Determinants of the impact of smart human resource management adoption on organizational performance: Challenges and opportunities in a developing economy

Abstract

Background

The integration of digital technologies into human resource management is driving the transformation toward Smart Human Resource Management (SHRM), which enhances efficiency and strategic alignment. In developing economies like Bangladesh, challenges such as limited infrastructure and organizational readiness shape SHRM adoption and performance.

Objectives

This study investigates the determinants of SHRM adoption and its effects on organizational performance (OP) in Bangladesh. Specifically, it examines technological, organizational, and environmental factors within the Technology–Organization–Environment (TOE) and Resource-Based View (RBV) theory, focusing on the mediating role of SHRM adoption.

Methodology

A cross-sectional survey was investigated among 220 HR professionals as well as managers from diverse Bangladeshi organizations. Data were analyzed by structural equation modeling (SEM) in R Studio in order to test the conceptual model and validate hypotheses.

Key Results

Technological and organizational factors significantly drive SHRM adoption, whereas environmental stimuli have weaker effects. SHRM adoption positively enhances organizational performance. Mediation analysis shows that organizational readiness significantly mediates the relationship between SHRM adoption and performance, while technological and environmental mediations remain insignificant.

Conclusions

SHRM adoption improves organizational performance and highlights organizational preparedness as the critical enabler of successful SHRM implementation in developing economies.

Keywords

SHRM organizational performance TOE framework RBV theory developing economy

Introduction

The rapid adoption of digital solutions has greatly transformed the way human resources work worldwide.¹ This job, which was mainly organization and labor-based before, is now finding its place in an ever-evolving, technology-based field for managing people at work. This transformation has led to Smart Human Resource Management (SHRM), which is also called electronic HRM (e-HRM), and it relies on technologies, like artificial intelligence, cloud computing, and big data, to make HR management more effective.^2,3 HRM is progressively employing AI for functions, for example, recruitment, training and development, and performance assessment.⁴ AI-driven HRM approaches demonstrate significant potential to automate repetitive operations, improve decision-making, and deliver more tailored employee experiences.^5,6 Applying information and insights from smart HRM enables organizations to manage HR tasks efficiently and improve their operations in all aspects.^7,8 Companies today need to use Smart HRM due to continuous pressure to remain versatile, cost-conscious, and ahead in the competition.^9–11 Several firms in advanced economies are already enjoying the advantages of SHRM because they have strong digital services, an educated staff, and favorable technology policies that allow HR functions to operate more efficiently.¹² SHRM contributes to ensuring a high-performance work system, which consequently enhances employee skills, commitment, and productivity. Digital literacy facilitates SHRM in this regard by enhancing employees’ digital competencies, which support data-driven decision-making.¹³

The use of SHRM technologies is not the same everywhere in the world, particularly in developing economies.¹⁴ Organizations in these regions often face many obstacles, including a lack of infrastructural resources, poor digital literacy, insufficient support from authorized institutions, and people’s reluctance to change.^15,16 Besides, issues related to rules, unclear policies, and financial constraints are affecting factors in the adoption of SHRM technologies. This scenario indicates that it is important to examine how SHRM can help organizations in low-resource environments to achieve their wider objectives.⁹

The need for digital progress in HR for developing countries has been highlighted by global entities, for example, the International Labour Organization and the World Bank. Since automation and digitalization are transforming the global workforce, technology is now even more important in helping people develop their skills.¹⁷ It is expected by the ILO (2020) that a significant part of the workforce will require digital skills to adjust to changes that technology brings by 2030. So, organizations in developing countries need SHRM because it improves labor productivity, affects employment, and ensures the organization remains strong. Despite their growing relevance, studies on the adoption of SHRM practices in developing nations remain very limited. Research on this topic generally reviews advanced economies, where the use of digital technology has become mainstream.^18,19 Therefore, researchers do not fully understand the factors in emerging markets that make it hard to implement SHRM.¹⁷ While researchers have previously examined the factors behind HRM technology adoption, relatively few have looked into its effects on a company’s overall performance, considering mediating factors.²⁰

To address these research gaps, the study examines how the use of SHRM can affect a company’s performance in an emerging nation such as Bangladesh. This study employed the Technology–Organization–Environment (TOE) framework²¹ and Resource-Based View (RBV) theory to look at how things inside the organization, like technology and structure, as well as things outside the company, like the environment, impact people’s decisions regarding the use of SHRM systems.²² This study enhances theoretical positioning and addresses existing literature gaps by adopting a more integrated view of the TOE and RBV paradigm, moving beyond a strictly descriptive application. Although prior studies have extensively identified the attributes of digital HRM uptake, insufficient attention has been devoted to explaining how such attention translates into organizational performance. This situation is more evident in the context of emerging economics. Moreover, the concept of SHRM is inadequately conceived and sometimes conflated with e-HRM or digital HRM, which overshadows its strategic significance. The authors resolve these limitations by reconceptualizing SHRM as an adaptive, data-driven, and intelligent HR system that leverages advanced technologies and analytics to support the decision-making process. Moreover, the study also investigates how using SHRM affects the organizational performance.⁹ From the perspective of Bangladesh, this study holds particular relevance as it aims to capture the unique dynamics of SHRM adoption, which is overlooked by existing literature and largely reflects general or developed-country perspectives. In emerging countries, like Bangladesh, limitations in infrastructure, regulatory support, and digital skills significantly influence both the adoption and effectiveness of SHRM practices. These conditions make it essential for organizations to focus not only on adopting technologies but also on developing internal capabilities to utilize them effectively. Consequently, Bangladesh provides a valuable setting to demonstrate how contextual challenges can reshape theoretical relationships and drive capability-based innovation.

The rest of this study is structured as following sections such as section 2 represents a literature review with a brief description of the theoretical foundation and hypothesis development. Data collection process is presented in the section 3, sampling strategy, and the method of data analysis. Section 4 reports and illustrates the findings. Discussion is explained in the section 5, while section 6 outlines from the implications to conclusion.

Related literature

Theoretical foundation and hypothesis development

Many studies in information systems research examine factors that influence people’s or organizations’ attitudes toward using new systems or technology.²³ Adoption of various technologies, for example, in health records, fintech, mobile banking, and EdTech, was examined by using several accepted theories, such as the Technology Acceptance Model,^24,25 Unified Theory of Acceptance and Use of Technology,^17,26–28 and Theory of Planned Behavior.^22,29 This research used the TOE model aligned with the Resource-Based View (RBV) theory, which is best suited for analyzing when organizations embrace technology.^18,30,31 This technology readiness model reviews technological, organizational, and environmental aspects that are not always included in models that examine only one individual model, such as TAM, UTAUT, or TPB. Therefore, authors utilize the TOE framework in this study. While TOE explains the key determinants influencing SHRM adoption, it does not sufficiently capture how such adoption leads to improved organizational performance. To address this limitation, RBV is employed to explain how SHRM-related resources, such as HR analytics capability, digital infrastructure, and employees’ technological competencies, can function as valuable and strategic assets that contribute to competitive advantage. Through this integration, SHRM is conceptualized not merely as a technological tool but as a capability-building mechanism that mediates the association between TOE factors and organizational performance.

Technology Organization Environment (TOE) framework

According to Tornatzky and Fleischer,²¹ adopting and using any innovative system relies on three main factors: the areas of technology, the organization, and the context of the entire environment. The technological factors refer to the characteristics of available technologies that an organization can adopt, as well as the current state of technology that can be leveraged, whether it involves tangible assets like equipment within the organization or intangible elements such as the methods and processes applied in operations.^32,16 Organizational context shows the structure of the organization and the availability of the process to apply the technology.³³ Environmental context clarifies the ecological conditions, for example, business sector structure and characteristics, the outside backing available for embracing innovations, and government regulations.³⁴

Resource-Based View (RBV) theory

This research complemented the TOE and RBV theory to explain how SHRM practices enhance organizational performance. The RBV³⁵ posits that firms achieve a competitive advantage by possessing resources that are valued, scarce, inimitable, and non-substitutable. In the context of HRM, advanced technologies, digital infrastructure, data analytics, and integrated communications are considered strategic assets that boost efficiency, elevate guest experiences, and promote sustainability objectives.^36–38 From this viewpoint, the adoption of SHRM provides organizations with a significant technological foundation that distinguishes them from competitors.³⁹ RBV theory emphasizes that sustainable competitive advantage is derived from internal firm-specific resources and capabilities rather than external market conditions alone.⁹ In this study, while TOE explains the technological, organizational, and environmental determinants that influence the adoption of SHRM, RBV extends this explanation by focusing on how such adoption translates into superior organizational performance through the effective deployment of strategic resources and capabilities.

Conceptual framework

The conceptual model for this study is anchored in the Technology Organization Environment (TOE) framework and Resource-Based View (RBV) theory, which integrates perspectives from technology acceptance theories and organizational behavior to comprehensively examine technology adoption within organizations.³⁴ Strohmeier⁹ stated that technological, organizational, and environmental issues can guide the adoption of SHRM, which shapes the performance of the organization.¹⁸ In this study, the TOE framework identifies technological, organizational, and environmental factors influencing SHRM adoption, while the Resource-Based View (RBV) explains how these factors translate into firm-specific resources and capabilities. Specifically, the technological context reflects the firm’s digital infrastructure and technological capabilities; the organizational context corresponds to human capital, managerial competence, and organizational culture; and the environmental context acts as an external driver shaping the effective configuration and deployment of these resources. According to this model, SHRM adoption functions as a mediator between the different factors and the company’s performance, as offered in Figure 1.

Figure 1.

Conceptual model. Source: Author’s work.

Hypothesis development

Based on the conceptual model, a few hypotheses are made to look at how all the parts are related to each other. They are formed using the results of earlier studies on SHRM, the use of technology, and how organizations show results.

Technological Factors (TF) and the Adoption of Smart HRM (ASH)

Technological factors are the availability, compatibility, and preparedness of technological infrastructure in an organization.⁴⁰ Previous studies indicate that organizations that have developed IT infrastructure and more technological competencies are more prone to digital innovations, such as HR technologies.⁴¹ Regarding SHRM, technological preparedness allows organizations to incorporate technology in their HR operations, including the use of HR analytics, cloud-based HR solutions, and artificial intelligence.⁴²

According to TOE, technological preparedness lessens uncertainty and increases the perceived adoption feasibility.⁴³ In addition, RBV proposes that technological capabilities are strategic resources that enable the adoption of innovations and the creation of value.^44,45 Hence, those organizations that have more robust technological bases are more likely to embrace SHRM practices. The hypothesis is stated.

Technological Factors (TF) positively influence the adoption of Smart HRM (ASH).

Organizational Factors (OF) and the Adoption of Smart HRM (ASH)

Organizational factors include internal factors, like leadership support, organizational culture, human capital, and change readiness.⁴⁶ According to previous research, management support and an organizational culture that promotes technology adoption are the key factors.^47,48 Companies that encourage knowledge transfer and flexibility are better placed to incorporate emerging technologies in their business operations.⁴⁹

In the TOE framework, organizational readiness is the ability of the firm to adopt innovations both in terms of structure and behavior. RBV also highlights that internal resources, like managerial competence and employee skills, are also central resources that decide on effective adoption and use of technologies.⁵⁰ These considerations can empower organizations to successfully establish and maintain digital HR systems in the context of SHRM. Based on these insights, we hypothesize.

Organizational Factors (OF) positively influence the adoption of Smart HRM (ASH).

Environmental Factors (EF) and the Adoption of Smart HRM (ASH)

Environmental factors are external forces like competitive pressure, regulatory requirements, and institutional forces.⁵¹ TOE argues that organizations with dynamic and competitive environments are more prone to implement innovative technologies to remain competitive.^52,53 The regulatory support and industry trends also contribute to the promotion of digital transformation significantly.

Institutional theory indicates that organizations react to external forces, coercive, normative, and mimetic forces, to acquire legitimacy and to survive.^54,55 These pressures in developing economies could be through government policies, global integration of the markets, and the expectations of the stakeholders. Hence, environmental factors may have a strong impact on the decision to adopt SHRM. Thus, the hypothesis is.

EF positively influence the adoption of Smart HRM (ASH).

Technological Factors (TF) and Organizational Performance (OP)

The technological capabilities not only enable adoption but also have a direct positive effect on performance in an organization.⁵⁶ Application of sophisticated technologies will increase efficiency, decision-making, and operational effectiveness.⁵⁷ Digital tools in HRM help to make decisions based on data, automate HR processes, and manage employees better. Aligning advanced technologies with ethical AI principles allows for responsible, context-sensitive human and machine decision-making, ultimately boosting organizational performance and employee well-being.⁵⁸ Nevertheless, according to RBV, the availability of technology is not sufficient to improve performance unless it is properly applied.^44,59 Therefore, although technological factors can have a direct impact on performance, it is usually enhanced by appropriate implementation through SHRM systems.⁶⁰ So, we suggest the following hypothesis.

Technological Factors (TF) positively influence Organizational Performance (OP)

Organizational Factors (OF) and Organizational Performance (OP)

The organizational aspects, like leadership support, culture, and competencies of employees, are important in improving performance outcomes.^61,62 According to RBV, the firms gain a competitive advantage as a result of the effective use of their internal resources and capabilities.⁶³ A supportive organizational culture encourages innovation, increases employee engagement, and boosts productivity.⁶⁴ Within the framework of SHRM, firms that have strong internal potential can better use digital HR systems to achieve better performance outcomes, including efficiency, employee satisfaction, and overall productivity.^7,65 Therefore, we hypothesize.

Organizational Factors (OF) positively influence Organizational Performance (OP).

Environmental Factors (EF) and Organizational Performance (OP)

Indirectly, environmental factors can affect organizational performance by determining strategic choices and the adoption of innovations.⁶⁶ The intensity of competition and regulatory demands might force organizations to be more efficient and use best practices.⁶⁷ Nonetheless, the immediate influence of environmental conditions on performance is usually constrained and mediated by internal capabilities and technology uptake.⁶⁸ In this way, although the environmental factors may have a certain direct impact, the main impact on the performance is likely to be mediated by the adoption of SHRM. Because of this, a hypothesis is formed as follows.

Environmental Factors (EF) positively influence Organizational Performance (OP).

Adoption of Smart HRM (ASH) and Organizational Performance (OP)

Implementing SHRM systems allows organizations to increase HR efficiency, decision-making, and employee engagement.⁶⁹ Previous studies show that digital HR practices have a greater impact on organizational performance as they facilitate the simplification of HR processes and allow the implementation of data-driven strategies.⁷⁰ SHRM, as an RBV perspective, is a strategic capability that converts technological and organizational resources into performance outcomes.⁷¹ Hence, it is probable that organizations that properly implement SHRM will have enhanced performance. Therefore, we hypothesize that:

Adoption of Smart HRM (ASH) positively influences Organizational Performance (OP).

Mediation hypotheses

The present study integrates the Technology–Organization–Environment (TOE) framework with the Resource-Based View (RBV) theory to explain not only the determinants of Smart Human Resource Management (SHRM) adoption but also the mechanism through which such determinants influence organizational performance. While the TOE framework identifies the technological, organizational, and environmental conditions that facilitate innovation adoption,²¹ it does not sufficiently explain how these antecedents are transformed into tangible organizational outcomes. To address this limitation, RBV theory provides a complementary explanation by emphasizing that competitive advantage and superior organizational performance emerge when firms effectively deploy valuable, rare, inimitable, and non-substitutable resources and capabilities.³⁵

Within this integrated perspective, SHRM adoption functions as a strategic organizational capability that converts technological readiness, organizational preparedness, and environmental responsiveness into performance-enhancing outcomes. Merely possessing technological infrastructure or operating within supportive external environments does not automatically improve organizational performance. Instead, organizations must effectively integrate digital HR technologies into their operational and strategic HR processes to generate value.^72,9 SHRM systems facilitate this transformation by enabling data-driven decision-making, automation of HR activities, employee analytics, talent optimization, and strategic workforce planning, all of which contribute to organizational efficiency and long-term competitiveness.^7,60

From the RBV perspective, SHRM adoption represents an intangible organizational capability that enhances the organization capability to develop internal resources more effectively. Technological factors such as IT infrastructure, AI capability, and digital competence become strategically valuable only when embedded within HR processes through SHRM implementation. Similarly, organizational factors including leadership support, knowledge-sharing culture, and employee readiness improve organizational performance when they facilitate successful SHRM integration and utilization. Environmental pressures, such as competitive intensity, institutional expectations, and regulatory changes, may encourage organizations to adopt SHRM practices; however, their influence on performance is likely to occur indirectly through the organization’s ability to internalize and operationalize SHRM systems.⁷³

The mediating role of SHRM adoption is predominantly significant in developing economies such as Bangladesh, where infrastructural limitations, resource constraints, and digital skill gaps often prevent organizations from translating technological investments directly into performance gains. In such contexts, SHRM adoption acts as a capability-building mechanism that bridges the gap between resource availability and effective organizational outcomes. Prior studies on digital transformation and HR technology adoption similarly argue that organizational performance improvements are achieved not through technology possession alone, but through the effective assimilation and strategic utilization of technological capabilities within organizational processes.^16,18

Accordingly, this study proposes that SHRM adoption mediates the relationships between TOE factors and organizational performance by transforming organizational resources and contextual conditions into strategic capabilities that enhance efficiency, employee engagement, decision quality, and overall firm performance. Therefore, the following mediation hypotheses are proposed.

SHRM adoption (ASH) mediates association between Technological Factors (TF) and Organizational Performance (OP).

SHRM adoption (ASH) mediates association between Organizational Factors (OF) and OP.

H10

SHRM adoption (ASH) mediates association between Environmental Factors (EF) and OP.

Methodology

Construct measurement

The research measurement items were based on the established literature, which guarantees their content validity and relevance. Some small changes were made to fit the items into the particular environment of SHRM implementation in Bangladesh. Table 1 shows the entire list of constructs, items, and sources.

Table 1.

Measurement constructs of the study.

Constructs	Items	Reference
Technological Factor (TF)	TF-1: I enjoy using technology.	34
	TF-2: I would be a better learner knowing to use technology.
	TF-3: I am very confident by working with technology.
	TF-4: I am learning more about using technology.
Organizational Factor (OF)	OF-1: Members share their knowledge within the organization.	34,74
	OF-2: The skills and knowledge of members are used to achieve the organization’s mission.
	OF-3: Free flow of information is allowed within the organization.
	OF-4: Members in my organization are willing to accept changes.
Environmental Factor (EF)	EF-1: Environmental issues are overrated and do not concern me.	34
	EF-2: Environmentally damaging industries don’t make me angry or frustrated.
	EF-3: Products that pollute the environment during manufacturing or consumption should be taxed.
	EF-4: All public sectors should demand environmental and conservation courses for all pupils.
Adoption of Smart HRM (ASH)	ASH-1: I would find the smart HRM system services easy to use.	60
	ASH-2: Using the smart HRM system increases my productivity.
	ASH-3: If I use the smart HRM system, I will increase my chances of getting a raise.
	ASH-4: It is easy for me to become skillful at using the smart HRM system.
Organizational Performance (OP)	OP-1: From your perception, the organizational Performance is efficient.	60
	OP-2: From your perception, employees feel very committed to the organizational performance.
	OP-3: In your opinion, the productivity of employees is much higher than that of organizational performance.
	OP-4: From your perception, work costs per employee are well below the organizational performance.

This paper uses the Technology–Organization–Environment (TOE) framework to model the factors of SHRM adoption. Though the constructs of TOE are of an organizational-level nature, previous studies in the information systems and management have broadly operationalized them with perceptual measures provided by important organizational informants.^75,76 This method is especially suitable in instances where the respondents, who include HR managers and professionals, have first-hand experience with the processes in the organization, the technological capacity, and external forces that contribute to adoption decisions.⁷⁷

In this study, the respondents are the HR professionals and managers who directly make the HR decisions and implement the technology. Thus, their perceptions are viewed as valid proxies of organizational-level conditions, which aligns with previous studies based on TOE.^32,16 Particularly, the technological variables are quantified using the perceived technological familiarity, confidence, and learning orientation of the respondents. Although these indicators are at the individual level, they capture organizational technological readiness by human capability, which is a critical source of digital transformation.^72,78 Modern extensions to TOE have highlighted that technological competence and user preparedness are part and parcel of adoption preparedness, especially in service-based and knowledge-rich fields.

The organizational factors embrace internal attributes like knowledge sharing, organizational culture, and openness to change that resonate with the structural and behavioral attributes of the TOE organizational environment.⁷⁹ Such internal resources are also in line with the Resource-Based View (RBV) that firm-specific resources and competencies are the drivers of competitive advantage.³⁵

The environmental factors are operationalized by the perceptions of the respondents to the wider societal and institutional factors. Even though other items mirror the sustainability consciousness, they are understood as normative and institutional pressures, which constitute a vital component of the environmental setting in the TOE-based and institutional theory research.^80,78 Such pressures can have an indirect effect on the organizational strategies in developing economies via regulatory expectations, social norms, and the demands of the stakeholders.

Each of the constructs was measured on a 5-point Likert scale with a range of strongly disagree to strongly agree. The model of measurement was tested on reliability and validity (factor loadings, Cronbach’s alpha, and discriminant validity (HTMT)) and achieved the recommended levels.^81,82 These results suggest that the constructs exhibit sufficient internal consistency and empirical validity, and it validates the suitability of the perceptual measure method.

Population and sampling

This study adopts a purposive sampling technique to ensure the presence of respondents with relevant expertise and direct involvement in Smart Human Resource Management (SHRM) adoption. The population of this research comprises managers of different levels employed in different organizations in Bangladesh. These people were specifically targeted because they play critical roles in HR decision-making, technology implementation, and strategic alignment within organizations. Their positions provide them with comprehensive insights into both the strategic and operational dimensions of SHRM adoption. Given that SHRM adoption is examined at the organizational level, the use of these respondents as key informants is appropriate and consistent with prior research in HRM and technology adoption studies.^83,60

This study employed structural equation modeling (SEM) to investigate the relationships between latent variables, as outlined by Hair et al.⁸¹ The literature on sample size determination for various types of data analytics reveals a significant divergence of viewpoints.⁸⁴ A sample size of 200 is usually considered adequate, while a sample size of 300 is deemed good enough for conducting data analysis by applying structural equation modeling (SEM).^85,19 Hair et al.⁸¹ suggested a sample size of 200 for examining a model using SEM. Mandeville⁸⁶ opined that for multivariate research, the sample size should be at least ten times the number of items within the study’s constructs. This research encompasses a total of 20 constructs, and ten times the constructs become 200. Consequently, the sample size must be at least 200 or above. The sample of this study is 220, which is justified based on the above-stated guidelines.^87,88

Data collection

A total of 338 questionnaires were administered to potential participants via an online survey link sent to their institutional email addresses, with a specified submission deadline. Non-respondents received one reminder email within the prescribed response period. Data collection occurred between 15 September 2024 and 15 December 2024. The study achieved a response rate of 65%, yielding 220 completed questionnaires. As noted in the population and sampling section, a minimum sample size of 200 was required; the obtained sample of 220 therefore meets and slightly exceeds this threshold and is appropriate for structural equation modeling (SEM) analyses. All 220 returned questionnaires were retained for subsequent statistical analysis. Participation was voluntary and respondents received no financial or other incentives.

Analytical method

This study performed descriptive statistical analyses using SPSS version 28.0. To evaluate and validate the proposed conceptual framework and the relationships among the constructs, structural equation modeling (SEM) was carried out in RStudio (version 4.3.3). SEM is a well-established method for testing hypotheses against empirical data,⁸⁹ and RStudio provides robust tools for SEM estimation and diagnostics. Because the data were self-reported via a structured questionnaire, both procedural and statistical precautions were taken to reduce common method bias (CMB). Procedurally, respondents were informed that their responses would remain anonymous and confidential, and items were worded to minimize ambiguity and social-desirability effects. Statistically, Harman’s single-factor test was employed to evaluate the potential presence of CMB. Harman’s single-factor test revealed that the first factor explained only 47.72% of the total variance, well below the 50% threshold, indicating no significant bias on the basis of addressing the potential common method bias (CMB).⁷⁷ To execute the necessary statistical analyses, including the development of the research model and estimation of the measurement model, the authors first organized the data in Microsoft Excel (.csv).

Analysis

Demographic analysis

Table 2 presents the demographic details of the study’s respondents. The sample consists of 56.4% male and 43.6% female participants, with the majority (80.5%) falling within the age group of 26-30 years. Regarding educational qualifications, 54.5% of respondents hold a Bachelor’s Degree, followed by 33.6% with Higher Secondary Certificate (HSC). The respondents’ professional designations include 58.2% HR operational managers, 19.5% mid-level HR managers, 16% HR technology and analytics specialists, and 6.3% belongs in top-level HR managers. This demographic distribution provides valuable context for analyzing the adoption of SHRM, as it reflects a diverse HR-centric sample, enabling insights into the perspectives of those directly involved in HR management and related fields.

Table 2.

Demographic information.

Items	Description	Frequency	Percentage
Gender	Male	124	56.4
Gender	Female	96	43.6
Age	Under 18	5	2.3
	18–25	17	7.7
	26–30	177	80.5
	31 and above	21	9.5
Educational	SSC	6	2.7
	HSC	74	33.6
	Bachelor	120	54.5
	Master’s	15	6.8
	Others	5	2.3
Designation	Top-level HR Executives	14	6.3
	Mid-level HR managers	43	19.5
	HR Technology and Analytics Specialists	35	16
	HR operational managers	128	58.2

Research model bootstrap

The Research Model Bootstrap result is a key part of the analysis of the study on SHRM adoption and its effects on OP, particularly in developing economies, as represented in Figure 2.

Figure 2.

Research model bootstrap result. Source: Author’s contribution.

The study finds a strong, significant positive impact (β = 0.410, p = 0.000), which aligns with the literature that highlights the importance of technological infrastructure (AI, big data, cloud computing) for SHRM systems adoption. Organizational readiness is another strong driver (β = 0.369, p = 0.000), reinforcing the idea that leadership support and internal culture are key for the successful integration of SHRM systems. External factors (like market dynamics) have a weaker influence (β = 0.122, p = 0.037), reflecting that while they matter, internal factors, like technology and organizational readiness, are more influential. Although technology strongly influences SHRM adoption, its indirect impact on performance is negligible, underscoring that organizational readiness plays a more decisive role. This aligns with the study’s argument that internal factors (especially organizational readiness) are more pivotal than technological factors in driving performance.

Organizational factors have a significant mediating effect (p = 0.049), confirming that organizational readiness (including culture and leadership) is crucial in improving performance through SHRM adoption. The mediation of environmental factors through SHRM adoption is also insignificant (p = 0.104), indicating that while external pressures influence SHRM adoption, their indirect impact on organizational performance is limited. The adoption of SHRM (ASH → OP) has a significant positive outcome (β = 0.156, p = 0.042), indicating that implementing SHRM systems enhances organizational performance through the improvements in recruitment, employee engagement, and HR productivity.

Model factor loading

Table 3 presents the factor loadings for the constructs used in the study, which represent how well the individual items (or indicators) align with the constructs they are meant to measure. Factor loadings provide an indication of the reliability and relevance of each item in representing the respective construct. The four items measuring Technological Factors (TF) are highly reliable, as their factor loadings are all above 0.8, indicating that these items strongly represent the technological readiness construct. The four items used to measure Organizational Factors (OF) also show strong loadings, all well above 0.8 in Table 3. This demonstrates that leadership, organizational culture, and internal readiness are highly significant in the study’s model.

Table 3.

Model factor loading.

	TF	OF	EF	ASH	OP
TF1	0.892
TF2	0.918
TF3	0.844
TF4	0.898
OF1		0.875
OF2		0.851
OF3		0.845
OF4		0.814
EF1			0.507
EF2			0.481
EF3			0.847
EF4			0.864
ASH1				0.864
ASH2				0.904
ASH3				0.839
ASH4				0.806
OP1					0.827
OP2					0.817
OP3					0.810
OP4					0.782

The items of Environmental Factors (EF) have more diverse loadings, although they are relatively consistent. Loadings above 0.40–0.50 are frequently retained in exploratory or early-stage research in developing-country contexts or when using adapted scales, provided other validity metrics (AVE ≥0.50 overall for the construct, CR >0.70, and theoretical relevance) are satisfactory.⁹⁰ A loading of 0.48 explains approximately 23% of the indicator’s variance, which contributes meaningfully when balanced against content validity. The items were retained because they captured relevant normative and institutional aspects of the environmental context in a developing economy (Bangladesh), aligning with prior TOE applications (e.g., Refs. 34, 91).

Mediation analysis

The mediating effects of TF, OF, and EF on OP as a result of the adoption of SHRM are examined in Table 4. The findings indicate that the indirect effect of TF on OP through SHRM adoption is negligible (p = 0.057), and thus, technological readiness does not contribute directly to the performance, although it is vital in SHRM adoption. In its turn, OF greatly impacts OP on behalf of SHRM adoption (p = 0.049), underlining the role of leadership and organizational culture as the driving factor of the performance outcome improvement. Table 4 reveals that the indirect effect of the EF is insignificant (p = 0.104), implying that external pressure has a minor influence on the performance of an organization by using SHRM adoption.

Table 4.

Mediation Result after Bootstrap: (Indirect influences).

Mediation/Name	“TF” through “ASH,” to “OP”		“OF,” through “ASH,” to “OP”		EF,” through “ASH,” to “OP
Original Est.	0.064	Original Est.	0.058	Original Est.	0.019
Bootstrap Mean	0.062	Bootstrap Mean	0.055	Bootstrap Mean	0.018
Bootstrap SD	0.040	Bootstrap SD	0.035	Bootstrap SD	0.015
T Stat.	1.588	T Stat.	1.661	T Stat.	1.264
2.5% CI	−0.011	2.5% CI	−0.011	2.5% CI	−0.007
97.5% CI	0.146	97.5% CI	0.124	97.5% CI	0.052
p value	0.057	p value	0.049	p value	0.104
Hypothesis	Insignificant	Hypothesis	Significant	Hypothesis	Insignificant

Model paths

Table 5 provides path coefficients and the value of R² for the association between the constructs used in the research model of the study. This means that Technological Factors are considerably significant positive factors of adoption of SHRM systems. The R² SHRM adoption of 0.665 indicates that technology factors contribute a large percentage of variance of SHRM adoption, and hence, technological readiness is important towards the successful implementation of SHRM. SHRM adoption is also affected in a positive way by organizational issues, such as leadership and organizational culture. The R² value of 0.661 also shows that organizational-level factors act as great determinants of the variance in SHRM adoption, which means that the internal preparedness plays the key role in the successful implementation of SHRM systems.

Table 5.

Model paths.

	ASH	OP
R²	0.665	0.575
Adj R²	0.661	0.567
TF	0.410	0.140
OF	0.369	0.436
EF	0.122	0.106
ASH	NA	0.156

The environmental set of factors, including market pressure and industry trends, demonstrates a less significant impact on SHRM adoption than the related factors of the technological and organizational sets. SHRM adoption positively affects organizational performance. An R² value of 0.575 indicates that SHRM adoption explains a significant proportion of the variance in organizational performance, suggesting that successful SHRM adoption contributes directly to improved performance outcomes.

HTMT analysis

One measure in determining the discriminant validity of constructs in a structural equation model (SEM) is the Heterotrait-Monotrait Ratio (HTMT). Although HTMT values below 0.85 are generally preferred as they clearly suggest that the constructs are distinct, higher values do not automatically indicate a lack of discriminant validity (Henseler et al., 2015). Following the precedent established by Islam et al.,⁹⁰ who reported an HTMT value of 0.9227 between the constructs “Management System” and “Information Security Training,” we adopt a more nuanced interpretation. Islam et al.⁹⁰ demonstrated that an HTMT value as high as 0.9227, while indicating a strong correlation, remained acceptable because it stayed below the liberal threshold of 1.0 and was supported by additional validity criteria (Table 6).

Table 6.

HTMT analysis.

	TF	OF	EF	ASH
TF
OF	0.819
EF	0.694	0.798
ASH	0.845	0.862	0.686
OP	0.742	0.859	0.775	0.771

TF and OF (0.819), TF and ASH (0.845), OF and ASH (0.862), and OF and OP (0.859) all have values above 0.85, suggesting some level of overlap between these constructs. However, they remain within an acceptable range (close to 0.85), meaning that while they are closely related, they are still distinct enough to measure separately. EF and other constructs show HTMT values below 0.85 (EF and ASH: 0.686, EF and OP: 0.775), indicating that environmental factors are sufficiently distinct from other constructs in the model. ASH and OP (0.771) are also within an acceptable range, suggesting that SHRM adoption and organizational performance are related but distinct constructs.

Constructs Reliability

Figure 3 reflects the reliability of the construct employed in the study regarding Smart Human Resource Management (SHRM) adoption and its effects on organizational performance (OP). The reliability of the construct is identified with Factor Loadings (FL) and Cronbach’s Alpha values of each construct. Internal consistency is normally measured by the Cronbach alpha of each construct, and values greater than 0.7 indicate that the concepts in each construct reliably measure the same underlying concept in Figure 3. All the constructs using Cronbach’s Alpha criteria are greater than 0.7, which ensures that the reliability of constructs in the model is supported.

Figure 3.

Constructs reliability. Source: Author’s work.

Table 7 shows the values of the Variance Inflation Factor (VIF) to check the collinearity in a model, which can help to decide whether the high correlations between the predictor variables (constructs) have occurred or not. When the VIF is greater than 5 or 10, then it is indicative of problematic multicollinearity, and the estimates may not be reliable. All VIF values are below 5, suggesting that the collinearity between the antecedents (TF, OF, and EF) to SHRM Adoption (ASH) is not problematic. The predictor variables do not exhibit high correlations, which ensures that each antecedent provides unique explanatory power to the model without redundant information.

Table 7.

Model VIF antecedents (collinearity) to ASH.

	x		x
TF	2.308	TF	2.810
OF	2.629	OF	3.036
EF	2.071	EF	2.115
		ASH	2.989

Model F square

The f² values highlight that Technological Factors and Organizational Factors are moderately influential in driving Organizational Performance, presented in Table 8, but the impact of SHRM Adoption on performance is relatively small. Environmental Factors have minimal influence on SHRM adoption and organizational performance, emphasizing the greater importance of internal organizational and technological readiness.

Table 8.

Model F-Square.

	ASH	OP
TF	0.218	0.015
OF	0.155	0.147
EF	0.021	0.011
ASH	0.000	0.020
OP	0.000	0.000

Model constructs correlations

Figure 4 illustrates the correlations between the model’s constructs TF, OF, and ASH (above 0.7), suggesting that technological readiness and organizational culture are strongly related to SHRM adoption. Correlations with OP show that both technological and organizational factors have a significant impact on performance, while environmental factors display a weaker correlation, indicating their limited effect on performance compared to internal factors.

Figure 4.

Model constructs correlations. Source: Author’s work.

Model descriptive statistics constructs

Table 9 presents the descriptive statistics for the constructs in the model, including mean, standard deviation, skewness, and kurtosis. TF and EF show moderate central tendencies with a negative skew, indicating responses are concentrated towards the lower end of the scale. OF has a low mean and slightly negative skew, suggesting responses are mostly clustered at lower values. ASH has a mean of approximately zero and a large degree of negative skew and low kurtosis, which shows that the distribution is distorted much and implies that SHRM adoption is in its initial phases in Table 9. OP also records rather negative values of skewness on the one hand and moderate kurtosis on the other hand which means responses are predominantly low with some out-of-the-ordinary values of higher performance. All in all, the constructs are of medium variability with skewness to low responses, implying a comparatively skewed distribution.

Table 9.

Model descriptive statistics constructs.

Items	No.	Median	Min	Max	Std.Dev.	Kurtosis	Skewness
TF	1.000	0.310	−3.603	0.870	1.000	5.948	−1.762
OF	2.000	0.071	−3.543	1.276	1.000	3.903	−0.969
EF	3.000	0.326	−3.055	1.463	1.000	3.825	−0.972
ASH	4.000	−0.014	−3.595	1.179	1.000	3.940	−1.052
OP	5.000	0.236	−3.389	1.444	1.000	3.042	−0.548

Model descriptive statistics items

Table 10 provides the descriptive statistics for the individual items used to measure the constructs in the model. The mean values for most items range between 3.5 and 4.3, suggesting that responses are generally skewed towards the higher end of the scale. Standard deviations vary between 0.93 and 1.08, indicating moderate variability in responses. Items measuring TF, like TF2 and TF4, show slightly higher mean values (4.28 and 4.37), while EF1 and EF2 have lower means (2.61 and 2.63), reflecting a concentration of responses towards the lower end. The skewness for most items is negative, indicating that responses are generally clustered toward the higher end, with some items showing positive kurtosis, suggesting moderate concentration around the mean, except for a few, like EF1, which has a more even distribution in Table 10.

Table 10.

Model descriptive statistics items.

	No.	Mean	Median	Std.Dev.	Kurtosis	Skewness
TF1	1.000	4.144	4.000	1.073	5.046	−1.539
TF2	2.000	4.288	5.000	0.967	4.909	−1.535
TF3	3.000	4.065	4.000	0.993	4.263	−1.192
TF4	4.000	4.367	5.000	1.000	6.488	−1.937
OF1	5.000	3.856	4.000	1.042	3.510	−0.925
OF2	6.000	4.051	4.000	0.923	4.439	−1.137
OF3	7.000	3.874	4.000	1.008	3.431	−0.843
OF4	8.000	3.967	4.000	0.954	3.758	−0.938
EF1	9.000	2.605	2.000	1.436	1.645	0.259
EF2	10.000	2.628	2.000	1.501	1.613	0.318
EF3	11.000	4.051	4.000	1.077	3.883	−1.203
EF4	12.000	4.042	4.000	1.108	3.876	−1.220
ASH1	13.000	3.898	4.000	1.036	3.148	−0.830
ASH2	14.000	4.093	4.000	0.948	4.719	−1.276
ASH3	15.000	3.977	4.000	1.007	4.334	−1.193
ASH4	16.000	4.074	4.000	0.934	3.967	−1.044
OP1	17.000	3.851	4.000	0.975	3.755	−0.914
OP2	18.000	3.833	4.000	1.005	3.401	−0.825
OP3	19.000	3.805	4.000	1.059	2.989	−0.694
OP4	20.000	3.716	4.000	1.058	2.732	−0.577

Hypothesis testing

Table 11 presents the results of hypothesis testing in the model, showing the original estimates, bootstrap mean, bootstrap standard deviation, t-statistics, and p-values for each relationship. The path from Technological Factors (TF) to SHRM Adoption (ASH) has an original estimate of 0.410 and is significant (p = 0.000), indicating that technological factors positively influence SHRM adoption. Similarly, the path from Organizational Factors (OF) to ASH has a significant estimate of 0.369 (p = 0.000), supporting the importance of organizational readiness, leadership support, and culture for SHRM adoption. The path from Environmental Factors (EF) to ASH is also significant (β = 0.122, p = 0.037), though with a weaker impact compared to internal factors. The direct effect of TF on Organizational Performance (OP) is significant (β = 0.204, p = 0.032), highlighting the role of technological factors in improving performance in Table 11.

Table 11.

Hypothesis testing.

	Original Est.	Bootstrap Mean	Bootstrap SD	T Stat.	2.5% CI	97.5% CI	p values	Hypothesis
TF → ASH	0.410	0.405	0.065	6.276	0.273	0.529	0	Significant
TF → OP	0.204	0.199	0.069	2.949	0.062	0.338	0.032	Significant
OF → ASH	0.369	0.369	0.063	5.842	0.251	0.498	0	Significant
OF → OP	0.494	0.497	0.071	6.965	0.355	0.632	0	Significant
EF → ASH	0.122	0.127	0.068	1.800	−0.004	0.262	0.037	Significant
EF → OP	0.125	0.129	0.076	1.650	−0.024	0.275	0.08	Insignificant
ASH → OP	0.156	0.150	0.090	1.731	−0.030	0.323	0.042	Significant

The mediation effect of SHRM Adoption (ASH) on the relationship between TF and OP is insignificant (p = 0.057), suggesting that while TF influences SHRM adoption, its impact on performance is weak. Conversely, the mediation effect of Organizational Factors (OF) on performance via SHRM adoption is significant (p = 0.049), indicating that internal organizational readiness plays a critical role in enhancing performance through SHRM adoption. The mediation effect of EF is insignificant (p = 0.104), pointing to the limited impact of external pressures on performance through SHRM adoption. The direct path from SHRM Adoption (ASH) to OP is significant (β = 0.156, p = 0.042), confirming that SHRM adoption positively impacts organizational performance, particularly in areas such as recruitment and employee engagement.

Discussion

This study examined the determinants of SHRM adoption and its impact on organizational performance in Bangladesh through the integrated lens of the Technology–Organization–Environment (TOE) framework and Resource-Based View (RBV) theory. The empirical findings reveal several important insights that extend beyond statistical relationships to illuminate the underlying mechanisms of technology adoption in resource-constrained settings.

Technological Factors (TF) emerged as the strongest predictor of SHRM adoption (β = 0.410, p < 0.001), followed by organizational factors (OF) (β = 0.369, p < 0.001), while environmental factors (EF) exerted a weaker but still significant influence (β = 0.122, p = 0.037). These results align with the TOE framework, which posits that internal technological infrastructure and organizational readiness are primary drivers of innovation adoption.^21;34 However, the relatively modest direct effect of TF on organizational performance (β = 0.204, p = 0.032) and the insignificant mediation through SHRM adoption (p = 0.057) suggest that mere availability of technology is insufficient in developing economies. Advanced tools such as AI, cloud computing, and HR analytics require complementary internal capabilities to generate value consistent with RBV’s emphasis on valuable, rare, and inimitable resources.^35;9

Organizational factors demonstrated both strong direct effects on performance (β = 0.494, p < 0.001) and significant mediation via SHRM adoption (p = 0.049). These findings underscore leadership support, knowledge-sharing culture, and change readiness as critical mechanisms that convert SHRM adoption into performance gains. In line with Kambur and Yildirim³⁸ and Al-Faouri et al.,⁷ strong internal readiness enables organizations to effectively deploy digital HR systems, fostering employee engagement, data-driven decision-making, and operational efficiency. In Bangladesh’s context, where infrastructural limitations and digital literacy gaps are prevalent, organizational preparedness acts as the pivotal bridge between technology and outcomes, reinforcing RBV’s assertion that firm-specific capabilities, rather than external resources alone, drive competitive advantage.

Environmental factors, while positively associated with SHRM adoption, showed no significant direct effect on performance (p = 0.08) and insignificant mediation (p = 0.104). This pattern supports Karman’s (2020)⁹² argument that external pressures (competitive, regulatory, or institutional) exert limited influence unless accompanied by robust internal resources. In developing economies, regulatory support and market pressures may encourage initial adoption but rarely translate into performance improvements without sufficient organizational absorptive capacity. This result diverges from some studies conducted in more mature contexts,⁵³ where environmental forces play a stronger role, highlighting the contextual contingency of TOE relationships.

The positive relationship between SHRM adoption and organizational performance (β = 0.156, p = 0.042) corroborates prior evidence that intelligent HR systems enhance efficiency, strategic decision-making, and workforce productivity.^60,70 However, the modest effect size indicates that successful SHRM implementation in resource-constrained environments demands deliberate integration with organizational strategy rather than technology deployment in isolation. Establishing a strong correlation between SHRM adoption and organizational performance supports earlier studies showing that sophisticated HRM practices improve organizational performance at different levels. Nonetheless, the fairly low effect suggests that integrating SHRM with organizational strategy is essential to achieving its maximum benefits.³⁸ This research also notes that implementation of SHRM practices remains a work in progress among many companies, based on the significant degree of asymmetry present in their adoption. Transformational HR management approaches can lead to improved organizational performance once key internal capabilities are developed.

Implications

This study has far-reaching results. Organizations that want to increase their performance with SHRM should put special attention on setting up proper technology and making sure their structures are arranged to use the system easily.

Theoretical implication

This study advances the TOE framework by demonstrating its applicability in a developing-economy context while highlighting the necessity of integrating RBV to explain the resource-conversion mechanism.⁹ By reconceptualizing SHRM as a capability-building mediator, the research addresses gaps for more nuanced models that move beyond adoption antecedents to performance outcomes. The dominance of organizational factors further enriches RBV by illustrating how internal human and managerial resources serve as the primary source of sustained advantage in emerging markets. Firms that focus on and oversee these resources are well prepared to use SHRM for better results. It also fits with the RBV approach, stating that using the right resources helps a business succeed when using technology. Also, this study confirms that the introduction of SHRM helps link technological, organizational, and environmental factors to better organizational performance.

Practical implications

From a managerial standpoint, the study points out that companies need to invest in their technology to make sure that HR processes work well. Tools, for example, artificial intelligence, big data analytics, and cloud-based platforms, should be used first to help HR work better and do things more efficiently.³⁸ For practitioners in Bangladesh and similar developing economies, the findings emphasize that investments in technology alone are inadequate. Managers should prioritize building organizational readiness through leadership commitment, change-management programs, and knowledge-sharing initiatives before and during SHRM implementation. Policymakers and industry associations can support adoption by strengthening regulatory frameworks and digital infrastructure, but ultimate success hinges on firms’ internal capabilities. Organizations that cultivate a supportive culture and skilled workforce are better positioned to realize performance gains in recruitment, employee engagement, and strategic HR decision-making. The study also shows that using SHRM in the workplace really does help a company to perform better. By enabling data-driven HR decisions and using them strategically, SHRM systems can contribute directly to organizational success. Thereby, SHRM promotes sustainable HRM by leveraging advanced technologies to improve workforce management, employee development, and sustainable organizational performance.^93,94

Limitations and future research

This study has some limitations, employing a cross-sectional design; therefore, future longitudinal studies could provide deeper insights into the relationship between the uptake of smart HRM and organizational performance. This study employs a purposive sampling technique, which, while enhancing the relevance and quality of the collected data by targeting knowledgeable respondents, may limit the generalizability of the findings beyond similar organizational contexts. As the sample selection is based on the researcher’s judgment, there is also a potential risk of sampling bias. Consequently, future studies can extend the findings to broader populations or different institutional settings. The research is based on perceptual scales of key informants, which might not be ideal in the assessment of organizational-level constructs of the TOE framework. It is advised that future research should utilize multi-level or objective measures in order to increase the convergence of constructs. We conducted this study in Bangladesh, a developing country, which suggests that the findings may be applicable to similar developing country contexts. However, future studies in underdeveloped and developed countries are recommended to deepen the understanding of SHRM and its implications in diverse economic settings. Although the sample size of the study meets the minimum criteria suggested by Mandeville⁸⁶ and Hair et al.,⁸⁴ future studies can extend the sample size to get more comprehensive insights. A mixed-methods approach involving diverse respondent groups could provide a better understanding of the situation. Future research may also refine the model by incorporating mediators, moderators, and control variables such as firm size and demographic factors like age, gender, and job position. Further exploration of employee-level outcomes, technological granularity, and the roles of digital capability, leadership, and regulatory environments would also enhance the field’s understanding of how SHRM practices can be optimized to drive sustainable organizational performance in diverse developing contexts.

Conclusion

The adoption of SHRM represents a transformational shift in how organizations manage human capital in the digital era. By leveraging advanced technologies, such as AI, cloud computing, and HR analytics, SHRM enhances operational efficiency, enables data-driven decision-making, and strengthens the strategic role of HR in driving business outcomes.⁶⁰ This study investigated the determinants of SHRM adoption and its impact on organizational performance in the context of a developing economy, Bangladesh. Grounded in an integrated TOE framework and RBV theory, the research examined technological, organizational, and environmental factors, while positioning SHRM adoption as a key mediating mechanism. The findings demonstrate that all three TOE factors positively influence SHRM adoption, with technological and organizational factors exerting the strongest effects. Crucially, SHRM adoption significantly enhances organizational performance, primarily when supported by strong organizational readiness and leadership commitment. Mediation analysis further reveals that organizational factors serve as the dominant pathway through which SHRM adoption translates into superior performance outcomes.

Theoretically, the research contributes a more nuanced, integrated model that explains not only the antecedents of SHRM adoption but also the mechanisms through which it generates performance gains. From a practical standpoint, the results offer actionable insights for managers and policymakers in developing nations. Organizations should prioritize building internal readiness through leadership development, cultural change, and knowledge-sharing mechanisms, alongside technological investments to maximize the benefits of digital HR transformation. Policymakers, in turn, can facilitate broader adoption by strengthening regulatory support and digital infrastructure. In conclusion, successful SHRM implementation in developing economies hinges less on technology itself than on the organizational capabilities that enable its effective deployment. This study provides a robust foundation for future research and offers clear pathways for organizations seeking sustainable competitive advantage through intelligent human resource management.

Footnotes

Acknowledgements

The authors liked to convey their profound appreciation to their supervisor (Dr Md. Rakibul Hoque, Professor, Department of Management Information Systems, University of Dhaka, Dhaka, Bangladesh) to their crucial direction, astute input, and unwavering support throughout the study process. He is recognized among the Top 2% of Scientists in the world by Elsevier in 2025. The authors expressed their sincere gratitude to all responders who contribute to this study. Their readiness to share experience and perspectives provided essential insight that enhance the conclusions of this research.

ORCID iDs

Afruza Haque

Mohammad Rakibul Islam Bhuiyan

Ethical considerations

This research was conducted following the highest ethical standards to ensure integrity, transparency, and academic honesty. We have received the formal ethical board approval from the Research Ethics Committee, Department of Management Information Systems (MIS), Begum Rokeya University, Rangpur, Bangladesh. The reference number of the approval ethical committee is BRUR/MIS/REC/2025/202.

Consent to participate

Moreover, informed consent was obtained from all participants, ensuring their voluntary participation and confidentiality. The authors declare that there are no conflicts of interest related to this study. The authors confirm their adherence to ethical research and publishing practices, ensuring that all contributions were properly acknowledged and that the study maintains academic integrity.

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

Data is shareable and available after reasonable request because of respondent privacy and security concern. However, data were collected only after informed consent had been obtained from all participants, guaranteeing voluntary participation and the protection of confidentiality. The authors declare that there are no conflicts of interest among respondents related to this study.*

References

Holopainen

Saunila

Rantala

, et al. Striving for sustainability performance through digital platforms and advanced collaboration in digital transformation. Bus Strat Environ 2026; 35(1): 665–680. https://doi.org/10.1002/bse.70201

Marler

Parry

. Human resource management, strategic involvement and e-HRM technology. Int J Hum Resour Manag 2016; 27(19): 2233–2253. https://doi.org/10.1080/09585192.2015.1091980

Kaur

Sharma

, The role of digital HRM in tech-enabled urbanism: navigating workforce challenges in smart cities, in tech-enabled urbanism and entrepreneurship for inclusive cities, Chand Sharma

Tyagi

Redhead

, et al., Eds., In Lecture Notes in Civil Engineering, vol. 779, Springer Nature Singapore, 2026, pp. 47–63. https://doi.org/10.1007/978-981-95-3873-7_3

Abu Orabi

Alahewat

AMM

Abualfalayeh

, et al. The interdisciplinary nature of AI and human resource management: a bibliometric analysis. Hum Syst Manag 2024; 43(6): 845–871. https://doi.org/10.3233/HSM-240054

Kasubi

Kisumbe

Nyabakora

. Mapping the knowledge base for the impact of artificial intelligence on human resources management: a bibliometric study. Sage Open 2025; 15(3): 21582440251377298. https://doi.org/10.1177/21582440251377298

Omoush

. Human–AI collaboration in HRM and employee-centric outcomes: evidence from E-supply chain management. Hum Syst Manag 2026; 45(3): 343–358. https://doi.org/10.1177/01672533251365119

Al-Faouri

Abu Huson

Aljawarneh

, et al. The role of smart human resource management in the relationship between technology application and innovation performance. Sustainability 2024; 16(11): 4747. https://doi.org/10.3390/su16114747

Sinha

Verma

Das

. E-HRM as a driver for frontline employee performance: a socio-technical model. Int J Sociol Soc Pol 2026: 1–21. https://doi.org/10.1108/IJSSP-07-2025-0463

Strohmeier

. Smart HRM – a Delphi study on the application and consequences of the internet of things in human resource management. Int J Hum Resour Manag 2020; 31(18): 2289–2318. https://doi.org/10.1080/09585192.2018.1443963

10.

Singh

Joshi

Dissanayake

, et al. Towards sustainable manufacturing in developing economies: a systems-based model linking industry 5.0, SCE, and green HRM. Sustainability 2026; 18(7): 3404. https://doi.org/10.3390/su18073404

11.

Ul-Anam

Hossain

Sohel

, et al. Digital governance and private-sector innovation: impacts on business growth and consumer behavior in the artificial intelligence based economy, 2026. https://doi.org/10.5281/ZENODO.20309811

12.

Conte

Siano

. Data-driven human resource and data-driven talent management in internal and recruitment communication strategies: an empirical survey on Italian firms and insights for European context. Corp Commun Int J 2023; 28(4): 618–637. https://doi.org/10.1108/CCIJ-02-2022-0012

13.

Siyi

Al-Samawi

. Effects of digital education on human resource development. Hum Syst Manag 2023; 42(6): 691–706. https://doi.org/10.3233/HSM-230111

14.

Popkova

Zhusueva

Abdyldaeva

, et al. Smart transport systems as an infrastructural mechanism for stimulating the foreign economic activity of developing countries. In: Smart Transport Systems and the Digital Economy Infrastructure, Shaumarov

, Ed., in Lecture Notes in Intelligent Transportation and Infrastructure. Springer Nature Switzerland, 2026, pp. 513–522. https://doi.org/10.1007/978-3-032-12181-3_51

15.

Mendy

Rahman

. Application of human resource management’s universal model: an examination of people versus institutions as barriers of internationalization for SMEs in a small developing country. Thunderbird Int Bus Rev 2019; 61(2): 363–374. https://doi.org/10.1002/tie.21985

16.

Gangwar

Date

Raoot

. Review on IT adoption: insights from recent technologies. J Enterprise Inf Manag 2014; 27(4): 488–502. https://doi.org/10.1108/JEIM-08-2012-0047

17.

Yousaf

Palazzo

. Synergetic legacy of organizational innovativeness, knowledge sharing, artificial intelligence adoption and big data analytic capability in human resource management. Int J Hum Resour Manag 2025; 36(8): 1407–1430. https://doi.org/10.1080/09585192.2025.2510546

18.

Jayawardena

Ahmad

Valeri

, et al. Technology acceptance antecedents in digital transformation in hospitality industry. Int J Hospit Manag 2023; 108: 103350. https://doi.org/10.1016/j.ijhm.2022.103350

19.

Bhuiyan

MRI

Akter

Amin

A-

, et al. The mediating effect of innovation capabilities, information quality and supply chain resilience in the relationship between Big Data Analytics Capability (BDAC) and healthcare performance. Sage Open 2025; 15(3): 21582440251362262. https://doi.org/10.1177/21582440251362262

20.

Kokkaew

Jokkaw

Peansupap

, et al. Impacts of human resource management and knowledge management on non-financial organizational performance: evidence of Thai infrastructure construction firms. Ain Shams Eng J 2022; 13(6): 101750. https://doi.org/10.1016/j.asej.2022.101750

21.

Drazin

. The processes of technological innovation: David A. Tansik book review editor Louis G. Tornatzky and Mitchell Fleischer. Lexington, MA: D.C. Heath & Company, 1990. 298 pages. £44.95. J Technol Tran 1991; 16(1): 45–46. https://doi.org/10.1007/BF02371446

22.

Quaosar

GMAA

Rahman

Uddin

, et al. Investigating the adoption of human resource information systems and its effects on organisational performance. South Asian J Hum. Resour Manag 2026: 23220937261448949. https://doi.org/10.1177/23220937261448949

23.

Bayo-Moriones

Erro-Garcés

Lera-López

. The role of technology, calculative and collaborative HRM and social trust in driving human resource analytics in European firms. Pers Rev 2026; 55(1): 1–26. https://doi.org/10.1108/PR-06-2024-0561

24.

Al Amin

Noh

Haque

, et al. Unveiling student adoption of generative AI in higher education: the roles of perceived humanness, personal innovativeness, and trust. Beijing Int. Rev. Educ 2026; 8(1): 114–136. https://doi.org/10.1177/25902547261421646

25.

Schlimm

Breuer

. Understanding the factors that drive AI adoption among sports consumers – an extended TAM perspective. Int J Sports Mark Spons 2026; 27(1): 174–198. https://doi.org/10.1108/IJSMS-05-2025-0225

26.

Sultana

Chowdhury

Haque

. Gravitating towards Fintech: a study on undergraduates using extended UTAUT model. Heliyon 2023; 9(10): e20731. https://doi.org/10.1108/IJSMS-05-2025-022510.1016/j.heliyon.2023.e20731

27.

Haque

Sultana

Kim

, et al. Integration of the UTAUT model in Mobile banking context: the mediating role of personal innovativeness and perceived value. Asia Pac J Inf Syst 2024; 34(3): 929–956. https://doi.org/10.1108/IJSMS-05-2025-0225

28.

Yan

Bai

Lin

, et al. Determinants of the public’s behavioral intention to adopt AI-Assisted lung cancer screening: an extended UTAUT model integrating trust and risk. Healthcare 2026; 14(7): 945. https://doi.org/10.3390/healthcare14070945

29.

Haque

Integrating UTAUT and TPB frameworks to examine digital wallet adoption: evidence from university students in a developing economy. DUET Journal 2025; 101: 1–11.

30.

Huynh

Khoa

. Artificial intelligence adoption intention in recruitment and selection: an integrated TOE-TAM framework for talent management, in applied artificial intelligence in business, Yaseen

, Ed., In: Studies in Systems, Decision and Control, vol. 597, Springer Nature Switzerland, 2025, pp. 709–722. https://doi.org/10.1007/978-3-031-90271-0_50

31.

Ristandi

Maarif

Affandi

, et al. Ecosystem-driven adoption of human resource analytics: institutional mechanisms and perceived organizational performance in Indonesia. Cogent Bus Manag 2026; 13(1): 2642549. https://doi.org/10.1080/23311975.2026.2642549

32.

Awa

Ukoha

Igwe

. Revisiting technology-organization-environment (T-O-E) theory for enriched applicability. Bottom Line 2017; 30(01): 2–22. https://doi.org/10.1108/BL-12-2016-0044

33.

Bruque

Moyano

. Organisational determinants of information technology adoption and implementation in SMEs: the case of family and cooperative firms. Technovation 2007; 27(5): 241–253. https://doi.org/10.1016/j.technovation.2006.12.003

34.

Malik

Chadhar

Vatanasakdakul

, et al. Factors affecting the organizational adoption of blockchain technology: extending the technology–organization–environment (TOE) framework in the Australian context. Sustainability 2021; 13(16): 9404. https://doi.org/10.3390/su13169404

35.

Barney

. Firm resources and sustained competitive advantage. J Manag 1991; 17(1): 99–120. https://doi.org/10.1177/014920639101700108

36.

Abdou

. Smarter technologies, innovation, and managerial capabilities driving hotel sustainability: the integration of resource-based view and dynamic capabilities perspective. Tour Hosp 2025; 6(5): 252. https://doi.org/10.3390/tourhosp6050252

37.

Hassan

SAZ

Eassa

. SHMIS: an integrated IoT context awareness framework for hotel management to enhance guest experience and operational efficiency. Inf Technol Tourism 2025; 27(3): 579–612. https://doi.org/10.1007/s40558-025-00316-4

38.

Kambur

Yildirim

. From traditional to smart human resources management. Int J Manpow 2023; 44(3): 422–452. https://doi.org/10.1108/IJM-10-2021-0622

39.

Liboni

Cezarino

Jabbour

CJC

, et al. Smart industry and the pathways to HRM 4.0: implications for SCM. Supply Chain Manag Int J 2019; 24(1): 124–146. https://doi.org/10.1108/SCM-03-2018-0150

40.

Byrd

. Measuring the flexibility of information technology infrastructure: exploratory analysis of a construct. J Manage Info Sys 2000; 17(1): 167–208.

41.

Nicolás-Agustín

Jiménez-Jiménez

Maeso-Fernandez

. The role of human resource practices in the implementation of digital transformation. Int J Manpow 2022; 43(2): 395–410. https://doi.org/10.1108/IJM-03-2021-0176

42.

Ongesa Nyamboga

. Enhancing strategic agility and real time decision-making in the technology sector: exploring the role of AI and e-HRM systems. F1000Research 2025; 14: 1238. https://doi.org/10.12688/f1000research.167708.1

43.

Agarwal

Prasad

. The antecedents and consequents of user perceptions in information technology adoption. Decis Support Syst 1998; 22(1): 15–29. https://doi.org/10.1016/S0167-9236(97)00006-7

44.

Ahn

Kim

K-S

Lee

K-H

. Technological capabilities, entrepreneurship and innovation of technology-based start-ups: the resource-based view. J Open Innov Technol Mark Complex 2022; 8(3): 156. https://doi.org/10.3390/joitmc8030156

45.

Lioukas

Reuer

Zollo

. Effects of information technology capabilities on strategic alliances: implications for the resource‐based view. J Manag Stud 2016; 53(2): 161–183. https://doi.org/10.1111/joms.12179

46.

Jones

Jimmieson

Griffiths

. The impact of organizational culture and reshaping capabilities on change implementation success: the mediating role of readiness for change. J Manag Stud 2005; 42(2): 361–386. https://doi.org/10.1111/j.1467-6486.2005.00500.x

47.

Park

Ribière

Schulte

. Critical attributes of organizational culture that promote knowledge management technology implementation success. J Knowl Manag 2004; 8(3): 106–117. https://doi.org/10.1108/13673270410541079

48.

Vadithe

Rajput

Kesari

, et al. Integrating green HRM practices for green organizational culture and organizational agility: a mediation analysis. Bus Strategy Dev 2026; 9(1): e70268. https://doi.org/10.1002/bsd2.70268

49.

Khasmafkan-Nezam

. Harnessing information system capabilities to enhance organizational agility: the interplay of green absorptive capacity, collaborative knowledge creation, and strategic flexibility. J Enterprise Inf Manag 2026; 39(2): 602–636. https://doi.org/10.1108/JEIM-02-2025-0080

50.

Umar

Khan

, et al. The roles of green intellectual capital, blockchain technology and green manufacturing: gateways to improved sustainability performance. J Intellect Cap 2026: 1–23. https://doi.org/10.1108/JIC-02-2025-0050

51.

Cicchini

Lombardi

Marasca

, et al. From corporate planning to performance: how institutional forces propel sustainability in European firms. Soc Responsib J 2026; 22(3): 529–550. https://doi.org/10.1108/SRJ-12-2024-0860

52.

Chittipaka

Kumar

Sivarajah

, et al. Blockchain technology for supply chains operating in emerging markets: an empirical examination of technology-organization-environment (TOE) framework. Ann Oper Res 2023; 327(1): 465–492. https://doi.org/10.1007/s10479-022-04801-5

53.

Nguyen

THL

. An extended technology-organization-environment (TOE) framework for online retailing utilization in digital transformation: empirical evidence from Vietnam. J Open Innov Technol Mark Complex 2022; 8(4): 200. https://doi.org/10.3390/joitmc8040200

54.

Bhuiyan

Rana

Baird

, et al. Strategic outcome of competitive advantage from corporate sustainability practices: institutional theory perspective from an emerging economy. Bus Strat Environ 2023; 32(7): 4217–4243. https://doi.org/10.1002/bse.3362

55.

Galleli

Amaral

. Bridging institutional theory and social and environmental efforts in management: a review and research agenda. J Manag 2026; 52(1): 42–93. https://doi.org/10.1177/01492063251322429

56.

Bag

Rahman

Gupta

, et al. Understanding and predicting the determinants of blockchain technology adoption and SMEs’ performance. Int J Logist Manag 2023; 34(6): 1781–1807. https://doi.org/10.1108/IJLM-01-2022-0017

57.

Al-Surmi

Bashiri

Koliousis

. AI based decision making: combining strategies to improve operational performance. Int J Prod Res 2022; 60(14): 4464–4486. https://doi.org/10.1080/00207543.2021.1966540

58.

Rana

Kumar

. AI in human resource management: an interdisciplinary review and bibliometric analysis using SPAR-4-SLR. Hum Syst Manag 2026; 45(2): 257–275. https://doi.org/10.1177/01672533251365116

59.

Samadhiya

Agrawal

. Total productive maintenance and sustainability performance: resource-based view perspective. Benchmark Int J 2024; 31(7): 2177–2196. https://doi.org/10.1108/BIJ-10-2022-0635

60.

Pillai

Srivastava

KBL

. Smart HRM 4.0 practices for organizational performance: the role of dynamic capabilities. Benchmark Int J 2024; 31(10): 3884–3908. https://doi.org/10.1108/BIJ-05-2023-0288

61.

Kim

Jung

H-S

. The effect of employee competency and organizational culture on employees’ perceived stress for better workplace. Int J Environ Res Public Health 2022; 19(8): 4428. https://doi.org/10.3390/ijerph19084428

62.

Mai

Ho Nguyen

. The impact of leadership competences, organizational learning and organizational innovation on business performance. Bus Process Manag J 2022; 28(5/6): 1391–1411. https://doi.org/10.1108/BPMJ-10-2021-0659

63.

Cuthbertson

Furseth

. Digital services and competitive advantage: strengthening the links between RBV, KBV, and innovation. J Bus Res 2022; 152: 168–176. https://doi.org/10.1016/j.jbusres.2022.07.030

64.

Oladimeji

Abdulkareem

Ishola

. Talent management, organizational culture and employee productivity: the moderating effect of employee involvement. J Hum Resour Manag - HR Adv Dev 2023; 2023(1): 43–56. https://doi.org/10.46287/DPKF9953

65.

Wang

Zhou

Zheng

. Linking digital HRM practices with HRM effectiveness: the moderate role of HRM capability maturity from the adaptive structuration perspective. Sustainability 2022; 14(2): 1003. https://doi.org/10.3390/su14021003

66.

Lutfi

Alqudah

Alrawad

, et al.

Green environmental management system to support environmental performance: what factors influence SMEs to adopt green innovations?

Sustainability 2023; 15(13): 10645. https://doi.org/10.3390/su151310645

67.

Wiredu

Yang

Sampene

, et al.

The effect of green supply chain management practices on corporate environmental performance: does supply chain competitive advantage matter?

Bus Strat Environ 2024; 33(3): 2578–2599. https://doi.org/10.1002/bse.3606

68.

Akhtar

Sohu

, et al. Unlocking green innovation and environmental performance: the mediated moderation of green absorptive capacity and green innovation climate. Environ Sci Pollut Res 2023; 31(3): 4547–4562. https://doi.org/10.1007/s11356-023-31403-w

69.

Alzeiby

Islam

Shaik

, et al. AI adoption in enterprises for enhanced strategic human resource management practices: benefiting the employee engagement and experience. J Enterprise Inf Manag 2025; 38(5): 1441–1464. https://doi.org/10.1108/JEIM-05-2024-0249

70.

Alrousan

AlOqaily

Tawalbeh

. Enhancing organizational effectiveness through digital HR transformation. J Posthumanism 2025; 5(5): 1463–1481. https://doi.org/10.63332/joph.v5i5.1519

71.

Kamoche

. Strategic human resource management within a resource‐capability view of the firm. J Manag Stud 1996; 33(2): 213–233. https://doi.org/10.1111/j.1467-6486.1996.tb00158.x

72.

Bharadwaj

. A resource-based perspective on information technology capability and firm performance: an empirical Investigation1. MIS Q 2000; 24(1): 169–196. https://doi.org/10.2307/3250983

73.

Alam

SMS

Islam

KMZ

. Examining adoption of electronic human resource management from the perspective of technology organization environment framework. IEEE Eng Manag Rev 2021; 49(4): 59–75. https://doi.org/10.1109/EMR.2021.3114034

74.

Zhang

Zeng

Liang

, et al. Understanding how organizational culture affects innovation performance: a management context perspective. Sustainability 2023; 15(8): 6644. https://doi.org/10.3390/su15086644

75.

Venkatraman

Grant

. Construct measurement in organizational strategy research: a critique and proposal. Acad Manag Rev 1986; 11(1): 71. https://doi.org/10.2307/258332

76.

Kumar

Stern

Anderson

. Conducting interorganizational research using key informants. Acad Manag J 1993; 36(6): 1633–1651. https://doi.org/10.2307/256824

77.

Podsakoff

MacKenzie

Lee

J-Y

, et al. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 2003; 88(5): 879–903. https://doi.org/10.1037/0021-9010.88.5.879

78.

Zhu

Kraemer

. The process of innovation assimilation by firms in different countries: a technology diffusion perspective on E-Business. Manag Sci 2006; 52(10): 1557–1576. https://doi.org/10.1287/mnsc.1050.0487

79.

Baker

, The technology–organization–environment framework, in information systems theory, Dwivedi

Wade

Schneberger

, Eds., In Integrated Series in Information Systems, vol. 28. Springer New York, 2012, pp. 231–245. https://doi.org/10.1007/978-1-4419-6108-2_12

80.

DiMaggio

Powell

. The iron cage revisited: institutional isomorphism and collective rationality in organizational fields. Am Sociol Rev 1983; 48(2): 147. https://doi.org/10.2307/2095101

81.

Hair

Hult

GTM

Ringle

, et al. Partial least squares structural equation modeling (PLS-SEM) using R: a workbook. In: Classroom Companion: Business. Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-80519-7

82.

Bhuiyan

MRI

Uddin

Sultana

, et al. Investigating ChatGPT methods in the higher education form a student engagement perspective: based on a descriptive-cross-sectional study. Sage Open 2026; 16(1): 21582440251413008. https://doi.org/10.1177/21582440251413008

83.

Ekhsan

Badrianto

Suwandi

, et al. Strategic human resource capabilities for digital transformation: linking talent, leadership, competence and readiness to technology adoption, SA J. Hum Resour Manag 2026; 24(0): a3327. https://doi.org/10.4102/SAJHRM.v24i0.3327

84.

Hair

Risher

Sarstedt

, et al. When to use and how to report the results of PLS-SEM. Eur Bus Rev 2019; 31(1): 2–24. https://doi.org/10.1108/EBR-11-2018-0203

85.

Markus

. Principles and practice of structural equation modeling by Rex B. Kline. Struct Equ Model Multidiscip J 2012; 19(3): 509–512. https://doi.org/10.1080/10705511.2012.687667

86.

Mandeville

Roscoe

. Fundamental research statistics for the behavioral sciences. J Am Stat Assoc 1971; 66(333): 224. https://doi.org/10.2307/2284880

87.

Anam

ASMT

Bhuiyan

MRI

Islam

, et al. Factors influencing university students’ adoption of online education platforms in Bangladesh: an extended TAM-based analysis. Educ Dev Psychol 2026: 1–17. https://doi.org/10.1080/20590776.2026.2619742

88.

Molla

Bhuiyan

MRI

Islam

, et al. Mediating role to banking performance for adopting digital bank using a technology organizational environment model. Digit Finance 2026; 8(1): 4. https://doi.org/10.1007/s42521-025-00168-x

89.

Götz

Liehr-Gobbers

Krafft

. Evaluation of structural equation models using the partial Least Squares (PLS) approach. In: Esposito Vinzi

Chin

Henseler

, et al. (eds). Handbook of Partial Least Squares. : Springer Berlin Heidelberg, 2010, pp. 691–711. https://doi.org/10.1007/978-3-540-32827-8_30

90.

Islam

Bhuiyan

MRI

Islam

, et al. Unlocking the mediating and moderating role of information security in information systems: a combined TAM, ISM, and HBM model. Hum Behav Emerg Technol 2026; 2026(1): 5593002. https://doi.org/10.1155/hbe2/5593002

91.

Ahmed

Streimikiene

Streimikis

, et al. A comparative analysis of multivariate approaches for data analysis in management sciences. EM Ekon Manag 2024; 27(1): 192–210. https://doi.org/10.15240/tul/001/2024-5-001

92.

Karman

. Flexibility, coping capacity and resilience of organizations: between synergy and support. J Organ Change Manag 2020; 33(5): 883–907. https://doi.org/10.1108/JOCM-10-2019-0305

93.

Trunk Širca

Riaz

Hamid

, et al. The interplay among employee resilience, sustainable HRM and work engagement. Hum Syst Manag 2024; 43(4): 573–587. https://doi.org/10.3233/HSM-230204

94.

Diep

TPT

Horváthová

. Human resource management, employee well-being, and performance: a systematic review. Hum Syst Manag 2026; 45(1): 89–108. https://doi.org/10.1177/01672533251355886