes and patient agility. Hospital department managers should develop the core competencies, knowledge, and skills to process patient information better, adequately respond to digital transformation, master the state-of-the-art digital technologies, and deliberately develop innovative patient services using digital technology. Hospital department managers should also be aware of the crucial role of knowledge processes. Mature knowledge processes enhance decision-making processes and drive patient agility in hospital departments. Therefore, they should dedicate their resources to leverage these capabilities fully so that they are better equipped to search, identify, and absorb new technological innovations; integrate, process, and exchange patient information; and use them for decision-making processes, and to anticipate and respond fast to changes in the patient’s health service needs. Our study results highlight the need for hospital departments to focus more on patient agility, a crucial antecedent of enhanced patient care. Hospital department managers and decision makers should also deliberately pay attention to end user’s psychological meaningfulness, stakeholder involvement, and providing adequate resourcing and infrastructures when implementing new digital technologies [33,161-163]. These aspects are crucial when implementing new digital technologies so that the hospital staff is supported and perceived value can be related to individual behavior changes and key stakeholders’ needs and expectations. The outcomes are particularly relevant for practitioners now, as hospitals worldwide need to take action to transform health care delivery processes using digital technologies and increase clinical productivity during the COVID-19 crisis [164].

In summary, hospital departments should strive to be agile in the modern turbulent economic environment. This study provides crucial insights and guidance to achieve this.

Limitations and Future Studies

Several study limitations should be mentioned. These limitations suggest future research avenues. This study used self-reported data to test the developed hypotheses as obtaining objective measures is typically a challenging endeavor. The decision to use self-reported data is still justifiable as empirical outcomes, as these data types are strongly correlated to objective measures [127,165,166]. Another concern is that data were collected using the single informant strategy. As such, method bias could still be a concern. This study did pay considerable attention to account for possible measurement errors and method bias. Future research could embrace a matched-pair design where different participants address independent (explanatory) and dependent constructs. Another opportunity for future research is triangulating the included measures with, for example, potentially available archival data from public sources. These insights, next to possibly applying the current model to other countries, could help validate the outcomes further. In addition, a more substantial sample of hospital departments will further contribute to the robustness of the results. Scholars could confirm this research’s outcomes using a replication study in different (non-Western) countries. Future research could also investigate patient agility, focusing on specific departments, as this study encompasses various participating departments. Focusing on a few departments with more responses could capture a richer view of the subject matter.

Finally, this study did not include patient service performance outcomes and benefits beyond this paper’s scope. Hence, it would be interesting to investigate the relationships between patient agility and the hospital department’s performance outcomes, as patient agility is considered a crucial ingredient in delivering high-quality patient value and overall streamlined patient journeys. Hence, this research’s outcomes inform further research about whether patient agility impacts clinical care quality and efficacy. Scholars could then investigate patient agility’s contribution to increasing, for example, clinical productivity and quality enhancement during different stages of the COVID-19 pandemic [164]. Finally, future work could also involve the patient engagement and digital technology co-design perspectives [163,167,168].

Acknowledgments

We want to thank Josja Willems, Reinier Dickhout, Rick Smulders, Yves-Sean Mahamit, and Renaldo Kalicharan for their valuable contributions to the data collection and for sharing their perspectives in numerous discussions. Additionally, we would like to express our gratitude to the 107 participating hospitals. Your active role made this a success.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Survey constructs and items and descriptive statistics.

DOCX File , 19 KB

Multimedia Appendix 2

Survey response per medical department.

DOCX File , 16 KB

Multimedia Appendix 3

Cross-loading analysis for the first-order factors.

DOCX File , 18 KB

  1. Kostopoulos KC, Spanos YE, Prastacos GP. The resource-based view of the firm and innovation: identification of critical linkages. Second Eur Acad Manage Conference 2002:1-19.
  2. Hitt MA, Hoskisson RE, Kim H. International diversification: effects on innovation and firm performance in product-diversified firms. Acad Manage J 1997 Aug;40(4):767-798. [CrossRef]
  3. Aral S, Weill P. IT assets, organizational capabilities, and firm performance: how resource allocations and organizational differences explain performance variation. Organ Sci 2007 Oct;18(5):763-780. [CrossRef]
  4. Joshi KD, Chi L, Datta A, Han S. Changing the competitive landscape: continuous innovation through IT-enabled knowledge capabilities. Inf Syst Res 2010 Sep;21(3):472-495. [CrossRef]
  5. Forés B, Camisón C. Does incremental and radical innovation performance depend on different types of knowledge accumulation capabilities and organizational size? J Business Res 2016 Feb;69(2):831-848. [CrossRef]
  6. Feldman SS, Buchalter S, Hayes LW. Health information technology in healthcare quality and patient safety: literature review. JMIR Med Inform 2018 Jun 04;6(2):e10264 [FREE Full text] [CrossRef] [Medline]
  7. McCullough JS, Casey M, Moscovice I, Prasad S. The effect of health information technology on quality in U.S. hospitals. Health Aff (Millwood) 2010 Apr;29(4):647-654. [CrossRef] [Medline]
  8. Lenz R, Reichert M. IT support for healthcare processes – premises, challenges, perspectives. Data Knowledge Eng 2007 Apr;61(1):39-58. [CrossRef]
  9. Girgis A, Durcinoska I, Levesque JV, Gerges M, Sandell T, Arnold A, PROMPT-Care Program Group. eHealth system for collecting and utilizing patient reported outcome measures for personalized treatment and care (PROMPT-Care) among cancer patients: mixed methods approach to evaluate feasibility and acceptability. J Med Internet Res 2017 Oct 02;19(10):e330 [FREE Full text] [CrossRef] [Medline]
  10. Kohli R, Tan SS. Electronic health records: how can IS researchers contribute to transforming healthcare? MIS Q 2016 Mar 3;40(3):553-573. [CrossRef]
  11. Sligo J, Gauld R, Roberts V, Villa L. A literature review for large-scale health information system project planning, implementation and evaluation. Int J Med Inform 2017 Jan;97:86-97. [CrossRef] [Medline]
  12. Kruse CS, Beane A. Health information technology continues to show positive effect on medical outcomes: systematic review. J Med Internet Res 2018 Feb 05;20(2):e41 [FREE Full text] [CrossRef] [Medline]
  13. Elton J, O'Riordan A. Healthcare Disrupted: Next Generation Business Models and Strategies. Hoboken, NJ: John Wiley & Sons; 2016.
  14. Zheng X, Sun S, Mukkamala RR, Vatrapu R, Ordieres-Meré J. Accelerating health data sharing: a solution based on the Internet of Things and distributed ledger technologies. J Med Internet Res 2019 Jun 06;21(6):e13583 [FREE Full text] [CrossRef] [Medline]
  15. Kuo AM. Opportunities and challenges of cloud computing to improve health care services. J Med Internet Res 2011 Sep 21;13(3):e67 [FREE Full text] [CrossRef] [Medline]
  16. Chen P, Lin C, Wu W. Big data management in healthcare: adoption challenges and implications. Int J Inf Manage 2020 Aug;53:102078. [CrossRef]
  17. Lin Y, Lin M, Chen H. Do electronic health records affect quality of care? Evidence from the HITECH Act. Inf Syst Res 2019 Mar;30(1):306-318. [CrossRef]
  18. Paré G, Jaana M, Sicotte C. Systematic review of home telemonitoring for chronic diseases: the evidence base. J Am Med Inform Assoc 2007;14(3):269-277 [FREE Full text] [CrossRef] [Medline]
  19. Chiasson M, Reddy M, Kaplan B, Davidson E. Expanding multi-disciplinary approaches to healthcare information technologies: what does information systems offer medical informatics? Int J Med Inform 2007 Jun;76 Suppl 1:S89-S97. [CrossRef] [Medline]
  20. Wang Y, Kung L, Wang WYC, Cegielski CG. An integrated big data analytics-enabled transformation model: application to health care. Inf Manage 2018 Jan;55(1):64-79. [CrossRef]
  21. Jones SS, Heaton PS, Rudin RS, Schneider EC. Unraveling the IT productivity paradox--lessons for health care. N Engl J Med 2012 Jun 14;366(24):2243-2245. [CrossRef] [Medline]
  22. Chiasson MW, Davidson E. Pushing the contextual envelope: developing and diffusing IS theory for health information systems research. Inf Organ 2004 Jul;14(3):155-188. [CrossRef]
  23. Eslami Andargoli A, Scheepers H, Rajendran D, Sohal A. Health information systems evaluation frameworks: a systematic review. Int J Med Inform 2017 Jan;97:195-209. [CrossRef] [Medline]
  24. Nair A, Dreyfus D. Technology alignment in the presence of regulatory changes: the case of meaningful use of information technology in healthcare. Int J Med Inform 2018 Feb;110:42-51. [CrossRef] [Medline]
  25. Mackert M, Mabry-Flynn A, Champlin S, Donovan EE, Pounders K. Health literacy and health information technology adoption: the potential for a new digital divide. J Med Internet Res 2016 Oct 04;18(10):e264 [FREE Full text] [CrossRef] [Medline]
  26. Chakravarty A, Grewal R, Sambamurthy V. Information technology competencies, organizational agility, and firm performance: enabling and facilitating roles. Inf Syst Res 2013 Dec;24(4):976-997. [CrossRef]
  27. Rai A, Tang X. Leveraging IT capabilities and competitive process capabilities for the management of interorganizational relationship portfolios. Inf Syst Res 2010 Sep;21(3):516-542. [CrossRef]
  28. Roberts N, Grover V. Leveraging information technology infrastructure to facilitate a firm's customer agility and competitive activity: an empirical investigation. J Manage Inf Syst 2014 Dec 08;28(4):231-270. [CrossRef]
  29. Fink L. How do IT capabilities create strategic value? Toward greater integration of insights from reductionistic and holistic approaches. Eur J Inf Syst 2017 Dec 19;20(1):16-33. [CrossRef]
  30. Brynjolfsson E, Hitt LM. Beyond computation: information technology, organizational transformation and business performance. J Econ Perspect 2000 Nov 01;14(4):23-48. [CrossRef]
  31. Overby E, Bharadwaj A, Sambamurthy V. Enterprise agility and the enabling role of information technology. Eur J Inf Syst 2017 Dec 19;15(2):120-131. [CrossRef]
  32. Carr NG. IT doesn't matter. Harvard Business Rev 2003:5-12.
  33. Kumar M, Singh JB, Chandwani R, Gupta A. “Context” in healthcare information technology resistance: a systematic review of extant literature and agenda for future research. Int J Inf Manage 2020 Apr;51:102044. [CrossRef]
  34. Hessels A, Flynn L, Cimiotti JP, Bakken S, Gershon R. Impact of heath information technology on the quality of patient care. Online J Nurs Inform 2015;19:1 [FREE Full text] [Medline]
  35. Lee O, Sambamurthy V, Lim KH, Wei KK. How does IT ambidexterity impact organizational agility? Inf Syst Res 2015 Jun;26(2):398-417. [CrossRef]
  36. Ortiz de Guinea A, Raymond L. Enabling innovation in the face of uncertainty through IT ambidexterity: a fuzzy set qualitative comparative analysis of industrial service SMEs. Int J Inf Manage 2020 Feb;50:244-260. [CrossRef]
  37. Gaughan D. Use bimodal and pace-layered IT together to deliver digital business transformation. Gartner Res 2016:1-15.
  38. Mesaglio M, Adnams S, Mingay S. Kick-start bimodal IT by launching mode 2. Gartner Res 2015:1-12.
  39. Khin S, Ho TC. Digital technology, digital capability and organizational performance: a mediating role of digital innovation. Int J Innovation Sci 2019 Jun 03;11(2):177-195. [CrossRef]
  40. Božič K, Dimovski V. Business intelligence and analytics use, innovation ambidexterity, and firm performance: a dynamic capabilities perspective. J Strategic Inf Syst 2019 Dec;28(4):101578. [CrossRef]
  41. Ryu S, Ho SH, Han I. Knowledge sharing behavior of physicians in hospitals. Expert Syst Applications 2003 Jul;25(1):113-122. [CrossRef]
  42. Wu H, Deng Z. Knowledge collaboration among physicians in online health communities: a transactive memory perspective. Int J Inf Manage 2019 Dec;49:13-33. [CrossRef]
  43. Van de Wetering R. IT ambidexteritypatient agility: the mediating role of digital dynamic capability. In: Proceedings of the Twenty-Ninth European Conference on Information Systems. 2021 Presented at: ECIS 2021; June 14-16, 2021; Virtual.
  44. Devaraj S, Ow TT, Kohli R. Examining the impact of information technology and patient flow on healthcare performance: a Theory of Swift and Even Flow (TSEF) perspective. J Operations Manage 2013 Mar 13;31(4):181-192. [CrossRef]
  45. Prgomet M, Georgiou A, Westbrook JI. The impact of mobile handheld technology on hospital physicians' work practices and patient care: a systematic review. J Am Med Inform Assoc 2009;16(6):792-801 [FREE Full text] [CrossRef] [Medline]
  46. Bradley R, Pratt R, Thrasher E, Byrd T, Thomas C. An examination of the relationships among IT capability intentions, IT infrastructure integration and quality of care: a study in US hospitals. 2012 Presented at: 45th Hawaii International Conference on System Sciences; January 4-7, 2012; Maui, HI. [CrossRef]
  47. Sim I. Mobile devices and health. N Engl J Med 2019 Sep 05;381(10):956-968. [CrossRef]
  48. Karaca Y, Moonis M, Zhang Y, Gezgez C. Mobile cloud computing based stroke healthcare system. Int J Inf Manage 2019 Apr;45:250-261. [CrossRef]
  49. Mosa ASM, Yoo I, Sheets L. A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak 2012 Jul 10;12:67 [FREE Full text] [CrossRef] [Medline]
  50. van de Wetering R. IT-enabled clinical decision support: an empirical study on antecedents and mechanisms. J Healthc Eng 2018;2018:6945498. [CrossRef] [Medline]
  51. Salge T, Vera A. Hospital innovativeness and organizational performance: evidence from English public acute care. Health Care Manage Rev 2009;34(1):54-67. [CrossRef] [Medline]
  52. van de Wetering R. Enhancing clinical decision support through information processing capabilities and strategic IT alignment. In: Abramowicz W, Paschke A, editors. Business Information Systems Workshops: BIS 2018 International Workshops, Berlin, Germany, July 18–20, 2018, Revised Papers. Cham: Springer; 2018:19-29.
  53. Li W, Liu K, Yang H, Yu C. Integrated clinical pathway management for medical quality improvement – based on a semiotically inspired systems architecture. Eur J Inf Syst 2017 Dec 19;23(4):400-417. [CrossRef]
  54. Krasuska M, Williams R, Sheikh A, Franklin BD, Heeney C, Lane W, et al. Technological capabilities to assess digital excellence in hospitals in high performing health care systems: international eDelphi exercise. J Med Internet Res 2020 Aug 18;22(8):e17022 [FREE Full text] [CrossRef] [Medline]
  55. Wu I, Hu Y. Examining knowledge management enabled performance for hospital professionals: a dynamic capability view and the mediating role of process capability. J Assoc Inf Syst 2012 Dec;13(12):976-999. [CrossRef]
  56. Fadlalla A, Wickramasinghe N. An integrative framework for HIPAA-compliant I*IQ healthcare information systems. Int J Health Care Qual Assur Inc Leadersh Health Serv 2004;17(2-3):65-74. [CrossRef] [Medline]
  57. McCracken MJ, McIlwain TF, Fottler MD. Measuring organizational performance in the hospital industry: an exploratory comparison of objective and subjective methods. Health Serv Manage Res 2001 Nov;14(4):211-219. [CrossRef] [Medline]
  58. Devaraj S, Kohli R. Performance impacts of information technology: is actual usage the missing link? Manage Sci 2003 Mar;49(3):273-289. [CrossRef]
  59. Winter SG. Understanding dynamic capabilities. Strategic Manage J 2003 Oct;24(10):991-995. [CrossRef]
  60. Danneels E. The dynamics of product innovation and firm competences. Strategic Manage J 2002 Sep 19;23(12):1095-1121. [CrossRef]
  61. Li T, Chan YE. Dynamic information technology capability: concept definition and framework development. J Strategic Inf Syst 2019 Dec;28(4):101575. [CrossRef]
  62. Wang CL, Ahmed PK. Dynamic capabilities: a review and research agenda. Int J Manage Rev 2007 Mar;9(1):31-51. [CrossRef]
  63. Zheng S, Zhang W, Wu X, Du J. Knowledge-based dynamic capabilities and innovation in networked environments. J Knowledge Manage 2011:1035-1051. [CrossRef]
  64. Chiasson MW, Davidson E. Taking industry seriously in information systems research. MIS Q 2005;29(4):591. [CrossRef]
  65. Liedtka JM. Formulating hospital strategy: moving beyond a market mentality. Health Care Manage Rev 1992;17(1):21-26. [CrossRef] [Medline]
  66. March JG. Exploration and exploitation in organizational learning. Organ Sci 1991 Feb;2(1):71-87. [CrossRef]
  67. Raisch S, Birkinshaw J, Probst G, Tushman ML. Organizational ambidexterity: balancing exploitation and exploration for sustained performance. Organ Sci 2009 Aug;20(4):685-695. [CrossRef]
  68. Jansen JJP, Van Den Bosch FAJ, Volberda HW. Exploratory innovation, exploitative innovation, and performance: effects of organizational antecedents and Eenvironmental moderators. Manage Sci 2006 Nov;52(11):1661-1674. [CrossRef]
  69. Gibson CB, Birkinshaw J. The antecedents, consequences, and mediating role of organizational ambidexterity. Acad Manage J 2004 Apr;47(2):209-226. [CrossRef]
  70. Tushman ML, O'Reilly CA. Ambidextrous organizations: managing evolutionary and revolutionary change. California Manage Rev 1996 Jul 01;38(4):8-29. [CrossRef]
  71. van de Wetering R. IT ambidexterity driven patient agility and hospital patient service performance: a variance approach. medRxiv Preprint posted online on July 22, 2021. [CrossRef]
  72. Tarenskeen D, van de Wetering R, Bakker R, Brinkkemper S. The contribution of conceptual independence to IT infrastructure flexibility: the case of openEHR. Health Policy Technol 2020 Jun;9(2):235-246. [CrossRef]
  73. Tripsas M. Surviving radical technological change through dynamic capability: evidence from the typesetter industry. Ind Corporate Change 1997 Mar 01;6(2):341-377. [CrossRef]
  74. Teece DJ, Pisano G, Shuen A. Dynamic capabilities and strategic management. Strategic Manage J 1997 Aug;18(7):509-533. [CrossRef]
  75. Teece DJ. Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic Manage J 2007 Dec;28(13):1319-1350. [CrossRef]
  76. Eisenhardt KM, Martin JA. Dynamic capabilities: what are they? Strategic Manage J 2000 Oct;21(10-11):1105-1121. [CrossRef]
  77. Van de Wetering R. Enterprise architecture resources, dynamic capabilities, and their pathways to operational value. 2019 Presented at: The Fortieth International Conference on Information Systems; December 15-18, 2019; Munich, Germany p. 1-18.
  78. Mikalef P, Pateli A, van de Wetering R. IT architecture flexibility and IT governance decentralisation as drivers of IT-enabled dynamic capabilities and competitive performance: The moderating effect of the external environment. Eur J Inf Syst 2020 Aug 27;30(5):512-540. [CrossRef]
  79. Schilke O. On the contingent value of dynamic capabilities for competitive advantage: the nonlinear moderating effect of environmental dynamism. Strategic Manage J 2013 May 06;35(2):179-203. [CrossRef]
  80. Pavlou P, El Sawy O. Understanding the elusive black box of dynamic capabilities. Decision Sci 2011;42(1):239-273. [CrossRef]
  81. Di Stefano G, Peteraf M, Verona G. The organizational drivetrain: a road to integration of dynamic capabilities research. Acad Manage Perspect 2014 Nov;28(4):307-327. [CrossRef]
  82. Drnevich PL, Kriauciunas AP. Clarifying the conditions and limits of the contributions of ordinary and dynamic capabilities to relative firm performance. Strategic Manage J 2011 Jan 05;32(3):254-279. [CrossRef]
  83. Wilden R, Gudergan SP. The impact of dynamic capabilities on operational marketing and technological capabilities: investigating the role of environmental turbulence. J Acad Marketing Sci 2014 Mar 7;43(2):181-199. [CrossRef]
  84. Teece D, Peteraf M, Leih S. Dynamic capabilities and organizational agility: risk, uncertainty, and strategy in the innovation economy. California Manage Rev 2016 Aug 01;58(4):13-35. [CrossRef]
  85. Pavlou PA, El Sawy OA. The “Third Hand”: IT-enabled competitive advantage in turbulence through improvisational capabilities. Inf Syst Res 2010 Sep;21(3):443-471. [CrossRef]
  86. van de Wetering R, Hendrickx T, Brinkkemper S, Kurnia S. The impact of EA-driven dynamic capabilities, innovativeness, and structure on organizational benefits: a variance and fsQCA perspective. Sustainability 2021 May 12;13(10):5414. [CrossRef]
  87. Cepeda G, Vera D. Dynamic capabilities and operational capabilities: a knowledge management perspective. J Business Res 2007 May;60(5):426-437. [CrossRef]
  88. Grant RM. Prospering in dynamically-competitive environments: organizational capability as knowledge integration. Organ Sci 1996 Aug;7(4):375-387. [CrossRef]
  89. Nickerson JA, Zenger TR. A knowledge-based theory of the firm—the problem-solving perspective. Organ Sci 2004 Dec;15(6):617-632. [CrossRef]
  90. Tanriverdi H. Information technology relatedness, knowledge management capability, and performance of multibusiness firms. MIS Q 2005;29(2):311. [CrossRef]
  91. Kohli AK, Jaworski BJ. Market orientation: the construct, research propositions, and managerial implications. J Marketing 2018 Nov 28;54(2):1-18. [CrossRef]
  92. Jayachandran S, Hewett K, Kaufman P. Customer response capability in a sense-and-respond era: the role of customer knowledge process. J Acad Marketing Sci 2004 Jul 01;32(3):219-233. [CrossRef]
  93. Turner A, Fraser V, Muir Gray JA, Toth B. A first class knowledge service: developing the National electronic Library for Health. Health Info Libr J 2002 Sep;19(3):133-145 [FREE Full text] [CrossRef] [Medline]
  94. Stefanelli M. Knowledge and process management in health care organizations. Methods Inf Med 2004;43(5):525-535. [Medline]
  95. Zahra SA, George G. Absorptive capacity: a review, reconceptualization, and extension. Acad Manage Rev 2002 Apr;27(2):185-203. [CrossRef]
  96. Roberts N, Galluch PS, Dinger M, Grover V. Absorptive capacity and information systems research: review, synthesis, and directions for future research. MIS Q 2012;36(2):625. [CrossRef]
  97. Vickery S, Droge C, Setia P, Sambamurthy V. Supply chain information technologies and organisational initiatives: complementary versus independent effects on agility and firm performance. Int J Production Res 2010 Jan 20;48(23):7025-7042. [CrossRef]
  98. Sambamurthy V, Bharadwaj A, Grover V. Shaping agility through digital options: reconceptualizing the role of information technology in contemporary firms. MIS Q 2003;27(2):237. [CrossRef]
  99. Park Y, Sawy O, Fiss P. The role of business intelligence and communication technologies in organizational agility: a configurational approach. J Assoc Inf Syst 2017 Oct;18(9):648-686. [CrossRef]
  100. Galbraith JR. Organization design: an information processing view. Interfaces 1974 May;4(3):28-36. [CrossRef]
  101. Lu K, Ramamurthy K. Understanding the link between information technology capability and organizational agility: an empirical examination. MIS Q 2011;35(4):931. [CrossRef]
  102. Roberts N, Grover V. Investigating firm's customer agility and firm performance: the importance of aligning sense and respond capabilities. J Business Res 2012 May;65(5):579-585. [CrossRef]
  103. van de Wetering R. Achieving digital-driven patient agility in the era of big data. In: Dennehy D, Griva A, Pouloudi N, Dwivedi YK, Pappas I, Mäntymäki M, editors. Responsible AI and Analytics for an Ethical and Inclusive Digitized Society 20th IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2021, Galway, Ireland, September 1–3, 2021, Proceedings. Cham: Springer; 2021:82-93.
  104. Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006 May 16;144(10):742-752 [FREE Full text] [CrossRef] [Medline]
  105. Bhattacherjee A, Hikmet N. Physicians' resistance toward healthcare information technology: a theoretical model and empirical test. Eur J Inf Syst 2017 Dec 19;16(6):725-737. [CrossRef]
  106. Gregory RW, Keil M, Muntermann J, Mähring M. Paradoxes and the nature of ambidexterity in IT transformation programs. Inf Syst Res 2015 Mar;26(1):57-80. [CrossRef]
  107. Jaana M, Ward MM, Bahensky JA. EMRs and clinical IS implementation in hospitals: a statewide survey. J Rural Health 2012 Jan;28(1):34-43. [CrossRef] [Medline]
  108. Chen Y, Liu H, Chen M. Achieving novelty and efficiency in business model design: striking a balance between IT exploration and exploitation. Inf Manage 2020 Jan:103268. [CrossRef]
  109. Voudouris I, Lioukas S, Iatrelli M, Caloghirou Y. Effectiveness of technology investment: impact of internal technological capability, networking and investment's strategic importance. Technovation 2012 Jun;32(6):400-414. [CrossRef]
  110. Mao H, Liu S, Zhang J. How the effects of IT and knowledge capability on organizational agility are contingent on environmental uncertainty and information intensity. Inf Dev 2014 Feb 04;31(4):358-382. [CrossRef]
  111. Benitez J, Castillo A, Llorens J, Braojos J. IT-enabled knowledge ambidexterity and innovation performance in small U.S. firms: the moderator role of social media capability. Inf Manage 2018 Jan;55(1):131-143. [CrossRef]
  112. Nazir S, Pinsonneault A. IT and firm agility: an electronic integration perspective. J Assoc Inf Syst 2012 Mar;13(3):150-171. [CrossRef]
  113. Acur N, Kandemir D, De Weerd‐Nederhof PC, Song M. Exploring the impact of technological competence development on speed and NPD program performance. J Product Innovation Manage 2010;27(6):915-929. [CrossRef]
  114. Zhou KZ, Wu F. Technological capability, strategic flexibility, and product innovation. Strategic Manage J 2009:n/a-n/a. [CrossRef]
  115. Wang Y, Lo H, Yang Y. The constituents of core competencies and firm performance: evidence from high-technology firms in China. J Eng Technol Manage 2004 Dec;21(4):249-280. [CrossRef]
  116. Coombs JE, Bierly PE. Measuring technological capability and performance. R&D Manage 2006 Sep;36(4):421-438. [CrossRef]
  117. Westerman G, Tannou M, Bonnet D, Ferraris P, McAfee A. The digital advantage: how digital leaders outperform their peers in every industry. MITSloan Manage Capgemini Consult 2012 Nov;2:2-3.
  118. Ritter T, Pedersen CL. Digitization capability and the digitalization of business models in business-to-business firms: past, present, and future. Ind Marketing Manage 2020 Apr;86:180-190. [CrossRef]
  119. Wade M, Hulland J. Review: the resource-based view and information systems research: review, extension, and suggestions for future research. MIS Q 2004;28(1):107. [CrossRef]
  120. Kim G, Shin B, Kim K, Lee H. IT capabilities, process-oriented dynamic capabilities, and firm financial performance. J Assoc Inf Syst 2011 Jul;12(7):487-517. [CrossRef]
  121. Chen Y, Wang Y, Nevo S, Jin J, Wang L, Chow WS. IT capability and organizational performance: the roles of business process agility and environmental factors. Eur J Inf Syst 2017 Dec 19;23(3):326-342. [CrossRef]
  122. Van de Wetering R, Versendaal J, Walraven P. Examining the relationship between a hospital's IT infrastructure capability and digital capabilities: a resource-based perspective. 2018 Presented at: Twenty-fourth Americas Conference on Information Systems; August 16-18, 2018; New Orleans, LA p. 1-10.
  123. Tzokas N, Kim YA, Akbar H, Al-Dajani H. Absorptive capacity and performance: the role of customer relationship and technological capabilities in high-tech SMEs. Ind Marketing Manage 2015 May;47:134-142. [CrossRef]
  124. Liu H, Ke W, Wei KK, Hua Z. The impact of IT capabilities on firm performance: the mediating roles of absorptive capacity and supply chain agility. Decision Support Syst 2013 Feb;54(3):1452-1462. [CrossRef]
  125. Wang Y, Kung L, Byrd TA. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting Soc Change 2018 Jan;126:3-13. [CrossRef]
  126. Wamba SF, Gunasekaran A, Akter S, Ren SJ, Dubey R, Childe SJ. Big data analytics and firm performance: effects of dynamic capabilities. J Business Res 2017 Jan;70:356-365. [CrossRef]
  127. Delaney JT, Huselid MA. The impact of human resource management practices on perceptions of organizational performance. Acad Manage J 1996 Aug 01;39(4):949-969. [CrossRef]
  128. Hobbs KW, Monette PJ, Owoyemi P, Beard C, Rauch SL, Ressler KJ, et al. Incorporating information from electronic and social media into psychiatric and psychotherapeutic patient care: survey among clinicians. J Med Internet Res 2019 Jul 12;21(7):e13218 [FREE Full text] [CrossRef] [Medline]
  129. Pronovost PJ, Weast B, Holzmueller CG, Rosenstein BJ, Kidwell RP, Haller KB, et al. Evaluation of the culture of safety: survey of clinicians and managers in an academic medical center. Qual Saf Health Care 2003 Dec;12(6):405-410 [FREE Full text] [CrossRef] [Medline]
  130. DesRoches CM, Campbell EG, Rao SR, Donelan K, Ferris TG, Jha A, et al. Electronic health records in ambulatory care--a national survey of physicians. N Engl J Med 2008 Jul 03;359(1):50-60. [CrossRef] [Medline]
  131. Zahra SA, Covin JG. Business strategy, technology policy and firm performance. Strategic Manage J 1993 Sep;14(6):451-478. [CrossRef]
  132. Saunders M, Lewis P, Thornhill A. Research Methods for Business Students. Harlow: Pearson Education Limited; 2009.
  133. Ringle C, Wende S, Becker JM. SmartPLS GmbH. 2015.   URL: https://www.smartpls.com/ [accessed 2021-09-01]
  134. Rigdon EE, Sarstedt M, Ringle CM. On comparing results from CB-SEM and PLS-SEM: five perspectives and five recommendations. Marketing Zfp 2017;39(3):4-16. [CrossRef]
  135. Hair JF, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage Publications; 2016.
  136. Hair JF, Sarstedt M, Ringle CM, Gudergan SP. Advanced Issues in Partial Least Squares Structural Equation Modeling. Thousand Oaks, CA: SAGE Publications; 2017.
  137. Lowry PB, Gaskin J. Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: when to choose it and how to use it. IEEE Trans Professional Commun 2014 Jun;57(2):123-146. [CrossRef]
  138. Chin W. Issues and opinion on structural equation modeling. Manage Inf Syst Q 1998;22(1):7-16.
  139. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: indeed a silver bullet. J Marketing Theory Pract 2014 Dec 08;19(2):139-152. [CrossRef]
  140. Faul F, Erdfelder E, Buchner A, Lang A. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods 2009 Nov;41(4):1149-1160. [CrossRef] [Medline]
  141. Berg N. Non-response bias. In: Encyclopedia of Social Measurement. London: Academic Press; 2005:1-35.
  142. Hikmet N, Chen SK. An investigation into low mail survey response rates of information technology users in health care organizations. Int J Med Inform 2003 Dec;72(1-3):29-34. [CrossRef] [Medline]
  143. Richardson HA, Simmering MJ, Sturman MC. A tale of three perspectives. Organ Res Methods 2009 Mar 11;12(4):762-800. [CrossRef]
  144. Podsakoff PM, MacKenzie SB, Lee J, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 2003 Oct;88(5):879-903. [CrossRef] [Medline]
  145. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Marketing Res 2018 Nov 28;18(1):39-50. [CrossRef]
  146. Farrell AM. Insufficient discriminant validity: a comment on Bove, Pervan, Beatty, and Shiu (2009). J Business Res 2010 Mar;63(3):324-327. [CrossRef]
  147. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Marketing Sci 2014 Aug 22;43(1):115-135. [CrossRef]
  148. Kock N, Lynn G. Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations. J Assoc Inf Syst 2012 Jul;13(7):546-580. [CrossRef]
  149. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling Multidisciplinary J 1999 Jan;6(1):1-55. [CrossRef]
  150. Chin W. The partial least squares approach to structural equation modeling. In: Marcoulides GA, editor. Modern Methods for Business Research. East Sussex, UK: Psychology Press; 1998:295-336.
  151. Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York City, NY: Guilford Press; 2013.
  152. Agarwal R, Gao G, DesRoches C, Jha AK. The digital transformation of healthcare: current status and the road ahead. Inf Syst Res 2010 Dec;21(4):796-809. [CrossRef]
  153. Herrmann M, Boehme P, Mondritzki T, Ehlers JP, Kavadias S, Truebel H. Digital transformation and disruption of the health care sector: internet-based observational study. J Med Internet Res 2018 Mar 27;20(3):e104 [FREE Full text] [CrossRef] [Medline]
  154. McGrail KM, Ahuja MA, Leaver CA. Virtual visits and patient-centered care: results of a patient survey and observational study. J Med Internet Res 2017 May 26;19(5):e177 [FREE Full text] [CrossRef] [Medline]
  155. Pang M, Lee G, DeLone WH. IT resources, organizational capabilities, and value creation in public-sector organizations: a public-value management perspective. J Inf Technol 2014 Sep 01;29(3):187-205. [CrossRef]
  156. Gerybadze A. Technological competence assessment within the firm: applications of competence theory to managerial practice. Forschungsstelle Internat Manage Innovation 1998:1-32 [FREE Full text]
  157. Singh R, Mathiassen L, Stachura M, Astapova E. Dynamic capabilities in home health: IT-enabled transformation of post-acute care. J Assoc Inf Syst 2011 Feb;12(2):163-188. [CrossRef]
  158. Ashrafi N, Xu P, Kuilboer J, Koehler W. Boosting enterprise agility via IT knowledge management capabilities. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences. 2006 Presented at: HICSS'06; January 4-7, 2006; Kauai, HI. [CrossRef]
  159. Schryen G. Revisiting IS business value research: what we already know, what we still need to know, and how we can get there. Eur J Inf Syst 2017 Dec 19;22(2):139-169. [CrossRef]
  160. Sabherwal R, Jeyaraj A. Information technology impacts on firm performance: an extension of Kohli and Devaraj (2003). MIS Q 2015 Apr 4;39(4):809-836. [CrossRef]
  161. Steele Gray C. Seeking meaningful innovation: lessons learned developing, evaluating, and implementing the electronic patient-reported outcome tool. J Med Internet Res 2020 Jul 29;22(7):e17987 [FREE Full text] [CrossRef] [Medline]
  162. Van Velthoven MH, Cordon C. Sustainable adoption of digital health innovations: perspectives from a stakeholder workshop. J Med Internet Res 2019 Mar 25;21(3):e11922 [FREE Full text] [CrossRef] [Medline]
  163. Papoutsi C, Wherton S, Shaw S, Morrison C, Greenhalgh T. Putting the social back into sociotechnical: case studies of co-design in digital health. J Am Med Inform Assoc 2021 Feb 15;28(2):284-293 [FREE Full text] [CrossRef] [Medline]
  164. Keesara S, Jonas A, Schulman K. Covid-19 and health care's digital revolution. N Engl J Med 2020 Jun 04;382(23):e82. [CrossRef] [Medline]
  165. Shanks G, Gloet M, Asadi Someh I, Frampton K, Tamm T. Achieving benefits with enterprise architecture. J Strategic Inf Syst 2018 Jun;27(2):139-156. [CrossRef]
  166. Wu SP, Straub DW, Liang T. How information technology governance mechanisms and strategic alignment influence organizational performance: insights from a matched survey of business and IT managers. MIS Q 2015 Feb 2;39(2):497-518. [CrossRef]
  167. Egener BE, Mason DJ, McDonald WJ, Okun S, Gaines ME, Fleming DA, et al. The charter on professionalism for health care organizations. Acad Med 2017 Aug;92(8):1091-1099 [FREE Full text] [CrossRef] [Medline]
  168. Donelan K, DesRoches CM, Dittus RS, Buerhaus P. Perspectives of physicians and nurse practitioners on primary care practice. N Engl J Med 2013 May 16;368(20):1898-1906. [CrossRef]


AI: artificial intelligence
AVE: average variance extracted
DCV: dynamic capabilities view
HTMT: heterotrait-monotrait ratio of correlations
IS: information system
IT: information technology
PLS: partial least squares
SEM: structural equation modeling
SRMR: standardized root mean square residual
VIF: variance inflation factor


Edited by E Meinert; submitted 23.07.21; peer-reviewed by I Adeleke, J Walsh, L Taraboanta; comments to author 23.09.21; revised version received 29.09.21; accepted 03.10.21; published 06.12.21

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