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

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.

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