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Abstract

In the technological era, changes are happening around the globe at a fast rate. In this regard, healthcare organizations are implementing changes to improve their process. Hence, to manage implemented changes, there is a need to assess AI capabilities, cybernetic thinking (CT), organizational ambidexterity (OA), and employee wellbeing (EWB). However, no validated scale exists specifically to measure the aspects mentioned earlier in the context of healthcare organizations (HCO). Accordingly, our study attempted to validate existing scales of AI capabilities, CT, OA, and EWB in the context of HCO. Besides, to attain this purpose, a pilot study was led on a sample of 150 doctors employed in private sector hospitals in Pakistan, and data were analyzed using CB-SEM. This study confirms the validity and reliability of the refined scale in the context of a Pakistani healthcare setting. From the practical context, healthcare organizations can use the validated scale to assess their capacity towards adopting emerging technologies. These scales can be used to formulate strategies for managing technological change from both organizational and employee perspectives in healthcare settings. In addition, this study offers a multidimensional perspective by integrating diffusion of innovation theory (DOIT) with AI capabilities, EWB, CT, and OA to specify how innovation diffuses across complex systems, such as healthcare settings.

Plain language summary

This study attempted to validate the scales of AI capabilities, cybernetic thinking, ambidexterity, and employee well-being to manage technological change. Therefore, a pilot study was run on a sample of 150 doctors working in private-sector hospitals, and collected data was analyzed using CB-SEM. Based on the analysis the validated scales were reported in the context of a developing country like Pakistan to evaluate their capacity towards opting the emerging technologies.

Keywords

cybernetic thinking organizational ambidexterity AI capabilities employee wellbeing scales validation diffusion of innovation theory

Introduction

Nowadays, dynamic changes have been observed in all sectors, including the healthcare setup around the globe, regarding the inclusion of emerging technologies. Among them, artificial intelligence (AI) has taken a prominent place in all areas of businesses and organizations including the service sector. At present, this major shift on the technology side falls under the category of the industrial (fourth) revolution (McKinsey & Company, 2022; Schwab, 2016), machines or algorithms’ second age (Brynjolfsson & McAfee, 2014; Danaher et al., 2017). This major shift is possible through the use of machine learning, natural language processing, image recognition, machine vision, and decision-making systems, leading to the transformation processes (Baabdullah, 2024; Walsh et al., 2019).

Behind this transformation, the most prominent driver is the implementation of AI technology (Bankins & Formosa, 2023; Fassa et al., 2019). On the other hand, this technological shift is transforming individuals’ working patterns and productivity (Bankins et al., 2024). Accordingly, AI refers to the assortment of various technologies that help resolve problems related to processes, working patterns, and customer experience (Wulff & Finnestrand, 2024). Hence, human and machine interaction is changing work dynamics, which in turn improves organizational processes and operational performance (Chen, 2024).

This interaction can be possible when organizations proactively think, act, and communicate using a cybernetic system to meet stakeholders’ needs about technological adoption (Osmanoglu, 2025). The notion of cybernetics was introduced in 1948 by Wiener’s (1948), as a system to control and streamline communication between humans and machines. Thus, communication is a foundational aspect to facilitate this technological change. Accordingly, cybernetics focuses on information processing to streamline structures (Groumpos, 2024). Thus, communication channels can play a critical role in processing and dissemination of information to facilitate behavioral change for technological adoption (Walter, 2024).

In addition, various aspects are required to build a Cybernetics system for the development of effective communication and feedback mechanisms using electronic circuits, brains, and organizational stakeholders to create synergy among all (Baltazar, 2021). In line with this, to facilitate change, cybernetic thinking (CT) includes a link between communication, adaptation, feedback loops, and control systems (Wong, 2023). For instance, during AI implementation, it helps to form a value and knowledge-based relationship between the organization, human, external environment, and society (Lavanderos, 2022). Hence, CT is seen as a new tool for the organization to manage the adaptation with intact control systems (Wong, 2023) and communication to manage human work relationships during AI implementation (Canbul Yaroğlu, 2024).

Accordingly, CT is a critical aspect to manage AI implementation along with protecting employee well-being (EWB), an area of concern for today’s organizations. Different views were presented about AI implementation and EWB within the organization, such as unemployment, pressures, or fear (Frey & Osborne, 2017). All these factors lead to affecting EWB in a negative manner. The prime reason behind this influence is perhaps reliant on AI mischaracterization displayed in science fiction related to emerging technologies and the societal narrative (Cave et al., 2018). Contrary to this, intact management support and communication during AI adoption can transform the process; as a result, EWB can be enhanced (Soomro et al., 2024). Likewise, other studies also report positive outcomes, such as improved work productivity (Al Naqbi et al., 2024), employee well-being (Feijóo et al., 2020; Oosthuizen, 2019), and organizational performance (Olan et al., 2022).

Consequently, wellbeing is linked to how happy and satisfied an individual feels about their job (Anitha & Shanthi, 2020). For instance, in what way is EWB protected during AI adoption? However, when effective measures are taken, such as reskilling and upskilling to deal with technological innovations within the organization (Bodea et al., 2024) creates a synergy among teams to perform well, leading to improved EWB (Babu et al., 2024). Undoubtedly, the future of all organizations will be tech-driven, and EWB remains the most important aspect to be protected in the advent of AI technologies on the other side.

EWB can be protected by adopting effective exploration and exploitation strategies when implementing AI (Stovall, 2023). During AI implementation and adoption of innovative technologies, the notion of organizational ambidexterity (OA) can play a role (Chakma et al., 2024). In OA, the emphasis on the exploitation aspect warrants the current organizational viability and exploration to ensure the forthcoming organizational viability (Levinthal & March, 1993; Yunita et al., 2023). Accordingly, OA is a potential force that ensures the service and cost excellence and organizational efficacy. This efficacy is attained by implementing AI and robot technologies that can support various tasks (Wirtz, 2020). Without OA, an organization cannot stand out in the market and fall behind in taking advantage of AI adoption (Sliż & Jackowska, 2025).

Even though researchers have developed scales on AI, CT, OA, and EWB to accurately capture phenomena in other contexts. However, no validated scale has been established till today to measure the abovementioned aspects from the diffusion of innovation theory (DOIT) perspective in the Pakistani healthcare setting. Investigators propose that there is a need to assess AI implementation in different sectors and contexts with different factors (Böhmer & Schinnenburg, 2023). To cover the existing gap, our study attempted to substantiate the existing scale in Pakistani private sector hospitals by conducting a pilot study. It is seen as a valuable addition to the literature that will help researchers and managers to use it in the future for further deliberations.

Theoretical Underpinning and Overview of Scales

Diffusion of Innovation Theory

Diffusion of innovation theory (DOIT) has been a widely used theoretical perspective in organizational settings (Rogers, 1981). It refers to sharing new concepts, ideas, philosophies, and practices from an innovation standpoint for the adoption and integration of technologies across organizations (Patnaik & Bakkar, 2024). The adoption process of innovation usually completes in five stages from emergence to implementation including (1) knowledge stage (exposure to innovation); (2) persuasion stage (favorable and unfavorable attitude); (3) decision stage (accept or reject adopted technology); (4) implementation stage (usage of innovation); and (5) confirmation stage (innovation is reinforced; Xia et al., 2022).

The abovementioned stages for technological adoption can be completed (Martins et al., 2016) via using different communication channels (Rogers et al., 2014), along with leadership, as well as considering internal and external organizational factors (De Mattos & Laurindo, 2017). Referring to internal organizational factors, technological adoption can be challenging for managers due to employees’ reluctance and unwillingness to accept changes introduced across different levels (Yousif et al., 2024). This challenge can be overcome by using communication channels to portray the advantages of technological innovation to lessen employees’ reluctance (Patnaik & Bakkar, 2024). Here, CT can play a critical role. It refers to developing system thinking and communication channels to gather feedback from stakeholders and adopt technological innovations (Osejo-Bucheli, 2025; Pfiffner, 2022). Hence, the use of AI across various organizational functions can lead to building confidence and resilience among employees to manage such change (Espejo, 2017; Espejo & Reyes, 2011). Therefore, developing and implementing AI capabilities can transform organizational functions; as a result, it offers a relative advantage in process automation (Brynjolfsson & McAfee, 2017). Adjacent to this, AI adoption can improve or lessen EWB. However, it depends on communication, and the way the problem is solved through AI. Consequently, it lessens the frustration level and EWB can be preserved and improved (Sadeghi, 2024). Moreover, innovation is a significant factor in determining individual determination for technological adoption and fostering better outcomes such as performance and wellbeing (Labay & Kinnear, 1981).

In line with the above argument, exploration is one of the key dimensions of OA that involves the early adoption of technological innovation, and exploitation leads to improving existing processes. These dimensions enhance employee confidence as well as build trust to grasp the technological change as an opportunity, as a result fostering wellbeing. DIOT is applied in several organizational contexts (Xu et al., 2024) related to the technological adoption in the accounting profession (Assidi et al., 2025), and cloud computing adoption in supply chain management (Amini & Jahanbakhsh Javid, 2023). However, the application of DIOT in the context of AI capability, EWB, OA, and CT remains inconclusive in the extant literature related to healthcare settings.

AI Capabilities

For organizations, AI is a foundational base for success in digital servitization and innovation. Innovation can be adopted by investing in infrastructure, technologies, and data. Thus, the artificial component is linked to the manufactured side, and intelligence refers to acquiring information from the available data and life experiences to learn and build the capability to perform (Chowdhury et al., 2023). However, this is not enough; Chowdhury et al. (2023) and Sjödin et al. (2021) elaborated that AI technology adoption requires new skills and capabilities that help in improving operational processes.

Hence, AI capabilities involve developing a system to utilize organizational resources to improve operational efficiency and make informed decisions. Moreover, it varies from industry to industry and sector to sector. According to Sjödin et al. (2021), context-specific AI capabilities relevant to the organization or culture need to be assessed. Moreover, researchers call for seeing the role of AI capabilities in different industries and sectors to broaden the scope of AI-related studies (Böhmer & Schinnenburg, 2023). To understand AI capability, it is necessary to validate the existing scale in private-sector hospitals of Pakistan, especially among the key players, that is, doctors. To attain this purpose, the AI scale was taken from the authors (see Mikalef et al., 2022; Mikalef & Gupta, 2021). Table 1 displays elaboration presented by investigators related to the existing AI scale in the extant literature.

Table 1.

AI Capabilities.

AI capabilities	Authors
Job design, transparency, performance, and data ambiguity	Böhmer and Schinnenburg (2023)
Look beyond technical resources by including non-technical resources (e.g., human skills, competencies, organizational culture, and coordination among teams, leadership, innovation mindset, AI employee integration, and governance strategies)	Chowdhury et al. (2023)
AI for process optimization, customer value proposition, resource optimization, and societal good	Abou-Foul et al. (2022)
Tangible, intangible, and human skills	Mikalef et al. (2022)
Tangible, human, and intangible AI-driven capabilities	Mikalef and Gupta (2021)

Employee Wellbeing

EWB refers to individual psychological, physical, and emotional health to ensure comfort and happiness. In today’s technological era, organizations have adopted different approaches to improve EWB, such as flexible methods, a clock schedule for work, and thinking out of the box, to facilitate the smooth functioning (Buick et al., 2024; Myrvang, 2020; Panda & Rath, 2018). These approaches grasped the employees’ attention due to the flexibility offered by the organization, performing assigned roles, especially in stressful working environments such as hospitals (Mahendra & Kurniawati, 2024).

Likewise, employees working in the education and hospital sectors suffer from burnout, stress, and emotional exhaustion (Angioha et al., 2020; Marquez, 2024; Sohani et al., 2024). In unification with this misery, sustaining wellbeing is a perceived challenge during AI adoption for service organizations (Deswal & Arora, 2025). However, understanding the EWB is still inconclusive and needs further deliberation to manage technological change (Pradhan & Hati, 2022) in healthcare settings. Achieving resilience among employees requires strong organizational support and practices to accept changes, enhancing EWB (Johnson et al., 2020). Table 2 indicates elaboration related to the EWB scale and related dimensions in the existing literature.

Table 2.

Employee Wellbeing.

EWB	Authors
Psychological wellbeing	Pradhan et al. (2017)
Subjective wellbeing	Bryson et al. (2015)
Positive and negative affect and job satisfaction	Slemp et al. (2015)
Life, workplace, and psychological well-being	Zheng et al. (2015)
Organizational factors and mental well-being	Orsila et al. (2011)

EWB is a key issue for doctors in particular, when a service organization adopts technological innovation. It is important to have a contextual scale to evaluate EWB in today’s digital era (Feijóo et al., 2020; Oosthuizen, 2019). Therefore, this study attempts to fill this gap by validating the scale taken from Juchnowicz and Kinowska (2021) to assess EWB among doctors working in Pakistani private sector hospitals from the perspectives of relationships, work-life balance, and physical and psychological health.

Cybernetic Thinking

Cybernetics involves steering, navigating, or governing the process by internalizing the feedback loop. This can help to decide actions based on the input and output received (Maden et al., 2022), leading to understanding and thinking about problems in a cybernetic manner to manage algorithms and data during the advent of AI (Krippendorff, 2021). CT eliminates the risks and socio-technical issues during AI execution in organizational systems by streamlining the input and output (Maden et al., 2022).

In a complex system, cybernetics is defined as a science that assesses the organizational abstract principles from the information imported and employed in the system with intact controlling actions to achieve goals despite various turbulences (Álvarez-Fontalvo et al., 2024). Cybernetics is a tool for assessing and controlling individual behavior to attain the designated goals (Peña-Ayala & Cárdenas-Robledo, 2019). Moreover, cybernetics involves purposes, values, sensorial mechanisms, and feedback to manage systems labor for goal achievement. Thus, it refers to goal-directed and self-regulating systems (DeYoung, 2015; Wiener, 1948).

Referring to the above-mentioned arguments, regulation and control are the central aspects of cybernetics. Accordingly, Girolami et al. (2015) explained cybernetics as a meta-theory for complex model systems. However, DeYoung (2015) outlined it as an integrated theory. In addition, cybernetics has a transdisciplinary paradigm, designing a variety of systems including physical, ecological, biological, technological, and social models, or an integration of all to create synergy in working (Peña-Ayala & Cárdenas-Robledo, 2019). Contrary to this, cybernetics is an interdisciplinary platform that uses communication and decision-making tools to acquire, manage, and share new ways of doing things (Choudhury, 2024; Montagnini, 2017; Montagnini et al., 2016).

Moreover, cybernetic system thinking is a human-focused approach based on the integration of system theory, cybernetics, management science, and operations to deal with innovation to succeed from the designer’s perspective by executing technologies to meet people’s needs as well as business requirements (Forlano, 2025; Greene, 2019; Krippendorff, 2007). Understanding the CT principles, the first aspect is feedback controls behavior, and it is demarcated as a negative feedback loop to lessen deviations from the expected outcomes (Fischer & Riedl, 2015), and works on the self-regulating system to better comprehend and manage human behavior. Therefore, Key aspects of cybernetics include feedback loops, control systems, adaptation, and communication (Wong, 2023), to acquire new knowledge (Armenia et al., 2023), leading to influence wellbeing and firm performance (Raiden et al., 2020; Salas-Vallina et al., 2022). Figure 1 indicates the organizational CT, such as communication, conduction, coordination, cohesion, co-autonomy, and centralization (Lavanderos, 2022). However, limited CT scales exist. This study attempts to validate the CT scale of Greene (2019) in the context of Pakistani private sector hospitals among doctors.

Figure 1.

Structure of cybernetics.

Organizational Ambidexterity

OA is a process of adapting a technology effectively by considering knowledge as a key resource for implementing incremental and radical change (Chakma et al., 2021). Accordingly, two factors are involved in OA such as exploitation and exploration. The exploitation side focuses on amplifying the revenues. Contrary to this, this side cannot accept environmental and technological changes. However, the exploration side of OA can accept these changes to bring innovation (Gschwantner, 2018). Therefore, exploration and exploitation can support the adoption and acceptance of technological innovation in an organization by streamlining both sides, including revenue generation and embracing technology, leading to improved market presence (Koryak et al., 2018).

Accordingly, ambidexterity refers to the ability to meet external environmental demands by accepting, adopting, and executing technological changes (Hwang et al., 2023; O’Reilly & Tushman, 2011; Restuputri et al., 2024). This means OA can support the change process and can have an impact on AI adoption and EWB. In addition, evidence found that OA creates a trust culture by ensuring consistency among key stakeholders (Sorsanen, 2009), through the timely sharing of information with team members (Çelik & Uzunçarşılı, 2023). As a result, wellbeing is affected (Salas-Vallina et al., 2022) and performance (Dranev et al., 2020).

The OA notion has gained popularity among investigators and practitioners. It can influence organizational management, learning, and technological innovation (Kassotaki, 2022). Apart from its significance for today’s organizations, limited studies exist about the applicability of OA in the healthcare setting. This study attempts to validate the OA scale taken by the authors Lubatkin et al. (2006) and Ojiako et al. (2023). Table 3 shows the elaborations related to the OA scale in existing literature.

Table 3.

Organizational Ambidexterity.

OA	Authors
Assessed through exploratory and exploitative activities	Mom et al. (2019)
Exploitation and exploration learning	Hwang et al. (2023)
Exploration and exploitation perspectives	Koryak et al. (2018)
Leadership, contextual, structural, and service delivery mode ambidexterity	Wirtz (2020)

Method

Participants and Scale Validation Overview

A pilot study was conducted on a small sample to validate the scales before the data collection on a larger sample size. To achieve this purpose, data were collected from doctors working in private sector hospitals in Pakistan using convenience sampling with a sample size of 150 to validate the scales. Hence, several measures were incorporated to limit the potential risks of harm to the respondents: first, minimization of risk through ensuring voluntary participation. Furthermore, no physical and psychological risks were involved, as respondents were not exposed to any intervention. Moreover, this study offers potential benefits to society by providing a validated scale that can help improve the adoption of emerging technologies and the decision-making process in the healthcare setting. Henceforth, the following aspects were incorporated into the consent process, providing a detailed explanation of the study’s purpose to the respondents, ensuring the voluntary participation of all respondents, and data collected would be kept confidential.

To complete the data collection process, 200 questionnaires were disseminated. Only 150 were received in a duly filled manner for further analysis. Among the respondents, there were n = 86 male doctors with a percentage of 57.3, and n = 64 were female doctors with 42.7% in the sample. In addition, 8.7% (n = 13) of doctors with 1 to 2 years of experience represent the sample, 24.7% (n = 37) with 2 to 3 years of experience, 38.7% (n = 58) with 3 to 4 years of experience, and the remaining 28% (n = 42) with >4 years of experience. In addition, as far as education level is considered, 63.3% (n = 95) represent the sample with bachelor’s, 27.3% (n = 41) with master’s, and 9.3% (n = 14) with PhD degrees. Moreover, referring to job title, 12.7% (n = 19) represented the sample as house officers, while the remaining 78% (n = 117) represented as medical officers, and 9.3% (n = 14) as postgraduate residents. Table 4 shows the scale validation overview. Moreover, respondents were presented with 70 items measuring AI capabilities, OA, CT, and EWB.

Table 4.

Scales Validation Overview.

Phase	Description	Technique and sample	Sample size	Country
Scale evaluation	Test of reliability and validity	Convenience sampling and doctors	150	Pakistan

Measures

To measure AI capability, EWB, CT, and OA scales were taken from different sources for validation in a Pakistani healthcare setting, particularly private hospitals. Details about each scale in mentioned in Table 5.

Table 5.

Scales Overview.

Construct	Items	Likert scale	Source(s)
AI capability	40	7	Mikalef et al. (2022), Mikalef and Gupta (2021)
EWB	9	5	Juchnowicz and Kinowska (2021)
CT	9	5	Greene (2019)
OA	12	5	Lubatkin et al (2006), Ojiako et al. (2023)

Data Analysis Procedure

Firstly, the descriptive analysis was performed to see the sample representation. Moreover, “KMO” as well as “Bartlett’s test of sphericity (BToS)” to assess sampling adequacy, and “Harman’s single (exploratory) factor test (HSFT)” to assess common method bias were performed by utilizing Jamovi software on each aspect of AI capabilities, CT, OA, and EWB. Additionally, CFA was run to see the underlying structural outline using PLS (CB-SEM). The reason for using CB SEM was to assess the measurement model in terms of fitness indices. PLS and CB-SEM are complementary, nonparametric, and variance-based approaches used in studies (Sarstedt et al., 2023). However, CB-SEM explains the covariance among the related indicators and constructs that were taken as common factors (Rigdon et al., 2017). In addition, CB-SEM has higher accuracy compared to PLS-SEM, and CB-SEM helps to better recover the parameters than PLS-SEM.

The reason for choosing CB-SEM over PLS-SEM was to test the theory and assess model fit rather than prediction, and when the data met sampling adequacy (Hair et al., 2018). Therefore, this study was focused on validating the scales based on DIOT. Henceforth, CB-SEM was an appropriate choice. Contrary to this, PLS-SEM is considered suitable for prediction rather than model fit assessment (Hair et al., 2013; Sarstedt et al., 2014). Thus, goodness of fit indices, item loadings, validity, and reliability were examined using CB-SEM. Details of each executed technique are shown in the subsequent section.

Assessment before Factor Analysis

An assessment of sampling and correlation matrix adequacy is a crucial test to be performed before factor analysis. For sampling adequacy, the KMO test was employed; as per the result, the value was .719, which is >.60, meeting the threshold criteria as proposed by Murugesan et al. (2023). Therefore, the data was considered satisfactory for factor analysis. Moreover, BToS was employed to assess the adequacy of a correlation matrix. As per the result, a significant link exists among the variables as the p-value is <.001 with a χ² = 26,283. This indicates variables are not orthogonal, and the sig value indicates the data set is appropriate for further analysis (Bilgiç & Aytaç, 2024).

Harman’s Single Factor Test

To evaluate common method bias (CMB), HSFT was run (Harman, 1976). As per the analysis shown in Table 6, there is no issue of multicollinearity in the data set, as the percentage of variance is 28.5% which is less than 50 as per the threshold recommended for CMB (Fuller et al., 2016). Therefore, data can be run for further analysis.

Table 6.

Harman’s Single Factor Test.

Components	Eigen values	% of variance	Cumulative
1	16.25	28.51	28.5
2	10.14	17.79	46.3
3	7.17	12.58	58.9
4	5.87	10.29	69.2
5	2.36	4.15	73.3
6	1.66	2.92	76.3
7	1.28	2.25	78.5
8	1.10	1.93	80.5
9	1.03	1.82	82.3
10	.94	1.66	83.9
11	.87	1.52	85.5
12	.82	1.44	86.9
13	.65	1.15	88.1
14	.63	1.11	89.2
15	.58	1.03	90.2
16	.54	.95	91.2
17	.50	.88	92.1
18	.47	.83	92.9
19	.40	.71	93.6
20	.34	.59	94.2
21	.32	.57	94.8
22	.29	.52	95.3
23	.25	.45	95.7
24	.24	.42	96.2
25	.21	.37	96.6
26	.19	.34	96.9
27	.18	.32	97.2
28	.17	.30	97.5
29	.16	.28	97.8
30	.13	.23	98.0
31	.13	.22	98.3
32	.12	.21	98.5
33	.10	.18	98.7
34	.09	.17	98.8
35	.08	.15	99.0
36	.07	.13	99.1
37	.07	.13	99.3
38	.06	.10	99.4
39	.05	.09	99.5
40	.04	.08	99.5
41	.04	.08	99.6
42	.04	.07	99.7
43	.03	.05	99.8
44	.02	.05	99.8
45	.02	.04	99.9
46	.01	.03	99.9
47	.01	.02	99.9
48	.01	.02	99.9
49	.01	.01	99.9
50	.00	.01	100.0
51	.00	.01	100.0
52	.00	.00	100.0
53	.00	.00	100.0
54	.00	.00	100.0

Results

Factor Loadings, Alpha Values, and Model Fit

Table 7 shows the factor loadings along with alpha values and items used (Bibi et al., 2025). All items were retained with factor loading values >.70 as per the recommendation of Cheung et al. (2023), while factor loading values <.70 were removed. Moreover, the model fit value of RMSEA falls under the threshold, that is, .05, showing that the model is reasonably fit as values fall between .05 and .08. On the other hand, CFI value is .94 again, which specifies an acceptable fit as values fall between .90 and .95. Likewise, TLI value is .91, which is again an acceptable fit as the value falls between .90 and .95 as per the recommendation of Hu and Bentler (1999). The measurement model (CFA) graphical illustration is shown in Figure 2.

Table 7.

Model Fit Indices, Outer Loading, and Cronbach’s Alpha.

Model	CMIN/DF	RMSEA	CFI	TLI	p
Model values	2.05	.065	.940	.910	.253
Recommended	(<3.0)	(<.08)	(>.90)	(>.90)	(>.05)
Factor and items (Bibi et al., 2025)				Outer loadings	Alpha
Cybernetic thinking
CT1 (My technical decisions influence the system in hospitals)				.823	.961
CT2 (I like to help my colleagues, even if it is not linked to my work directly)				.881
CT3 (I feel comfortable working in a hospital with a flexible/changing system)				.763
CT4 (I can make technical decisions in a hospital with partial information)				.713
CT5 (I always make a backup plan when things are not as per plan)				.856
CT6 (I like taking on leadership roles during stressful situations)				.948
CT7 (I can create a common vocabulary to facilitate communication with colleagues from other disciplines)				.905
CT8 (I feel happy while giving tasks to others)				.873
CT9 (I can resolve minor issues at work in person rather than over email)				.938
Employee wellbeing
EWB1 (A friendly environment exists in my hospital)				.759	.937
EWB2 (Relationship with my supervisor is good)				.805
EWB3 (My supervisor respects me rather than treats me like a subordinate)				.839
EWB4 (I have confidence in my hospital colleagues and supervisor)				.840
EWB5 (My health is appropriate for the work I perform)				.729
EWB6 (I always feel optimistic about the future with hope)				.760
EWB7 (My hospital environment gives me satisfaction)				.778
EWB8 (I do my best every day at a hospital)				.775
EWB9 (In my hospital good work and personal life balance exists)				.815
Organizational ambidexterity
OA1 (Hospital management always looks for novel technological designs)				.889	.951
OA2 (Hospital management built on its ability to see new technologies)				.830
OA3 (Hospital management focuses on creating innovative products and services to manage patients)				.849
OA4 (To satisfy patients’ needs, hospital management always finds new ways to manage creatively)				.722
OA5 (Hospital management looks for new ventures in the market)				.796
OA6 (Hospital management actively targets new patient groups for their health management)				.855
OA7 (Hospital management shows commitment to improving quality)				.722
OA8 (Hospital management displays a commitment to lower cost)				.810
OA9 (Hospital management focuses on improving its products and services continuously)				.711
OA10 (Hospital management focuses on increasing automation in hospital operations and processes)				.814
OA11 (Hospital management constantly surveys existing patients to assess their satisfaction)				.797
AI capabilities-intangible resources
INTAI1 (Hospital management allows for collaboration among different departments in a hospital)				.796	.968
INTAI2 (Departments effectively work on collective goals)				.833
INTAI3 (Leaders’ emphasis on teamwork)				.987
INTAI4 (All hospital departments work on the same vision)				.846
INTAI5 (Employees and hospital management have a greater level of mutual understanding)				.858
INTAI6 (All information is shared with each stakeholder within the hospital)				.899
INTAI9 (Hospital management considers the dimensions and aspects of the reengineering efforts)				.789
INTAI15 (In our hospital, management takes courageous steps to accomplish objectives)				.970
INTAI16 (Hospital management typically adopts a courageous stance by exploiting possible opportunities)				.811
AI capabilities-tangible resources
TRAIC1 (I can access large and unstructured data for investigation)				.992	.980
TRAIC2 (I can integrate data from multiple internal sources into data in a central database for easy access)				.991
TRAIC3 (I can integrate data from external to internal sources to facilitate high-value assessment in our hospital)				.896
TRAIC4 (I can share data across hospital departments)				.983
TRAIC5 (I can prepare and evaluate AI data efficiently for errors)				.875
TRAIC6 (I can obtain data to produce meaningful insights)				.730
TRAIC7 (Hospital management has adopted cloud-based services for processing AI data and machine learning to streamline procedures)				.809
TRAIC9 (Hospital management has invested in networking infrastructure to support the application’s efficiency)				.942
TRAIC14 (The hospital has enough team members to get the work done using AI-based tools and projects)				.873
TRAIC15 (The hospital has given team members enough time to complete AI-based projects)				.967
AI capabilities-human resources
HRAI1 (In hospital, we have an internal and external talent pool with technical skills to support AI)				.815	.958
HRAI2 (I can use AI technologies such as machine learning, natural language processing, and deep learning effectively)				.827
HRAI3 (I possess the right skills to complete jobs)				.841
HRAI5 (We are provided with the required training to deal with AI applications)				.996
HRAI7 (I have appropriate experience to justify job requirements)				.993
HRAI9 (Hospital managers can work with stakeholders and data scientists to grasp opportunities that AI might bring)				.835
HRAI12 (Hospital management demonstrates ownership and commitment to AI initiatives)				.736

Figure 2.

Confirmatory factor analysis.

In addition, Table 8 shows the estimation parameters of latent variables to assess the indicator’s variance and covariance. Latent variables are seen as the indicator’s parameters. All latent variables have been significant parameters for the indicator, with a p-value (<.001) for each variable.

Table 8.

Estimated Parameters of Latent Variables.

Constructs	Parameter estimates	SE	T-values	p -Values
CT	.663	.114	5.808	.000
EWB	.703	.131	5.353	.000
HRAI	.911	.149	6.097	.000
INTAI	.874	.149	5.859	.000
OA	.832	.122	6.834	.000
TRAIC	.858	.099	8.643	.000

As per Table 9, CR values fit with the suggested threshold of >.70. Moreover, AVE values >.50 also show that it meets the suggested value (Dos Santos & Cirillo, 2023; Niclasen et al., 2013). Thus, there is no issue of validity as well as reliability in the data analyzed.

Table 9.

Scales Validity and Reliability.

Constructs	CA	CR	AVE
CT	.961	.963	.738
EWB	.937	.936	.624
HRAI	.958	.954	.753
INTAI	.968	.965	.754
OA	.951	.953	.649
TRAIC	.980	.979	.827

Note. AVE = average variance extracted; CA = Cronbach alpha; CR = composite reliability.

As per Table 10, all construct values fall under .85 as suggested by Cheung et al. (2023) and Henseler et al. (2015). Therefore, the discriminant validity is achieved for all constructs.

Table 10.

Discriminant Validity.

Constructs	CT	EWB	HRAI	INTAI	OA
CT
EWB	.812
HRAI	.134	.106
INTAI	.054	.067	.094
OA	.163	.238	.073	.121
TRAIC	.236	.178	.164	.087	.828

Furthermore, bootstrapping was run in CB-SEM to assess the significance of the validated scale. All the specified loadings are significant as shown in Table 11.

Table 11.

Bootstrapping Using CB-SEM.

Items	Beta	SD	T -statistics	p -Values	CI
Items	Beta	SD	T -statistics	p -Values	Low	High
CT1 ← CT	.823	.091	9.002	.000	.692	.899
CT2 ← CT	.881	.065	13.464	.000	.799	.922
CT3 ← CT	.763	.071	10.739	.000	.624	.791
CT4 ← CT	.713	.094	7.600	.001	.573	.794
CT5 ← CT	.856	.079	10.867	.000	.672	.861
CT6 ← CT	.948	.059	16.137	.000	.823	.98
CT7 ← CT	.905	.054	16.635	.000	.782	.909
CT8 ← CT	.873	.068	12.839	.000	.787	.910
CT9 ← CT	.938	.059	15.835	.000	.807	.965
EWB1 ← EWB	.759	.059	12.862	.000	.644	.752
EWB2 ← EWB	.805	.029	27.479	.000	.786	.836
EWB3 ← EWB	.839	.08	10.554	.000	.712	.893
EWB4 ← EWB	.840	.028	30.174	.000	.794	.859
EWB5 ← EWB	.729	.084	8.646	.000	.610	.718
EWB6 ← EWB	.760	.056	13.505	.000	.672	.749
EWB7 ← EWB	.778	.058	13.525	.000	.660	.793
EWB8 ← EWB	.775	.112	6.930	.001	.600	.843
EWB9 ← EWB	.815	.029	28.132	.000	.780	.833
HRAI1 ← HRAI	.815	.063	13.024	.000	.747	.829
HRAI12 ← HRAI	.736	.038	19.401	.000	.666	.755
HRAI2 ← HRAI	.827	.076	10.905	.000	.691	.84
HRAI3 ← HRAI	.841	.074	11.321	.000	.768	.84
HRAI5 ← HRAI	.996	.005	208.249	.000	.984	.994
HRAI7 ← HRAI	.993	.012	80.404	.000	.961	.993
HRAI9 ← HRAI	.835	.074	11.254	.000	.621	.806
INTAI1 ← INTAI	.796	.079	10.016	.000	.633	.805
INTAI15 ← INTAI	.970	.019	50.533	.000	.920	.948
INTAI16 ← INTAI	.811	.029	28.410	.000	.728	.793
INTAI2 ← INTAI	.833	.026	31.865	.000	.784	.838
INTAI3 ← INTAI	.987	.016	59.970	.000	.942	.982
INTAI4 ← INTAI	.846	.038	22.391	.000	.783	.86
INTAI5 ← INTAI	.858	.011	78.798	.000	.853	.87
INTAI6 ← INTAI	.899	.021	42.385	.000	.844	.898
INTAI9 ← INTAI	.789	.115	6.868	.001	.550	.811
OA1 ← OA	.889	.024	36.588	.000	.852	.896
OA10 ← OA	.814	.019	41.768	.000	.771	.819
OA11 ← OA	.797	.023	33.947	.000	.707	.763
OA2 ← OA	.830	.052	16.094	.000	.748	.828
OA3 ← OA	.849	.017	48.853	.000	.838	.86
OA4 ← OA	.722	.047	15.242	.000	.559	.682
OA5 ← OA	.796	.030	26.237	.000	.712	.78
OA6 ← OA	.855	.041	20.954	.000	.816	.906
OA7 ← OA	.772	.084	9.23	.000	.639	.774
OA8 ← OA	.81	.043	18.899	.000	.760	.831
OA9 ← OA	.711	.029	24.706	.000	.651	.721
TRAIC1 ← TRAIC	.992	.003	352.204	.000	.987	.994
TRAIC14 ← TRAIC	.873	.064	13.555	.000	.758	.926
TRAIC15 ← TRAIC	.967	.009	106.879	.000	.925	.945
TRAIC2 ← TRAIC	.991	.003	288.162	.000	.987	.995
TRAIC3 ← TRAIC	.896	.060	15.034	.000	.796	.957
TRAIC4 ← TRAIC	.983	.016	61.848	.000	.938	.971
TRAIC5 ← TRAIC	.875	.038	22.871	.000	.787	.889
TRAIC6 ← TRAIC	.730	.103	7.065	.001	.542	.783
TRAIC7 ← TRAIC	.809	.067	12.006	.000	.679	.826
TRAIC9 ← TRAIC	.942	.015	62.799	.000	.899	.928

Discussion and Conclusion

This study attempted to validate the developed scale on AI capabilities, OA, CT, and EWB for doctors working in Pakistani private sector hospitals. Our study provides an empirically validated scale that researchers and practitioners can use to assess the link between AI capabilities, OA, CT, and EWB from the theoretical lens of DOIT. Results indicate a good fit, along with the reliability and validity of the scales. So, AI capabilities are viewed as intangible resources, tangible resources, and human resources. The questionnaires were taken from Mikalef et al. (2022), Mikalef and Gupta (2021) to apply and validate in the Pakistani context. Moreover, to measure EWB, the scale was taken from Juchnowicz and Kinowska (2021) to evaluate relationships, work-life balance, and physical and psychological health. Furthermore, CT was assessed using the scale adapted from Greene (2019) to evaluate the communication and feedback loop. Lastly, the scale was adapted from Lubatkin et al. (2006), Ojiako et al. (2023) to measure OA. However, a few items were excluded due to the low factor values according to the results of our study.

Organizations need to assess their capabilities in an era of technological changes related to AI capability, EWB, CT, and OA. Therefore, it is important to have a scale to determine the aspects in different sectors and contexts with different factors (Böhmer & Schinnenburg, 2023). Our study filled this gap by validating the scale in healthcare settings. Accordingly, this integrated approach to validate the scale for the healthcare setting in this study can be used by practitioners to assess their capabilities to adopt the technology promptly. This study provides a framework for understanding an employee-centric approach to how innovation, like AI, can affect organizational systems. In addition, DOIT’s integrated framework with AI capabilities, CT, OA, and EWB demonstrates that effective execution requires an ecosystem within a complex organizational setup like healthcare. This integrated framework will help lower the adoption barrier and create a comprehensive foundation for the sustained adoption of innovation in healthcare organizations.

It is concluded that healthcare organizations can use the validated scale to assess their capacity, processes, efficiency, communication, and control mechanisms towards adopting innovation such as AI from the view of organizational tangible, intangible, human resources, CT, EWB, and OA. Moreover, using DOIT as a theoretical lens to formulate a strategy regarding the development of organizational capabilities to meet the organizational demand for adoption of technologies, leading to improved existing processes, and EWB using effective communication channels.

Theoretical and Practical Implications

Our study has made the following contributions. Initially, our study seals the gap by validating the scale related to AI capabilities in private sector hospitals in Pakistan as per the call of Böhmer and Schinnenburg (2023). Furthermore, this study contributes to the theoretical development of DIOT (Rogers, 1983, 1987) by applying it to the AI implementation in healthcare settings, particularly private sector hospitals in Pakistan. In addition, this study offers a multidimensional perspective by integrating DOIT with AI capabilities, EWB, CT, and OA to specify how innovation diffuses across complex systems like healthcare settings. In addition, the scales provide empirically grounded tools for assessing how the organizational and individual factors can influence the adoption, adaptation, and internalization of AI innovation across various elements of DOIT, which are not represented in prior studies. This study enriches the extent of DOIT not only from the theoretical context but also provides a robust measurement tool to be used by researchers and practitioners. This study also shows that innovation diffusion is embedded in human systems and organizational factors, contributing to the validation of measurement tools that can be used for meaningful analysis across various settings and cultures.

This study extends the DOIT (Rogers, 1983, 1987) perspective on AI capabilities, OA, CT, and EWB. To provide a holistic framework, five elements of DOIT are used to develop a guide for integrated AI using a people-centric approach in which AI capabilities affect EWB in the presence of CT and OA, as mentioned in Table 12.

Table 12.

DOIT Application to AI Capabilities, EWB, CT, and OA.

DOIT elements	AI capability	EWB	CT	OA
Innovation (AI tools)	Tangible resources (tech infrastructure and AI tools)	Workload reduction and improving work-life balance	Use of employee feedback to recalibrate AI	Testing and refining process for AI adaptation
Communication (internal sharing tools and AI-powered HR platforms)	Intangible resources (organization culture and communication channels)	Improve readiness and relationships	Monitor the communication mechanism	Using new channels and standardizing the existing ones
Time (gradual and rapid)	Human resources (change management skills and training)	Foster resilience and EWB	Use an adaptive timeline based on feedback received from employees	Implement new strategies promptly
Social system (team readiness to support AI implementation)	Intangible and human resources (trust, leadership styles, and peer influence)	Support and improve psychological wellbeing	Feedback loops to the leader for correction	Encourage an innovative culture and build synergy among team members
Adopter categories (innovator, early and late adopters, and laggards)	Human resources (employee adaptive performance)	Innovators may feel energized, while laggards feel left behind affects physical and psychological wellbeing	Tailor training and support based on feedback received from the adopters or the laggards’ category	Support early adopters and improve the existing practices across an organization

From the practical context, healthcare practitioners can use the AI capability scale to assess their capacity for adopting technologies from the perspective of tangible, intangible, and human resources. This will help identify gaps from the capability perspective on technological readiness to automate the process using AI to align the operational priorities, such as diagnostic and patient management, using AI capabilities. Furthermore, the EWB validated scale can be used by practitioners to track the work-life balance, relationships, and physical and psychological well-being among the doctors working in private sector hospitals during AI implementation, thereby enabling the timely use of interventions for better EWB. Therefore, the EWB scale can help managers manage job burnout, stress, and emotional exhaustion in the hospital context (Angioha et al., 2020; Bakker et al., 2005).

Subsequently, the OA scale can assess the balance between exploration (innovations like AI) and exploitation (existing processes). This may help in strategic alignment and allocation of resources to create a balance between exploitation and exploration levels. Moreover, the CT scale can be used to develop a feedback system that allows healthcare practitioners and managers to monitor the technological adoption and adjust the system and strategies based on doctors’ input.

Limitations and Directions for Future Studies

The following are the limitations of this study. Primarily, the sample size was limited to 150 doctors working in private sector hospitals in Karachi, Pakistan; however, it is relatively small and context-specific, which may limit the generalizability of the study and may not be representative of the public sector hospitals. Thus, the cultural norms, availability of resources, and sector-specific dynamics may differ in the public sector and other regions, affecting the generalizability of the provided insights. Therefore, future researchers may validate this scale on a larger sample size in public sector hospitals and conduct comparative research to expand the horizon and generalizability of the study, along with focusing on other geographical territories.

Moreover, future researchers may collect cross-level data from managers and nurses on AI capability, EWB, OA, and CT, as it remains unexplored in this study. In addition, the data was taken at one point in time. So, future researchers may examine this scale using longitudinal studies to capture the temporal specifications that surface between AI capability, CT, OA, and EWB. In addition, only CFA was run to validate the scale; future studies may use the validated scale to test the study variables’ relationship by keeping AI capability and EWB as predictors and outcome variables, and CT and OA as mediators or moderators between AI-EWB link using PLS-SEM for analysis on a larger sample size. Accordingly, these validated scales allow researchers to examine the mediating and moderating roles in innovation diffusion, like AI, to refine and expand the DOIT explanatory power.

Footnotes

Acknowledgements

This work was supported by a postdoctoral fellowship from the Universiti Malaysia Kelantan, Kelantan, Malaysia.

ORCID iDs

Munaza Bibi

Tse Guan Tan

Heng Yao

Ethical Considerations

The Faculty of Management Sciences, Bahria University, Pakistan, granted ethical clearance: Letter No. BUKC/DMS/2024 (111).

Consent to Participate

Not applicable as we declare that no data related to human medical intervention, and tissues were involved in this study. Principles of informed consent, data confidentiality, and voluntary participation were strictly followed.

Author Contributions

Munaza Bibi: Original draft, investigation, formal analysis, conceptualization, validation, methodology. Tse Guan Tan: Review & editing, original draft, supervision. Heng Yao: Analysis, review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data will be available on request.

References

Abou-Foul

Ruiz-Alba

López-Tenorio

(2022). The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective. Journal of Business Research, 157, Article 113609. https://doi.org/10.1016/j.jbusres.2022.113609

Al Naqbi

Bahroun

Ahmed

(2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), Article 1166.

Álvarez-Fontalvo

Guerrero-Navarro

L. M.

Melamed-Varela

(2024). Knowledge management through organizational cybernetic theory: Insights of a bibliometric analysis. In Rocha

Á.

Montenegro

Pereira

E. T.

Victor

J. A. M.

Ibarra

(Eds.), Management, tourism and smart technologies (Vol. 1190, pp. 516–525). Springer Nature.

Amini

Jahanbakhsh Javid

(2023). A multi-perspective framework established on diffusion of innovation (DOI) theory and technology, organization and environment (TOE) framework toward supply chain management system based on cloud computing technology for small and medium enterprises (SSRN Scholarly Paper 4340207). Social Science Research Network. https://papers.ssrn.com/abstract=4340207

Angioha

P. U.

Omang

T. A.

Ishie

E. U.

Iji

M. E.

(2020). Employee stressors and wellbeing of healthcare workers in government owned hospitals in Calabar, Nigeria. Journal of Public Administration, 2(4), Article 2020.

Anitha

Shanthi

(2020). Employee well-being-a path to redefine workplace. Sumedha Journal of Management, 9(1), 79–90.

Armenia

Barile

Iandolo

Pompei

Sicca

L. M.

(2023). Organisational ambidexterity and knowledge management: A systems perspective towards Smart Model-based Governance. Systems Research and Behavioral Science, 41(3), 439–452. https://doi.org/10.1002/sres.2985

Assidi

Omran

Rana

Borgi

(2025). The role of AI adoption in transforming the accounting profession: A diffusion of innovations theory approach. Journal of Accounting & Organizational Change. Advance online publication. https://doi.org/10.1108/JAOC-04-2024-0124

Baabdullah

A. M.

(2024). The precursors of AI adoption in business: Towards an efficient decision-making and functional performance. International Journal of Information Management, 75, Article 102745.

10.

Babu

Marda

Mishra

Bhattar

Ahluwalia

, & Edupub Services (2024). The impact of artificial intelligence in the workplace and its effect on the digital wellbeing of employees. Journal for Studies in Management and Planning, 10, 1–32. https://doi.org/10.5281/zenodo.10936348

11.

Bakker

A. B.

Demerouti

Euwema

M. C.

(2005). Job resources buffer the impact of job demands on burnout. Journal of Occupational Health Psychology, 10(2), 170–180. https://doi.org/10.1037/1076-8998.10.2.170

12.

Baltazar

A. P.

(2021). Rethinking cybernetics with a transfunctional approach to structure and organization. Technoetic Arts, 19(1), 49–60. https://doi.org/10.1386/tear_00050_1

13.

Bankins

Formosa

(2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185, 1–16. https://doi.org/10.1007/s10551-023-05339-7

14.

Bankins

Ocampo

A. C.

Marrone

Restubog

S. L. D.

Woo

S. E.

(2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159–182. https://doi.org/10.1002/job.2735

15.

Bibi

Tan

T. G.

Yao

(2025). Exploring the impact of AI capabilities on employee well-being: A mediated moderation analysis. SAGE Open, 15(3), Article 21582440251361981. https://doi.org/10.1177/21582440251361981

16.

Bilgiç

Aytaç

(2024). School occupational health and Safety Performance Scale: Validity and reliability study for scale development. İnsan ve Sosyal Bilimler Dergisi, 7(1), 104–123. https://doi.org/10.53048/johass.1473563

17.

Bodea

C.-N.

Paparic

Mogos

R. I.

Dascalu

M.-I.

(2024). Artificial intelligence adoption in the workplace and its impact on the upskilling and reskilling strategies. Amfiteatru Economic, 26(65), 126–144.

18.

Böhmer

Schinnenburg

(2023). Critical exploration of AI-driven HRM to build up organizational capabilities. Employee Relations: The International Journal, 45(5), 1057–1082. https://doi.org/10.1108/ER-04-2022-0202

19.

Brynjolfsson

McAfee

(2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.

20.

Brynjolfsson

McAfee

(2017). Machine, platform, crowd: Harnessing our digital future (Vol. 4). W. W. Norton & Company. https://www.mintur.gob.es/Publicaciones/Publicacionesperiodicas/EconomiaIndustrial/RevistaEconomiaIndustrial/409/SEGUNDA%20CR%C3%8DTICA%20DE%20LIBROS.pdf

21.

Bryson

Forth

Stokes

(2015). Does worker wellbeing affect workplace performance? Discussion Papers, No. 9096, Institute for the Study of Labor (IZA), Bonn.

22.

Buick

Blackman

D. A.

Glennie

Weeratunga

O’Donnell

M. E.

(2024). Different approaches to managerial support for flexible working: Implications for public sector employee well-being. Public Personnel Management, 53(3), 377–405. https://doi.org/10.1177/00910260241226731

23.

Canbul Yaroğlu

(2024). The effects of artificial intelligence on organizational culture in the perspective of the hermeneutic cycle: The intersection of mental processes. Systems Research and Behavioral Science. Advance online publication. https://doi.org/10.1002/sres.3037

24.

Cave

Craig

Dihal

Dilon

Montgomery

Singler

Taylor

L. C.

(2018). Portrayals and perceptions of AI and why they matter. The Royal Society.

25.

Çelik

Uzunçarşılı

Ü.

(2023). Is the effect of organizational ambidexterity and technological innovation capability on firm performance mediated by competitive advantage? An empirical research on Turkish manufacturing and service industries. SAGE Open, 13(4), Article 21582440231206367. https://doi.org/10.1177/21582440231206367

26.

Chakma

Paul

Dhir

(2021). Organizational ambidexterity: A review and research agenda. IEEE Transactions on Engineering Management, 71, 121–137.

27.

Chakma

Paul

Dhir

(2024). Organizational ambidexterity: A review and research agenda. IEEE Transactions on Engineering Management, 71, 121–137. https://doi.org/10.1109/TEM.2021.3114609

28.

Chen

(2024). Influence of employees’ intention to adopt AI applications and big data analytical capability on operational performance in the high-tech firms. Journal of the Knowledge Economy, 15(1), 3946–3974. https://doi.org/10.1007/s13132-023-01293-x

29.

Cheung

G. W.

Cooper-Thomas

H. D.

Lau

R. S.

Wang

L. C.

(2023). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41, 745–783. https://doi.org/10.1007/s10490-023-09871-y

30.

Choudhury

M. A.

(2024). Cybernetics in socio-scientific systems. In Choudhury

M. A.

(Ed.), Handbook of Islamic philosophy of science (pp. 1031–1049). Springer Nature.

31.

Chowdhury

Dey

Joel-Edgar

Bhattacharya

Rodriguez-Espindola

Abadie

Truong

(2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), Article 100899.

32.

Danaher

Hogan

M. J.

Noone

Kennedy

Behan

De Paor

Felzmann

Haklay

Khoo

S.-M.

Morison

Murphy

M. H.

O’Brolchain

Schafer

Shankar

(2017). Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big Data & Society, 4(2), Article 205395171772655. https://doi.org/10.1177/2053951717726554

33.

De Mattos

C. A.

Laurindo

F. J. B.

(2017). Information technology adoption and assimilation: Focus on the suppliers portal. Computers in Industry, 85, 48–57.

34.

Deswal

Arora

(2025). Interplay between workplace spirituality and employee wellbeing: The mediating roles of EI and AI. In Barua

(Ed.), Practices, challenges, and deterrents in workplace wellbeing: Strategies for building resilient and thriving workplaces (pp. 45–66). IGI Global Scientific Publishing.

35.

DeYoung

C. G.

(2015). Cybernetic big five theory. Journal of Research in Personality, 56, 33–58. https://doi.org/10.1016/j.jrp.2014.07.004

36.

Dos Santos

P. M.

Cirillo

M. Â.

(2023). Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions. Communications in Statistics - Simulation and Computation, 52(4), 1639–1650. https://doi.org/10.1080/03610918.2021.1888122

37.

Dranev

Izosimova

Meissner

(2020). Organizational ambidexterity and performance: Assessment approaches and empirical evidence. Journal of the Knowledge Economy, 11(2), 676–691. https://doi.org/10.1007/s13132-018-0560-y

38.

Espejo

(2017). Organisational systems and innovation. Prospettive in Organizzazione, 6, Article 2017.

39.

Espejo

Reyes

(2011). Organizational systems: Managing complexity with the viable system model. Springer.

40.

Fassa

P. D. L.

Regenbrecht

P. H.

Fahms

P. P. S.

(2019). The internet of things. Australian Council of Learned Academies (ACOLA).

41.

Feijóo

Kwon

Bauer

J. M.

Bohlin

Howell

Jain

Potgieter

Whalley

Xia

(2020). Harnessing artificial intelligence (AI) to increase wellbeing for all: The case for a new technology diplomacy. Telecommunications Policy, 44(6), Article 101988.

42.

Fischer

Riedl

(2015). Theorizing technostress in organizations: A cybernetic approach [Conference session]. In 12th International Conference on Wirtschaftsinformatik, Osnabrück, Germany.

43.

Forlano

(2025). CybORGs. Cybernetic organisms and cybernetic organizations. Sociologica, 19(1), 179–183.

44.

Frey

C. B.

Osborne

M. A.

(2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019

45.

Fuller

C. M.

Simmering

M. J.

Atinc

Babin

B. J.

(2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192–3198.

46.

Girolami

Schmidt

Adesso

(2015). Towards quantum cybernetics. Annalen Der Physik, 527(9–10), 757–764. https://doi.org/10.1002/andp.201500133

47.

Greene

(2019). Systems design thinking: Identification and measurement of attitudes for systems engineering, systems thinking, and design thinking [Thesis]. http://deepblue.lib.umich.edu/handle/2027.42/151577

48.

Groumpos

P. P.

(2024). The cybernetic artificial intelligence (CAI): A new scientific field for modelling and controlling Complex Dynamical Systems. IFAC-PapersOnLine, 58(3), 145–152.

49.

Gschwantner

(2018). Ambidexterity and management control. Johannes Kepler Universität Linz.

50.

Hair

J. F.

Black

W. C.

Babin

B. J.

Anderson

R. E.

(2018). Multivariate data analysis (8th ed.). Cengage India.

51.

Hair

Hult

G. T. M.

Ringle

C. M.

Sarstedt

(2013). A primer on partial least squares structural equation modeling (1st ed.). Sage Publications, Inc.

52.

Harman

H. H.

(1976). Modern factor analysis. University of Chicago Press.

53.

Henseler

Ringle

C. M.

Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8

54.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

55.

Hwang

B.-N.

Lai

Y.-P.

Wang

(2023). Open innovation and organizational ambidexterity. European Journal of Innovation Management, 26(3), 862–884.

56.

Johnson

Dey

Nguyen

Groth

Joyce

Tan

Glozier

Harvey

S. B.

(2020). A review and agenda for examining how technology-driven changes at work will impact workplace mental health and employee well-being. Australian Journal of Management, 45(3), 402–424. https://doi.org/10.1177/0312896220922292

57.

Juchnowicz

Kinowska

(2021). Employee well-being and digital work during the COVID-19 pandemic. Information, 12(8), Article 8. https://doi.org/10.3390/info12080293

58.

Kassotaki

(2022). Review of organizational ambidexterity research. SAGE Open, 12(1), Article 215824402210821. https://doi.org/10.1177/21582440221082127

59.

Koryak

Lockett

Hayton

Nicolaou

Mole

(2018). Disentangling the antecedents of ambidexterity: Exploration and exploitation. Research Policy, 47(2), 413–427. https://doi.org/10.1016/j.respol.2017.12.003

60.

Krippendorff

(2007). The cybernetics of design and the design of cybernetics. Kybernetes, 36(9–10), 1381–1392. https://doi.org/10.1108/03684920710827364

61.

Krippendorff

(2021). From uncritical design to critical examinations of its systemic consequences. https://openresearch.ocadu.ca/id/eprint/3813/

62.

Labay

D. G.

Kinnear

T. C.

(1981). Exploring the consumer decision process in the adoption of solar energy systems. Journal of Consumer Research, 8(3), Article 271. https://doi.org/10.1086/208865

63.

Lavanderos

(2022). From cybersin to cybernet. Considerations for a cybernetics design thinking in the socialism of the XXI century. AI & SOCIETY, 37(3), 1279–1292. https://doi.org/10.1007/s00146-021-01354-2

64.

Levinthal

D. A.

March

J. G.

(1993). The myopia of learning. Strategic Management Journal, 14, 95–112.

65.

Lubatkin

M. H.

Simsek

Ling

Veiga

J. F.

(2006). Ambidexterity and performance in small-to medium-sized firms: The pivotal role of top management team behavioral integration. Journal of Management, 32(5), 646–672. https://doi.org/10.1177/0149206306290712

66.

Maden

van der Lomas

J. D.

Hekkert

(2022). Design for wellbeing during Covid-19: A cybernetic perspective on data feedback loops in complex socIotechnical systems. In Lockton

Lenzi

Hekkert

Oak

Sádaba

Lloyd

(Eds.), DRS2022: Bilbao, Bilbao, Spain.

67.

Mahendra

Kurniawati

D. T.

(2024). Flexi-time as a predictor of employee performance in public hospital sector: Mediating role of work-life balance and job satisfaction. International Journal of Research in Business and Social Science, 13(3), 194–204.

68.

Marquez

(2024). COVID-19 pandemic: Enervation of mental health professionals [PhD thesis, The Chicago School of Professional Psychology]. https://search.proquest.com/openview/15cc1f7fad8c696e3e8fbe7c5ad3f8a3/1?pq-origsite=gscholar&cbl=18750&diss=y

69.

Martins

Oliveira

Thomas

M. A.

(2016). An empirical analysis to assess the determinants of SaaS diffusion in firms. Computers in Human Behavior, 62, 19–33.

70.

McKinsey & Company. (2022). What is industry 4.0 and the Fourth Industrial Revolution? McKinsey & Company. https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-are-industry-4-0-the-fourth-industrial-revolution-and-4ir

71.

Mikalef

Gupta

(2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), Article 103434. https://doi.org/10.1016/j.im.2021.103434

72.

Mikalef

Lemmer

Schaefer

Ylinen

Fjørtoft

S. O.

Torvatn

H. Y.

Gupta

Niehaves

(2022). Enabling AI capabilities in government agencies: A study of determinants for European municipalities. Government Information Quarterly, 39(4), Article 101596. https://doi.org/10.1016/j.giq.2021.101596

73.

Mom

T. J. M.

Chang

Y.-Y.

Cholakova

Jansen

J. J. P.

(2019). A multilevel integrated framework of firm HR practices, individual ambidexterity, and organizational ambidexterity. Journal of Management, 45(7), 3009–3034. https://doi.org/10.1177/0149206318776775

74.

Montagnini

(2017). Interdisciplinarity in Norbert Wiener, a mathematician-philosopher of our time. Biophysical Chemistry, 229, 173–180. https://doi.org/10.1016/j.bpc.2017.06.009

75.

Montagnini

Tabacchi

M. E.

Termini

(2016). Out of a creative jumble of ideas in the middle of last Century: Wiener, interdisciplinarity, and all that. Biophysical Chemistry, 208, 84–91. https://doi.org/10.1016/j.bpc.2015.05.004

76.

Murugesan

Subramanian

Srivastava

Dwivedi

(2023). A study of artificial intelligence impacts on human resource digitalization in Industry 4.0. Decision Analytics Journal, 7, Article 100249. https://doi.org/10.1016/j.dajour.2023.100249

77.

Myrvang

N. A.

(2020). The relationship between employee well-being, burnout and perceived organizational support in healthcare professionals. Journal of International Health Sciences and Management, 6(12), 34–39.

78.

Niclasen

Skovgaard

A. M.

Andersen

A.-M. N.

Sømhovd

M. J.

Obel

(2013). A confirmatory approach to examining the factor structure of the strengths and difficulties questionnaire (SDQ): A large scale cohort study. Journal of Abnormal Child Psychology, 41(3), 355–365. https://doi.org/10.1007/s10802-012-9683-y

79.

Ojiako

Petro

Marshall

Williams

(2023). The impact of project portfolio management practices on the relationship between organizational ambidexterity and project performance success. Production Planning & Control, 34(3), 260–274. https://doi.org/10.1080/09537287.2021.1909168

80.

Olan

Ogiemwonyi Arakpogun

Suklan

Nakpodia

Damij

Jayawickrama

(2022). Artificial intelligence and knowledge sharing: Contributing factors to organizational performance. Journal of Business Research, 145, 605–615. https://doi.org/10.1016/j.jbusres.2022.03.008

81.

Oosthuizen

R. M.

(2019). Smart Technology, Artificial Intelligence, Robotics and Algorithms (STARA): Employees’ perceptions and wellbeing in future workplaces. In Potgieter

I. L.

Ferreira

Coetzee

(Eds.), Theory, research and dynamics of career wellbeing (pp. 17–40). Springer International Publishing.

82.

O’Reilly

C. A.

Tushman

M. L.

(2011). Organizational ambidexterity in action: How managers explore and exploit. California Management Review, 53(4), 5–22. https://doi.org/10.1525/cmr.2011.53.4.5

83.

Orsila

Luukkaala

Manka

M.-L.

Nygard

C.-H.

(2011). A new approach to measuring work-related well-being. International Journal of Occupational Safety and Ergonomics, 17(4), 341–359. https://doi.org/10.1080/10803548.2011.11076900

84.

Osejo-Bucheli

(2025). Purposeful evolution in organisational cybernetics: symbiosis, co-Evolution, transduction and axiology in viable systems. Systems Research and Behavioral Science. Advance online publication. https://doi.org/10.1002/sres.3122

85.

Osmanoglu

(2025, April 28–29). The theory of cybernetics for managing [Conference session]. Advances in Information and Communication: Proceedings of the 2025 Future of Information and Communication Conference (FICC), Berlin, Germany.

86.

Panda

Rath

S. K.

(2018). Modelling the relationship between information technology infrastructure and organizational agility: A study in the context of India. Global Business Review, 19(2), 424–438. https://doi.org/10.1177/0972150917713545

87.

Patnaik

Bakkar

(2024). Exploring determinants influencing artificial intelligence adoption, reference to diffusion of innovation theory. Technology in Society, 79, Article 102750.

88.

Peña-Ayala

Cárdenas-Robledo

L. A.

(2019). A cybernetic method to regulate learning through learning strategies: A proactive and reactive mechanism applied in U–Learning settings. Computers in Human Behavior, 98, 196–209.

89.

Pfiffner

(2022). A model of control and communication. In Pfiffner

(Ed.), The neurology of business (pp. 53–62). Springer International Publishing.

90.

Pradhan

Hati

Kumar

(2017). Impact of employee wellbeing on psychological empowerment: Mediating role of happiness. International Journal of Manufacturing Technology and Management, 31, Article 581. https://doi.org/10.1504/IJMTM.2017.089083

91.

Pradhan

R. K.

Hati

(2022). The measurement of employee well-being: Development and validation of a scale. Global Business Review, 23(2), 385–407. https://doi.org/10.1177/0972150919859101

92.

Raiden

Räisänen

Kinman

(2020). Behavioural ambidexterity: Effects on individual well-being and high performance work in academia. Journal of Further and Higher Education, 44(4), 568–582. https://doi.org/10.1080/0309877X.2019.1596232

93.

Restuputri

D. P.

Masudin

Septira

A. P.

Govindan

Widayat

(2024). The role of knowledge management to improve organizational performance through organizational ambidexterity within the uncertainties. Business Process Management Journal, 30(7), 2237–2282.

94.

Rigdon

E. E.

Sarstedt

Ringle

C. M.

(2017). On comparing results from CB-SEM and PLS-SEM: Five perspectives and five recommendations. Marketing: ZFP – Journal of Research and Management, 39(3), 4–16.

95.

Rogers

E. M.

(1981). Diffusion of innovations: An overview. In Anderson

J. G.

Jay

S. J.

(Eds.), Use and impact of computers in clinical medicine (pp. 113–131). Springer New York.

96.

Rogers

E. M.

(1983). Diffusion of innovations (3rd ed.). Free Press.

97.

Rogers

E. M.

(1987). Diffusion of innovations: An overview. In Anderson

J. G.

Jay

S. J.

(Eds.), Use and impact of computers in clinical medicine (pp. 113–131). Springer.

98.

Rogers

E. M.

Singhal

Quinlan

M. M.

(2014). Diffusion of innovations. In Stacks

D. W.

Eichhorn

K. C.

Salwen

M. B.

(Eds.), An integrated approach to communication theory and research (pp. 432–448). Routledge.

99.

Sadeghi

(2024). Employee well-being in the age of AI: Perceptions, concerns, behaviors, and outcomes. arXiv arXiv:2412.04796. https://doi.org/10.48550/arXiv.2412.04796

100.

Salas-Vallina

Alegre

Ferrer-Franco

(2022). Well-being-oriented management (WOM), organizational learning and ambidexterity in public healthcare: A two wave-study. International Public Management Journal, 25(6), 815–840. https://doi.org/10.1080/10967494.2021.1942341

101.

Sarstedt

Hair

J. F.

Ringle

C. M.

(2023). “PLS-SEM: Indeed a silver bullet”– retrospective observations and recent advances. Journal of Marketing Theory and Practice, 31(3), 261–275. https://doi.org/10.1080/10696679.2022.2056488

102.

Sarstedt

Ringle

C. M.

Hair

J. F.

(2014). PLS-SEM: Looking back and moving forward. Long Range Planning, 47(3), 132–137. https://doi.org/10.1016/j.lrp.2014.02.008

103.

Schwab

(2016). The fourth industrial revolution. World Economic Forum. https://www.weforum.org/about/the-fourth-industrial-revolution-by-klaus-schwab/

104.

Sjödin

Parida

Palmié

Wincent

(2021). How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research, 134, 574–587. https://doi.org/10.1016/j.jbusres.2021.05.009

105.

Slemp

Kern

Vella-Brodrick

(2015). Workplace well-being: The role of job crafting and autonomy support. Psychology of Well-Being, 5, Article 7. https://doi.org/10.1186/s13612-015-0034-y

106.

Sliż

Jackowska

(2025). AI implementation and organizational ambidexterity in the context of BPM: Polish service sector experience. Journal of Management and Financial Sciences, 52, 63–76.

107.

Sohani

Valizadeh-Haghi

Nasibi-Sis

Zandkarimi

Sheikhshoaei

(2024). An evaluation of organizational climate and its relationship with job burnout in hospital and college libraries. Performance Measurement and Metrics, 25(3/4), 117–126.

108.

Soomro

Fan

Sohu

J. M.

Soomro

Shaikh

S. N.

(2024). AI adoption: A bridge or a barrier? The moderating role of organizational support in the path toward employee well-being. Kybernetes, 54. Advance online publication. https://doi.org/10.1108/K-07-2024-1889

109.

Sorsanen

(2009). Examining management control systems packages and organisational ambidexterity-case Tekla Oyj. Helsinki School of Economics. https://aaltodoc.aalto.fi/items/88761fc4-abef-47d1-a07d-8019f09c9549

110.

Stovall

(2023). The unexpected ways AI is boosting employee well-being. Total Wellness. https://info.totalwellnesshealth.com/blog/ai-and-employee-well-being

111.

Walsh

Levy

Bell

Elliott

Maclaurin

Mareels

Wood

(2019). The effective and ethical development of artificial intelligence. ACOLA.org.

112.

Walter

(2024). Harmonising humans and technology: Exploring the dynamics of cognitive production, artificial intelligence and social communication in cybernetic systems. Open Research Europe, 4, Article 201.

113.

Wiener

(1948). Cybernetics Cambridge. MIT Press.

114.

Wirtz

(2020). Organizational ambidexterity: Cost-effective service excellence, service robots, and artificial intelligence. Organizational Dynamics, 49(3), 1–9.

115.

Wong

K. K.

(2023). Cybernetical intelligence: Engineering cybernetics with machine intelligence. John Wiley & Sons.

116.

Wulff

Finnestrand

(2024). Creating meaningful work in the age of AI: Explainable AI, explainability, and why it matters to organizational designers. AI & SOCIETY, 39(4), 1843–1856. https://doi.org/10.1007/s00146-023-01633-0

117.

Xia

Zhang

(2022). Economic, functional, and social factors influencing electric vehicles’ adoption: An empirical study based on the diffusion of innovation theory. Sustainability, 14(10), Article 6283.

118.

Kee

K. F.

Yamamoto

Riggs

R. E.

(2024). Examining the diffusion of innovations from a dynamic, differential-effects perspective: A longitudinal study on AI adoption among employees. Communication Research, 51(7), 843–866. https://doi.org/10.1177/00936502231191832

119.

Yousif

Asghar

Akbar

Masood

Arshad

M. R.

Naeem

Azam

Iqbal

(2024). Exploring the perspectives of healthcare professionals regarding artificial intelligence; acceptance and challenges. BMC Health Services Research, 24(1), Article 1200. https://doi.org/10.1186/s12913-024-11667-9

120.

Yunita

Sasmoko

Bandur

Alamsjah

(2023). Organizational ambidexterity: The role of technological capacity and dynamic capabilities in the face of environmental dynamism. Heliyon, 9(4), Article e14817.

121.

Zheng

Zhu

Zhao

Zhang

(2015). Erratum: Employee well-being in organizations: Theoretical model, scale development, and cross-cultural validation. Journal of Organizational Behavior, 36(5), 645–647. https://doi.org/10.1002/job.2033

Empirical Validation of Measurement Scales: AI Capabilities,Cybernetic Thinking,Organizational Ambidexterity,and Employee Wellbeing

Abstract

Plain language summary

Keywords

Introduction

Theoretical Underpinning and Overview of Scales

Diffusion of Innovation Theory

AI Capabilities

Employee Wellbeing

Cybernetic Thinking

Organizational Ambidexterity

Method

Participants and Scale Validation Overview

Measures

Data Analysis Procedure

Assessment before Factor Analysis

Harman’s Single Factor Test

Results

Factor Loadings, Alpha Values, and Model Fit

Discussion and Conclusion

Theoretical and Practical Implications

Limitations and Directions for Future Studies

Footnotes

Acknowledgements

ORCID iDs

Ethical Considerations

Consent to Participate

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References