Cloud computing is changing the concept of operational business because it is a flexible and cost-effective way of delivering IT services. It can be based on parallel and distributed computing concepts that provide shared access to hardware, software, and potential data in computers or other (LD and Krishna, 2013)In general, sustainable cloud computing signifies cloud-enabled technologies and infrastructures catering to environmental, economic, and social sustainability concerns (Bristy et al., 2023). The central concept behind this approach is minimizing energy usage and optimizing resource allocation to achieve the lowest environmental footprints at higher scalability and efficiency levels of operations (Radhika et al., 2024).

This study, therefore, identifies these factors and develops an integrated adoption model for the successful implementation of sustainable cloud-based HRM in SMEs.

Zhao and Rabiei (2023) evaluated whether it would be feasible to implement a cloud-based HR payroll system within a business. They discovered that the necessary conditions for implementing cloud computing technology in the enterprise were desire, capability, and acceptance. To develop an optimization model for HRM issues, Liu et al. (2022) recommended using cloud edge computing and the Whale Optimization Algorithm. The findings demonstrate that the proposed model outperforms alternative metaheuristics in two distinct scenarios, increasing the amount of human resources provided while decreasing the allocation cost. To help SMEs manage their human resources challenges, Oduh et al. (2018) created and executed a preliminary version of a cloud-based application. A novel framework called Expert Cloud was created by Navimipour et al. (2015) to allow Cloud users to request human expertise and abilities without being aware of their location and to encourage the sharing of HR knowledge, experience, and skills. The findings showed that the Expert Cloud boosts HR performance within the company and increases customer satisfaction.

However, sustainable cloud computing encompasses both the traditional advantages of flexibility and cost-effectiveness and the environmental, economic, and social dimensions of sustainability. In the context of the environment, it involves using cloud computing to minimize power consumption through optimized server use and reduces paper waste by substituting it with digitization of workflow (Bharany et al., 2022). Economically, it allows SMEs to lower operational costs due to no upfront investments in hardware while scaling up the resources to meet demand (Bandara, 2024). Socially, it opens up a wide avenue toward inclusivity, enabling remote employment and providing equal opportunities for training and development through cloud-based enabling platforms (Yenugula et al., 2024). These features make sustainable cloud computing fundamentally different from conventional cloud models and make it a strategic enabler for SMEs in their ambitions to meet global sustainability objectives Johnson and Diman (2017) utilized the TOE model and found that small businesses are more likely to use cloud-based human resource information systems to support ongoing HR operations. Yao and Azma (2022) showed that while cloud privacy and security hurt HR productivity, other factors such as employee skills and knowledge, company financial situation, current IT structure, payment terms, and cloud accessibility have a positive impact on staff efficiency. In a separate study, Dong and Salwana (2022) conducted a study to investigate whether cloud-based HRM and supply chain management enhance the performance of multinational organizations. The findings supported the notion that cloud-based HRM and supply chain management are essential for improving these organizations' financial, marketing, and collaboration performance.

In recent years, researchers have begun to explore the adoption of green cloud computing by SMEs in emerging economies. For instance, Senyo et al. (2016) explored cloud computing adoption in SMEs in Ghana. The study findings indicated that relative advantage, top management support, and competitive pressure all significantly impact the adoption of cloud computing among SMEs. In a related study, Gupta et al. (2013) investigated cloud computing usage and its adoption by SMEs in Malaysia. Perceived benefit, technological readiness, and external pressure were significant antecedents to adopting cloud computing.

Alshamaila et al. (2013) studied SMEs' adoption of cloud computing in the Middle East. They established that although technological issues are relevant, environmental and organizational contexts are equally crucial. This certainly has implications for Bahrain, where cultural and regulatory reasons could impact any adoption.

Comparative studies show that SMEs in developing economies face similar challenges and drivers in adopting cloud computing technologies (Al-Shboul, 2019; Skafi et al., 2020; Khayer et al., 2021). These include technological readiness, perceived benefits, and security/privacy concerns. However, differences in infrastructure development, regulatory environments, and cultural attitudes toward technology ensure that variations lie. For example, Ghanaian SMEs might have to put up with poor internet connectivity (Dayour et al., 2023), Bahraini firms have the better infrastructure but might face varying regulatory obstacles (AlBenJasim et al., 2023). In the review of related literature, the usage of cloud computing in HRM research was found to be very popular among academics globally (Porkodi and Raman, 2024). Recently, such scope seems to need to be added in studies about sustainable cloud computing-based HRM in the SME environment in developing countries like Bahrain.

Although some studies investigate cloud computing adoption in SMEs, those investigating sustainable cloud-based HRM system contexts remain scant (Maqueira Marín et al., 2022; Ashrafuzzaman, 2024). To the best of the authors' knowledge, no prior study has addressed the issue of sustainable cloud computing-based HRM in an SME environment using the Technology-Organization-Environment (TOE) framework and individual context in developing nations such as Bahrain.

Adding to the list of recent studies from other developing economies, our research enriches the comparative element and broadens the understanding of factors influencing cloud adoption in diverse contexts. Demonstrating similarities and differences in Bahrain and other similar economies strengthens the study's theoretical framework. By incorporating these insights and focusing on the unique context of Bahraini SMEs, this research can inspire further research and contribute significantly to the field.

The rapid growth of cloud computing technology has significantly transformed HRM, particularly for SMEs. Cloud-based HRM systems offer unique benefits such as scalability, cost-effectiveness, and practical solutions, which are crucial for addressing the distinct challenges faced by SMEs (Marler and Fisher, 2013; Strohmeier, 2014). The cloud HRM system refers to various software applications running on remote servers to enable HR functions that range from recruitment, payroll management, performance tracking, and employee engagement (Ardebili et al., 2023). Since these systems are highly scalable and flexible, they can comfortably facilitate SMEs' adaptation to HR processes with zero investment in infrastructure (Herzallah and Ayyash, 2024). These systems are software applications hosted on remote servers and accessed via the Internet, requiring minimal upfront investment in hardware or infrastructure (Bondarouk and Brewster, 2016). Unlike traditional on-premise HRM systems, cloud solutions enable SMEs to become flexible and scalable; resources can be adjusted up or down depending on the organization's needs without an exponential cost increase (Kavanagh and Johnson, 2017). The systems are designed so that updates, which should be performed periodically, are done automatically by the service providers, ensuring that the latest functionality and security features are available for organizations without the need and headache of maintenance (Voermans and Van Veldhoven, 2007; Stone and Deadrick, 2015).

By systematizing human resources management in SMEs, cloud-based HRM applications empower owners and professionals with real-time tracking and automation features (Lee and Lee, 2024). For instance, they automate recruitment and onboarding, reducing time-to-hire and improving the candidate experience (Johnson et al., 2016). They efficiently track and manage applicants, enabling performance management and timely feedback in line with organizational objectives (Strohmeier, 2014). Payroll and benefits administration is also automated, minimizing errors and ensuring compliance with tax laws (Marler and Boudreau, 2017). This level of control and empowerment is a significant benefit of cloud-based HRM systems.

The capabilities of these systems include extensive data management and analytics methodologies (Namperumal et al., 2022). Cloud environments protect the security of employee information that is kept therein, thus making access to and managing such data easier (Awaysheh et al., 2021). Advanced analytics let SMEs identify trends in the workforce, informing strategic decisions in talent management and organizational development (Conte and Siano, 2023). These systems enhance compliance and security since they have built-in features that help SMEs check their adherence to labor laws, thus minimizing legal risk (Strohmeier, 2014).

Despite robust security measures, concerns related to data breaches and data privacy persist in cloud HRM systems (Du Plessis and De Wet Fourie, 2016). Features in HRM systems, such as self-service portals, also help employees access their personal information, leave applications, and benefit enrollment, leading to increased transparency and satisfaction (Martin and Reddington, 2010). The integrated communications within cloud-based HRM systems enable collaboration and connectedness among the workforce, which is helpful for SMEs in building an integrated organizational culture (Berglund, 2023). This reassurance and confidence in data management are significant benefits of cloud-based HRM systems.

Such adoption of cloud-based HRM systems contributes to the sustainability of SMEs in terms of environmental, economic, and social dimensions (Lopes de Sousa Jabbour et al., 2018). From an environmental perspective, these systems reduce paper usage by digitizing records and workflows, reducing waste, and aiding conservation accordingly (Sanjeev et al., 2024). The consolidation of server usage in cloud computing reduces energy consumption compared with individual on-premise systems, increasing energy efficiency and reducing carbon emissions associated with IT infrastructure (Thaqi et al., 2024). This point is supported by several authors, such as Yenugula et al. (2024) and Columbres and Victoriano (2024).

Economically, cloud-based HRM is cost-effective since it saves money that would be used in capital expenditures like hardware and maintenance, which SMEs can utilize in other areas of their core business activities (Zhao and Rabiei, 2023). It operationalizes efficiency in human resources through the automation of processes, thereby increasing productivity due to reduced administrative burdens (Fawehinmi et al., 2024). The scalability of cloud solutions offers support for sustainable business growth since it allows the operations of SMEs to expand without high additional costs (Natrajan et al., 2024).

Promoting social sustainability, cloud-based HRM systems offer virtual work access, enabling employees to work anytime and from anywhere (Porkodi and Raman, 2024). These systems also support talent development through online training and development modules, fostering continuous learning and career advancement. Moreover, they facilitate inclusive practices, expanding the talent pool and promoting diversity and inclusion within the organization (Lopes de Sousa Jabbour et al., 2018).

Finally, HRM systems based on cloud computing present SMEs with one of the most effective tools to enhance operational efficacy in furthering sustainability objectives.

Implementation of these systems enables SMEs to achieve competitive performance by promoting environmental stewardship, economic resilience, and social responsibility (Yenugula et al., 2024). Integrating a cloud-based HRM system into organizational strategies denotes its importance in developing a sustainable future in business and society in general.

The authors notice that the TOE framework of Tornatzky and Fleischer (1990) might summarize the assumed factors into a more excellent picture, adding a deep theoretical perspective to this study. More importantly, the framework was meant to link the information system innovation adoption decisions to contextual considerations to make it suitable for cloud computing as an innovative industry (Chau and Tam, 1997; Yaseen et al., 2023).

The TOE framework proposes three contextual factors: technological, organizational, and environmental decisions within SMEs. It involves relevant internal and external technologies to the firm. Traditionally, it encompasses such factors as relative advantage, complexity, compatibility, and technological readiness (Tornatzky and Fleischer, 1990; Oliveira and Martins, 2010). Initially, the TOE framework focuses on the technological factors of IT infrastructure characteristics and technological competence. In our study, we have also included relative advantage, complexity, and compatibility because, according to Rogers (2003), those are critical in the context of cloud computing adoption.

The organizational context is affected by the company's features, such as its size and the degree of bureaucratization, decentralization, and complexity of its management structure. It includes factors like top management support, organizational readiness, and firm size (Alshamaila et al., 2013; Rahayu and Day, 2015). These factors influence the firm's capacity to adopt and implement new technologies (Salah and Ayyash, 2024).

The environmental context refers to the external environment in which the firm operates and is composed of industry characteristics, market structure, competitors, suppliers, customers, and regulatory environment (Zhu et al., 2006; Rahayu and Day, 2015). Although the original TOE framework includes industry characteristics, market structure, and regulation, our study focused on competitive and trading partner pressure. Specifically, in the context of the adoption of sustainable cloud computing-based HRM by SMEs, these factors have been identified as the most immediate environmental factors (Low et al., 2011).

The TOE framework or other models have been used in previous studies on the adoption of information technology and information systems at the corporate level (Oliveira and Martins, 2010; Aboelmaged, 2014; Ali et al., 2015; Min et al., 2015; Alkhalil et al., 2017). Instead of emphasizing a particular viewpoint, the framework provides a comprehensive view of the many facets of an organization (Nkhoma et al., 2013). As a result, we decided to base our research model on the TOE framework. This choice was made after taking into account a variety of considerations. First of all, prior research has generally recognized the TOE as a trustworthy paradigm for analyzing the adoption of cloud computing (Oliveira et al., 2014; Tashkandi and Al-Jabri, 2015; Senyo et al., 2016; Al-Hujran et al., 2018; Asiaei and Rahim, 2019; Hiran and Henten, 2020). The TOE framework emphasizes the background of technology while also considering organizational and environmental factors.

The TOE model provides a valuable basis for this study's understanding of various factors that affect the adoption and implementation of green cloud computing in SMEs. The original TOE constructs already include a wide range of factors, and we have selected certain factors in each context that are most relevant to our research.

Thus, in this study relative advantage, complexity, and compatibility factors in the technological context, as they directly affect cloud computing technologies' perceived usefulness and ease of use. As Rogers (2003) puts it, the relative advantage is the degree to which an innovation is perceived as being better than the idea it supersedes- a significant factor in assessing new technologies such as sustainable cloud computing-based HRM systems. Complexity refers to the degree to which an innovation is perceived as complicated to understand and use; this might be a potential barrier in the adoption process. Compatibility represents how consistent the innovation is with the potential adopters' existing values, past experiences, and needs.

In this study, within an organizational context, we focused on top management support, technology readiness, and organization size. Top management support allows for adequate resources to be provided within the supportive atmosphere necessary for innovation. According to Khayer et al. (2020), top management support will provide the appropriate resources for innovation to develop and facilitate an enabling environment for innovation to take place. Technology readiness reflects the firm's infrastructural and human resource capabilities (Khayyam et al., 2024). This study considers organization size because larger firms are more able to adopt new technologies than smaller firms (Shahzad et al., 2024).

In the context of the environment, we have included competitive and trading partner pressures. Competitive pressure forces firms to adopt new technologies to be more competitive (Kwarteng et al., 2024). The pressure from trading partners is essential because SMEs often depend on their partners, and alignment with partners aids adoption (Benhayoun et al., 2021). Although the TOE framework does include other factors, such as industry characteristics, market structure, and regulations, we focused on the most immediately relevant factors for SMEs in Bahrain.

Justification of these factors and realization of the exclusion of others have captured the TOE framework so that its application suits the context of sustainable cloud computing-based HRM adoption among Bahraini SMEs. This kind of selective inclusion can allow for a more focused analysis of the most pertinent factors of such adoption in our context.

Individual-related factors will help in understanding the attitudes and perceptions of people and their capacity for behavior change about the acceptance of cloud computing-based sustainable HRM in SMEs. These might also benefit firms by helping them design more specifically tailored approaches to promoting adoption and addressing potential barriers. On the other hand, TOE does not disregard factors that relate to the personal characteristics of managers and employees (Ghobakhloo and Tang, 2013; Rahayu and Day, 2015). As a result, the individual context has been added to the original TOE framework, represented by the factor of managerial perceived innovativeness because it could play a significant role in SMEs. After all, decision-making is often within their centralized power (Al-Qirim, 2007).

Although we understood the wide array of factors present within the original TOE framework, we have identified and justified a certain number of those factors in each context, which will most suit our study on the adoption of sustainable cloud-based computing HRM in Bahrain's SMEs. The approach provides a theoretically sound and practically relevant research model that captures the essential elements driving the adoption decisions within the particular context of the study, with interests shared by our fellow researchers in the field of HRM and sustainable computing.

This study's technological environment includes compatibility, relative advantages, and complexity. The relative advantage significantly impacts whether new information system innovations are adopted. According to Rogers (2003) relative advantage refers to the extent to which a technological component is believed to help businesses more. The effect of relative advantage on the adoption of technology in firms has been investigated in detail in earlier research (Thong, 1999; Lee, 2004). Their findings found that when organizations understand an innovation's relative advantage, adoption is more likely to occur (Alshamaila et al., 2013). Berman et al. (2012) claim that for innovative tools to be adopted, they must be easy to manage and use. As cloud computing is a relatively new technology, some organizations may not have the necessary confidence or technological enablers, such as high-speed communication networks, to support it. Adoption times are consequently prolonged (Dwivedi and Mustafee, 2010). Second, complexity refers to how difficult it is for SMEs to understand and use cloud computing. Contrary to other aspects of the technology, complexity has been found to have a negative link with the likelihood of adoption despite being a substantial influence on the decision to accept a new technology (Gutierrez et al., 2015). Lastly, the level of perceived fit between the new technology and the organization's values and needs is referred to as compatibility (Rogers, 2003). Cloud computing, for example, allows companies to stay up-to-date with technology without disrupting their existing legacy IT systems (Sultan, 2011; Gupta et al., 2013). Consequently, factors such as complexity, compatibility, and relative advantage contribute to adopting sustainable cloud computing-based HRM in SMEs. Therefore, it is hypothesized that:

Hypothesis 1: Technological context has a significant impact on SME's adoption of cloud computing-based HRM.

In this study, the organizational context is defined as certain factors of an organization's structure that could affect the adoption of sustainable cloud computing-based HRM. The study focuses, in particular, on three organizational factors: top management support, technology readiness, and organization size, which are considered to be significant predictors of SMEs' adoption of sustainable cloud computing-based HRM. One aspect of the organizational context that this study highlighted as potentially influencing SMEs' adoption of sustainable cloud-based HRM is top management support. The decision of an organization to adopt new technology is heavily influenced by the backing of top management (Yigitbasioglu, 2015). A lack of leadership support decreases the chances of adopting an innovation like cloud computing. Hence, the degree of support SMEs receive from their senior executive manager has an important effect on the extent to which cloud technology is adopted. Top management's inability to see the potential advantages of cloud computing deters them from implementing it (Khayer et al., 2020). Technology readiness, on the other hand, refers to how much a firm's technical capabilities, systems, and infrastructure can support SMEs in their adoption of sustainable cloud computing (Zhu et al., 2006). Technology infrastructure and IT personnel are both essential components of technology readiness (Zhu et al., 2004) and both are crucial if SMEs want to integrate e-business into the value chain fully (Natario et al., 2010). The size of the firm is another element of organizational context that has been connected to e-commerce adoption by SMEs. This is a consequence of the connection between a firm's size and its ability to offer particular resources, including both financial and human resources (Rahayu and Day, 2015). As a firm grows, its capacity to provide specific resources rises, making adopting cloud computing more likely. Furthermore, SMEs with strong top management support, high technology readiness levels, sufficient resources, and infrastructure are more likely to embrace and effectively execute sustainable cloud-based HRM. Therefore, it is hypothesized that:

Hypothesis 2: Organizational Context has a significant impact on SMEs' adoption of cloud computing-based HRM.

The adoption of sustainable cloud computing-based HRM by SMEs is influenced by external influences including competitive and trading partner pressure. Competitive pressure is the degree of rivalry between businesses in the particular sector in which the company operates (Thong and Yap, 1995). Incentives to adopt pertinent new technologies include the competitive pressure a firm must deal with (Majumdar and Venkataraman, 1993; Alshamaila et al., 2013). Further, many SMEs rely on their business associates to complete IT design and implementation duties (Pan and Jang, 2008). According to reports, pressure from trading partners is a key driver of IT adoption (Chong et al., 2009; Amini et al., 2014). On the other hand, SMEs are more likely to embrace sustainable cloud computing-based HRM practices when they are up against the firm competition or work with partners who share their values in this area. If SMEs want to keep their competence and prosperous business relationships, they must stay abreast of market developments and their partners' expectations. Therefore, it is hypothesized that:

Hypothesis 3: Environment Context has a significant impact on SMEs' adoption of cloud computing-based HRM.

While the technological, organizational, and environmental contexts have been the primary focus of this study, we have also expanded our investigation to include the area of individual contexts, particularly managerial innovativeness, as a determinant factor for adopting sustainable cloud computing-based HRM systems in SMEs. This is supported by the argument that management actions often have a significant impact on strategic decisions made inside SMEs, as demonstrated by foundational publications like Cloete et al. (2002). Fink (1998) and other earlier studies have shown that small commercial firms with creative managers at the helm have a higher possibility of effectively implementing information systems. Similar to this, Al-Qirim (2007) showed how the chief executive officer's creativity had a big influence on how e-commerce technology was incorporated into the SME sector in New Zealand.

Using this fundamental research as our guide, we propose that the degree of managerial innovativeness may have a considerable impact on SMEs' tendency to adopt sustainable cloud computing-based HRM solutions. This builds on prior research by claiming that managers in the world of SMEs with a high level of innovativeness may be more receptive to evaluating, appreciating, and ultimately embracing disruptive technology such as sustainable cloud computing-based HRM systems. Such managers would probably be more aware of the long-term strategic advantages of this technology, such as cost-effectiveness, scalability, and sustainability, making them more open to adoption. However, it's important to understand that managerial innovativeness in this context is not a standalone concept; rather, it probably interacts with the other organizational, technical, and environmental aspects mentioned in this research. For instance, adopting sustainable cloud computing-based HRM may still be significantly hampered for a creative manager working in a company that is technologically immature or not aligned with sustainable principles.

As a result, it is important to take managerial innovativeness into account as one of several interconnected factors that influence the choice to embrace a new technology. A more nuanced understanding of the obstacles to and enablers for the adoption of long-term cloud-based HRM systems in SMEs might be gained by recognizing the synergy between managerial innovativeness and these other decisive elements. As a result, we claim that SMEs with innovative managers would be more likely to explore implementing sustainable cloud computing-based HRM. As a result, it is assumed that:

Hypothesis 4: Individual Context have a significant impact on SMEs' adoption of cloud computing-based HRM.

With a focus on Small and Medium Enterprises (SMEs) in Bahrain, the current inquiry is placed within the dynamic field of cloud computing-based Human Resource Management practices. Bahrain was specifically chosen as the research location because it provides a unique scenario for examining how cloud computing, and HRM interact within the context of a growing economy. The dynamics of the global market and local government are having an increasing influence on Bahrain's SMEs. They are a very ideal subject for in-depth examination and inquiry due to the confluence of factors.

Our research used a quantitative cross-sectional design. The main participants targeted for data collection were senior managers and HR specialists. This was undertaken because they are individuals who generally possess an influential say in organizational decisions about the implementation of technology.

Convenience sampling was a deliberate method of accelerating data collection. This non-probability technique is efficient and practical in getting hold of large numbers of participants in the shortest time possible. Two hundred eighty participants were used, constituting 25 small and medium enterprises from diverse sectors and organizations of different sizes. Their selection was guided by proximity to ensure willingness to participate and achieve a representative sample across different industries and firm sizes. We compared the key demographic characteristics of our sample to the larger SME population in Bahrain using the data from the Bahrain Ministry of Industry, Commerce, and Tourism (2021). Our sample represents the overall SME population in Bahrain in terms of industry distribution and organizational size.

The sample size determination was informed by power analysis to ensure that there would be adequate statistical rigor for the testing of hypotheses. According to Albusaidi et al. (2024), a power analysis in research helps identify a minimum sample size needed to detect the effect size of interest at the needed levels of confidence to establish the reliability and robustness of the study findings. The targeted population was that of SMEs in Bahrain. However, given the diversity and variety of these enterprises, the findings can also be generalized to other SME setups in developing countries. This increases the general applicability of this research and, hence, its relevance even more.

Data collection was based on online adapted questionnaires from previously conducted research studies (Ahmad et al., 2018; Ayyash, 2022, 2023), tailored to the particular contextual features of SMEs in Bahrain. In our research, the 5-point Likert scale operationalized the variables. Therefore, with this scale, we could measure technical preparedness, organizational size, competitive pressure, and pressure from trade partners for the first time from our research. Table 1 below explains the measuring constructs used in this investigation.

Data analysis was performed by applying Partial Least Squares Structural Equation Modeling with the SmartPLS software. The justification for the use of PLS-SEM is that it is applicable in exploratory research and when the model involves several constructs and indicators (Hair et al., 2019). The approach or way PLS-SEM goes about doing things is to maximize the explanation of variance in dependent constructs. Logically, this fits within the purpose of this study in identifying those factors that influence adoption. Further, PLS-SEM can efficiently handle sample sizes that are small (Chin, 1998), and our sample size is 280; hence, it is suitable for our study. Besides, PLS-SEM does not impose strict normality assumptions on the data; hence, it is appropriate for real-world data that may not meet the criteria of normality distribution perfectly (Hair Jr et al., 2021).

The research performed a statistical analysis on a comprehensive basis to identify the key drivers of the adoption of cloud computing-based HRM systems in SMEs in Bahrain. The descriptive data analysis will help to gain an in-depth understanding of the multidimensional trends revealed by the HR executives and senior managers in Bahrain. Table 2 has been presented in its entirety to illustrate all the measurement constructs used in the underlying study.

The average score of 3.605 within the technological context denotes a modest level of technical readiness among the surveyed firms. Moving our attention to the environmental context, we can see that the average competitive pressure value of 4.845 is noticeably different from the others. Comparatively speaking, the average compatibility score found in the technology domain is 3.462.

Regarding each measure, the standard deviations reveal how much agreement or disagreement there was among the participants (Al-Tahitah et al., 2023). The lowest standard deviation of technology readiness has a value of 0.398, meaning that participants highly agreed upon the readiness of their firm to adapt to new technologies. Compatibility, in particular, explains a higher standard deviation of 1.083, hence showing that respondents have varied perceptions about how the cloud computing-based HRM system fits their existing systems.

This low variability in technology readiness could suggest that large SMEs in this sample view their technological infrastructure and capabilities as similar, which may influence their readiness to adopt. The higher variability in compatibility may suggest that the respondents have different opinions on whether cloud-based HRM systems are compatible with current practices, which could influence decisions on adoption.

Also, this is supported by a high mean value of competitive pressure, which is 4.845; it identifies that SMEs are under considerable pressure from competitors with respect to the adoption of cloud computing-based HRM systems. The standard deviation for competitive pressure is 0.734, which represents moderate agreement among participants.

These descriptive statistics support our theoretical framework and hypotheses that environmental factors like competitive pressure may be of vital importance for the diffusion of cloud computing-based HRM systems among SMEs in Bahrain.

The descriptive statistics in Table 2 show that SMEs in Bahrain perceive the technological and organizational contexts as favorable for adopting cloud computing-based HRM systems. The mean score of the technological context is 3.605, with a standard deviation of 0.812, indicating it is moderately favorable. This is also reflected in consistent perceptions of technological readiness as measured by the low variability in technology readiness (mean = 4.308, SD = 0.398).

Means of 3.462 and 3.842 and standard deviations of 1.083 and 0.687, respectively, demonstrate that compatibility and complexity are not only different but also manageable in infrastructure complexity. The very high scores in Relative Advantages indicate the recognized benefits of strong leadership support for innovation adoption. Organization Size also has a positive effect on the rate of adoption, with the mean value being 4.568, showing that the larger the SME, the more capable it feels of implementing new technologies.

The high mean scores in Competitive Pressure, 4.845, and Trading Partner Pressure, 4.708, underscore the importance of environmental and individual factors in the adoption process. The moderate consensus of these factors across one another reinforces their significance as external forces. Moreover, Individual Context and Managerial Innovativeness retain high means of 4.733 and 4.768, respectively, indicating strong personal attitudes and managerial support in championing technological advances. The overall high intention to adopt cloud-based HRM systems with reasonable consistency is a strong indicator of the will and readiness of SMEs in Bahrain to embrace these systems.

These findings bring out the fact that even though there is uniform high technological readiness, the strong facilitators in adopting a cloud computing-based HRM system are mainly environmental pressures and individual leadership. By 'environmental pressures, 'we refer to the competitive and partner-driven factors that influence an organization's technological decisions. This leads to a favorable environment for technological advancement in these organizations.

Bootstrapping is a resampling technique used to estimate the reliability and significance of path coefficients in structural equation modeling (SEM). In this analysis, bootstrapping helps evaluate the relationships between different constructs, such as Technological Context, Organizational Context, Environmental Context, Individual Context, and SMEs' adoption of cloud computing-based human resource management.

The diagram presents path coefficients and t-values (represented by numbers near each path). Figure 2 shows the strength and significance of the relationships. For example, significant paths (typically those with t-values above 1.96) indicate strong relationships between constructs. This method allows for a robust analysis of the model by providing confidence intervals and standard errors for each path, enhancing the validity of the findings. Bootstrapping ultimately supports the stability of the model's results, confirming that observed relationships are not due to sampling error alone.

The measurement model in this study was evaluated using Smart-PLS's partial least squares (PLS) implementation to confirm the reliability and validity of the framework (Hair Jr et al., 2021; Alastal et al., 2024). Two main types of validity—convergent and discriminant—were assessed as part of the measurement model evaluation. Convergent validity ensured that items within each construct were strongly correlated, verified by outer loadings above 0.7, AVE values above 0.5, and CR values exceeding 0.7 (Hair et al., 2021). Discriminant validity confirmed that each construct was empirically distinct, meeting the Fornell-Larcker criterion and cross-loading standards. Structural Equation Modeling (SEM) was applied to illustrate the theoretical framework and depict relationships among constructs, including Technological, Organizational, Environmental, and Individual Contexts and their influence on SMEs' adoption of cloud computing-based HRM.

Figures 2 and 3 demonstrate how the PLS algorithm calculated path coefficients and how bootstrapping analysis was used to validate the significance of these paths. Bootstrapping, a resampling technique, provides confidence intervals and standard errors for each path, supporting the robustness and validity of the model's findings by ensuring that observed relationships are not due to sampling error alone. Significant paths, with t-values above 1.96, indicate strong relationships between constructs, thus enhancing the validity of the proposed model.

In the measurement model evaluation, we evaluated important factors such as outer loadings (OL), Cronbach's alpha (CA), composite reliability (CR), average variance extracted (AVE), Variance Inflation Factor (VIF), and validity. Each indicator of each factor should be greater than or above its threshold value to meet the requirements with respect to the reliability and validity of the measurement model.

CA is a measure of internal consistency reliability, meaning the extent to which the set of items relate to each other. A high level of CA indicates that the items measure the same underlying construct. According to Nunnally and Bernstein (1994), a CA score of 0.70 or greater is acceptable. Similarly, CR is a measure of internal consistency for a set of items; however, it considers the actual loadings of the indicators and, hence, is a better estimate of reliability in PLS-SEM. The threshold value for CR is 0.70 or higher to reflect good reliability (Hair et al., 2019). Our findings showed that for each construct, the values of CA and CR are presented in Table 3; all constructs show values of CA and CR >0.70, which indicates that the internal consistency reliability is good. Further, all AVE values were >0.50, thus indicating satisfactory convergent validity.

According to Mohammad et al. (2024); Hair et al. (2019), the CR was calculated to infer internal consistency, which should be at least 0.70. According to the indications of Henseler et al. (2015), the AVE value of a construct was evaluated for its appropriateness; thus, the value had to be >0.50 to reach convergent validity. Alzoraiki et al. (2024) suggested that new items having recently been developed should have a loading factor of more than 0.5; items that are established should have at least affected the loading factor of 0.6, and items demonstrating < 0.6 but with R² > 0.1 should be preferred; and Falk and Miller (1992) endorsed this for items.

It is also worth noting that all the constructs had correlation coefficients exceeding 0.50, reflecting their high reliability in capturing the contextual factors without appreciable influence on the hypotheses posed. More empirical evidence capable of supporting such a conclusion that there is sufficient convergent validity, as in Table 3, is that all constructs had AVE values greater than the cutoff threshold value of 0.50.

In order to find indicators with significant connections to one construct and those with high loadings across several constructs, the authors looked at cross-loadings (Milhem et al., 2024). This research sought to ascertain if the indicators loaded on a particular latent variable more heavily than they did on other latent variables in the same row. As a result, it suggests that indications or objects should be loaded more heavily on the principal construct than on other constructs. Table 4 shows that all latent indicators in each row have stronger loadings on their respective constructions than on other constructs. These findings show each concept to be significantly one-dimensional.

Common Method Bias (CMB) is the term for the phenomena that occurs when differences in answers can be attributed to the intrinsic characteristics of the measuring tool, as opposed to the tool's intended accurate representation of the genuine inclinations of the subjects. Harman's approach was employed in this study to assess the existence of the CMB. The results of the research showed that one single factor only explained 31.098% of the total variation, suggesting that the CMB had little effect on the data that we have.

It is a statistical “method used to explain the directed relationships among variables.” Several linear regression, canonical correlation analysis, factor analysis, discriminant analysis, and many more complex sets of models are usually applied in a multivariate analysis of variance and covariance analyses such as MANOVA, ANOVA, and ANCOVA, among the models in the field of statistical analysis (Tabachnick et al., 2013). After estimating the model, one can use structural equation modeling to assess the variable relationships. Hair et al. (2006) explain that “the structural model provides a complete and comprehensive description of the relationships and covariances between the multiple variables being studied.” The model allows the researcher to develop detailed and rich portrayals of the complex processes within their subject area. Exogenous and endogenous factors, generally called independent and dependent variables, correspondingly relate to one another, as the study results indicate. In general, a structural model's overall fit must be considered the relevant factor since its postulated estimations with their coefficients, dimensions, route, and significance must be considered (Hair et al., 2006).

Therefore, the present study's purpose was to research the combined effect of technological context, organizational context, environmental context, and individual context on the adoption of cloud computing-based human resource management by SMEs using PLS-SEM. From this unique setting of research, a research model has been developed. The text is given with the hypothesis model presented in Figure 2. This will provide the statistical significance of the hypothesis model with the T-value using the bootstrapping method shown in Figure 3. The bootstrapping approach utilizes random sampling and replacement processes to create similar resamples to the actual data. The method followed in the present study is bi-functional; it evaluates the reliability of data and allows an estimation of the probable relevance and imprecision of the path coefficient (Chin, 1998).

Our study offers a wide range of significant implications in the area of theoretical contributions that go beyond the simple affirmation of preexisting frameworks. Our main point is that current theoretical frameworks, including the Technology-Organization-Environment framework, must be updated or expanded in light of the adoption landscape in emerging nations. The limitations of existing adoption theories are highlighted by the actual data from the study on small and medium-sized firms (SMEs) in Bahrain. As a result, this calls for academics to examine and even modify these models to fit the regional context. In order to meet particular economic and socio-cultural situations, it is crucial to construct localized and complex adaptations of these frameworks, as stated in the aforementioned statement.

Our finding of the decreasing importance of individual management innovation in Bahraini SME's adoption of technology has important theoretical implications as well. The existing corpus of information, which predominantly focuses on the significance of management or individual agencies in adoption choices, considerably diverges from this discovery. Due to the large disparity shown in this domain, an academic study has been conducted about the incorporation of regional and organizational cultural characteristics as moderating or mediating variables. Future theoretical studies should focus on how cultural elements could perhaps encourage the distribution or modification of decision-making authority from a centrally located management body to a more distributed or decentralized agency.

The investigation of environmental variables and their function as influencing elements in the adoption trajectory is given considerable attention in our study. We highly support the enhancement of present theoretical frameworks by including factors that explicitly take into account these environmental catalysts in light of our study findings, which highlight the tremendous effect of competitive forces and market sustainability dynamics. When doing research on SMEs in emerging economies, this element is very important. External variables have the potential to have a large, sometimes disproportionate impact in certain situations.

A challenging theoretical issue raised by the disconfirmation of the fourth hypothesis (H4) is whether existing frameworks like the TOE model can be used widely. When compared to earlier studies, our research results show inconsistencies, which calls for a comprehensive reevaluation and possible model improvement. This is especially relevant to their capacity to thoroughly address the complex patterns of technology adoption in scenarios encompassing various economic and cultural circumstances.

The current study acts as a spur for the development of ideas on the adoption of technology. It fervently supports the inclusion of factors that are unique to organizational and geographic contexts. By offering a thorough examination of the complex interaction between technology advances, organizational structures, and environmental variables in determining the patterns of adoption behaviors, the current study adds to the body of scholarly literature. Notably, this study illuminates a situation that has received less attention, namely SMEs operating in emerging markets like Bahrain.

From a management standpoint, the findings of our inquiry offer a wealth of complex insights that go beyond what is currently known, particularly in the area of SMEs in developing countries like Bahrain adopting new technologies.

The available empirical data provide solid support for the idea that technical compatibility is vital for aiding a successful adoption process. It is imperative that those in leadership roles pay serious attention to how possible technologies match the existing systems and procedures in place, especially those in charge of making strategic decisions within their businesses. It is important to give the issue of alignment the status of a strategic objective rather than just an operational detail. The achievement of a high degree of compatibility not only makes the adoption process go more quickly but also helps to maximize return on investment, successfully achieving the organization's two goals.

Our analysis further emphasizes the critical need for senior management assistance throughout the technology adoption lifecycle. Our research suggests that top-level management support is, in fact, a necessary condition for the effective execution of a specific project, in contrast to earlier perspectives that saw managerial support as beneficial but not essential. The aforementioned finding has important ramifications for management choice-making since it shows that long-term commitment and support from top-level executives are not only preferred but also absolutely essential. Therefore, to secure the crucial support needed for this goal, businesses must place the highest priority on executive education and sensitization activities that focus on the deployment of sustainable cloud computing-based HRM systems.

Additionally, it has been established that the environmental context, particularly the impact of market forces related to sustainability, has a substantial role in influencing adoption behaviors. Managers must be acutely aware of the external competitive environment and the rising importance of sustainability as a differentiator in the marketplace. Organizations may gain market share by purposely linking their technology adoption plans with sustainability goals rather than being exclusively led by normative demands to adopt sustainable practices.

Our research underlines complex inter-relationships amongst technological, organizational, environmental, and individual factors. This then underlines the importance of managers taking holistic views when considering any adoption landscape. Too narrowly focusing on one factor or another will likely yield less than optimal outcomes. Sometimes, a manifestly superior technology faces unfavorable deployment in management figures or other influences at play. Only when an all-inclusive and multi-disciplined plan has been developed for the variables relevant to the technology impact realignment is it considered optimal for deployment.

The study offers managers an impressively rich and detailed insight into the landscape of technology adoption. It highlights the numerous considerations that need to be made during strategic judgments. From our findings, a multidimensional approach becomes necessary in an effort to achieve competitive advantage and sustainable organizational resilience through effective technology adoption.

The present study will comprehensively address the adoption of technology in terms of the adoption of sustainable cloud computing-based Human Resource Management (HRM) systems amongst small and medium-scale enterprises in emerging economies.

Through the digitization of HR processes, for instance, cloud-based HRM systems minimize paper and physical resource usage, thereby directly reducing or minimizing their contribution to environmental degradation and increasing ecological sustainability. In addition, these systems encourage remote working and flexibility in work arrangements, thus reducing carbon emissions produced during daily movement to and from work. From a social perspective, increased transparency and inclusiveness in human resources support the equity and diversity of employees in the workplace, accordingly satisfying corporate social responsibility objectives.

In-depth research conducted through the present study provides valuable information, though it has significant practical implications for several stakeholders comprising executives of organizations and operational workforces.

The study's main point of focus is the importance of filling in an intensive review of infrastructure. This assessment will determine the compatibility between the proposed sustainable HRM systems and the prevailing organizational structures. To realize this objective, SMEs are advised to use tested methods such as PESTLE (Political, Economic, Social, Technological, Legal, and Environmental) studies. By doing so, companies can avoid potential roadblocks in the operationalization and integration stages by addressing issues in advance, thus making overall implementation more effective and affordable. The most influencing factor is the alignment of advanced planning with the strategic importance of technology compatibility, as depicted in our case study.

Our research findings also emphasize the organizational environment as being of particular significance and the need for businesses to implement and invest in specialist staff training initiatives and programs that would start building capacity. In this respect, the actual development of technical skills should not be the sole focus of such projects; instead, it should address a broad spectrum of other issues, including but not limited to organizational preparation and management of change. Specialized modules must be designed to manage such complex and unique sustainable cloud computing-based HRM systems. The addition of such modules will be specially designed to handle the complexity involved, ensuring smooth transitioning from both a technological and human resources perspective. Addressing and closing the knowledge and skill gaps will minimize any potential barriers to such a system's successful deployment and usage.

The direct contribution of sustainable cloud computing-based HRM systems to environmental objectives has been reducing C0₂ emissions by better exploiting the servers and reducing energy use compared to traditional IT infrastructure. These systems digitize previously paper-based processes, hence minimizing the usage of paper and contributing to the minimization of paper waste. By enabling virtual work and collaboration, the need for travel is reduced, hence reducing the environmental footprint of SMEs.

Socially, these systems have improved inclusion and equity by offering flexible work environments to employees and the ability to train and develop from home. They enhance workforce engagement and satisfaction through smooth HR processes in areas like payroll, benefits management, and performance appraisals. Further, they enable SMEs to align their operations with broader societal sustainability expectations and enhance their reputation as socially responsible organizations.

Our findings also indicate that environmental issues, particularly sustainability, affect the SMEs. Given such a reality, it is opportune for enterprises to consider undertaking comprehensive sustainability audits, either internally or with external experts. In undertaking such audits, an organization has a unique opportunity to play two roles. On one hand, internally, these audits help an organization's ecological effects to be quantified and understood, which improves their grasp of the issue. On the other hand, these audits can increase a company's market position externally in a competitive environment that prioritizes environmental responsibility. The practice of such audits can have a twin advantage: first, a competitive edge, and second, serving as a proactive outlet vis-à-vis consumer bases, regulatory authorities, and possible business partners since there is now a growing stakeholder focus on sustainable business practices.

The question of sustainable technology for SMEs has to be effectively communicated proactively in the present market environment, where increasing emphasis is placed on sustainability. A proactive communication strategy could allow differentiation and pass on some stringent trade and regulatory requirements by serving as a unique selling proposition. Hence, the said growth brings new opportunities for business partnerships and market expansion.

Below, the results of our research project shed a multidimensional light on the problems created around the effort to establish sustainable HRM systems in less developed countries. This treasure trove of insights furnishes managers with conceptual perspectives, and intervention means to effectively translate such concepts and further a more sustainable and competitive corporate world. Organizations seeking to advance their strategic positioning concerning short-term and long-term success are well served by considering these valuable outcomes.