Narratives of Total Quality Management, Green Management, Risk-Taking on Organizational Performance: 2024 PDF

RESEARCH ARTICLE Published Online: May 31, 2024 https://doi....

RESEARCH ARTICLE Published Online: May 31, 2024 https://doi.org/10.31893/multiscience.2024230 Narratives of total quality management, green management, risk-taking on organizational performance: The moderating role of IT infrastructure Usman Mohammeda | Shahmir Sivaraj Abdullaha | Shuhymee Ahmada aSchool of Business Management, College of Business, Universit Utara Malaysia. Abstract This study delved into examining the interplay among TQM, GM, RSK, and OP, alongside exploring the moderating influence of IT infrastructure within these relationships in organizations. Drawing from a sample of 361 owners/managers in north-western Nigeria, the research focused on managers due to their comprehensive understanding of organizational dynamics. Employing the PLS-PM algorithm, the analysis revealed that GM and TQM significantly influence OP directly, while the association between IT infrastructure and RSK was not statistically significant. Additionally, the study shed light on the moderating role of IT infrastructure, particularly in the relationship between GM and OP, an area less explored in previous research. These findings underscore the importance of proactive approaches and innovation to foster organizational success, with TQM and GM policies emerging as crucial factors for sustaining and enhancing OP in SME manufacturing organizations. Keywords: total quality management (TQM), green management (GM), risk-taking (RSK), organizational performance (OP), small and medium enterprises (SMEs) 1. Introduction Organizational performance serves as a fundamental criterion for evaluating organizations, reflected in its frequent use as both an independent and dependent variable in research (March & Sutton, 1997; Siddiq & Javed, 2014; Obeidat, 2016; Latif et al., 2020; Cheffi et al., 2021; Singh & Misra, 2021). In today's business landscape, it distinguishes one organization from another (Lakomski & Evers, 2011), encompassing both financial and non-financial dimensions (Kee & Rahman, 2017). Continuous translation of entrepreneurial capabilities into strategic actions is essential for achieving superior organizational performance (Javad et al., 2020; Nazarian et al., 2017). However, many manufacturers in developing countries lack clear green business strategies, despite the potential benefits of environmental management practices (Yasir et al., 2020; Javeed et al., 2020; Roscoe et al., 2019). Additionally, implementing risk management has been shown to lead to cost reductions and improved performance across various studies (Chen et al., 2019; Hoyt & Liebenberg, 2011; Pagach & Warr, 2011), particularly crucial for Small and Medium Enterprises (SMEs) facing heightened competition and economic uncertainties (Angeline & Teng, 2016; Altman et al., 2010; Wright et al., 2001). By identifying risks and opportunities, SMEs can enhance their resilience and innovative capacities, ultimately bolstering their competitiveness in the global market (Laforet & Tann, 2006; Rehman & Anwar, 2019; Falkner & Hiebl, 2015). 2. Theoretical background 2.1. Resource-based theory Organizational performance within the SME domain has often been anchored in Resource-Based Theory (RBT), evident in studies by Aksoy (2017), Ch’ng et al. (2021), Saridakis et al. (2019), and Sok et al. (2013). RBT offers a foundational framework to discuss how resources and capabilities contribute to organizational performance. Total Quality Management (TQM), Green Management (GM), and Risk-taking enable SMEs to capitalize on market opportunities, mitigate threats, and cultivate unique capabilities that competitors struggle to replicate. Despite this, prior research has not extensively examined the impact of these constructs on SME performance (Aksoy, 2017; Mirvis et al., 2016; Rosenbusch et al., 2011). RBT underscores the critical role of internal and external resources in shaping organizational performance and competitiveness, highlighting the value of scarce, Multidiscip. Sci. J. (2024) 6:e2024230 Received: November 21, 2023 | Accepted: April 21, 2024 Mohammed et al. (2024) 2 immobile, and difficult-to-imitate resources. By effectively managing these resources, firms can outperform their peers and gain a competitive edge (Seddon, 2014; Turel et al., 2017; Xu et al., 2016). 2.2. Literature Review 2.2.1. Organizational performance Organizational performance serves as a commonly studied dependent variable in organizational and management research (Rodrigues & Pinho, 2010; Yeo, 2003; Zgrzywa-Ziemak, 2015), capturing the significance of various factors in organizational activities and strategies. However, the concept's scope and definition remain somewhat elusive and inconsistent (Kirby, 2005; Richard et al., 2009). March and Sutton (1997) note its pervasive presence in organizational research, often assumed without explicit justification of its definition and structure. Laeeq, Shahzad, Ramalu, and Fareed (2016) define organizational performance as an indicator of effective management and an organization's capacity to provide value to both customers and stakeholders. While Santos and Brito (2012) differentiate it conceptually from organizational effectiveness, in practice, organizational performance encompasses diverse indicators such as accounting returns, growth, and stock market performance. Fareed, Isa, Ahmad, and Laeeq (2016) emphasize that performance entails the realization of strategic goals to optimize employee performance and enhance overall organizational effectiveness. 2.2.2. Total Quality Management Jimoh et al. (2018) define Total Quality Management (TQM) as the continuous enhancement of all organizational processes to ensure consistent customer satisfaction. Conversely, Aquilani et al. (2017) perceive TQM as an organizational strategy aimed at improving customer satisfaction by meticulously managing output quality. Furthermore, TQM is regarded as an integrated approach to acquiring and sustaining high-quality outputs. This approach emphasizes maintenance, continuous improvement, and prevention of failures across all levels and functions of the company to meet or surpass consumer expectations (Ghani et al., 2020). 2.2.3. Risk management Risk management practices are defined as the capability of a firm to use its resources effectively to gain a competitive advantage and improve performance by reducing loss (Yang et al., 2018). Moreover, risk-management practices enhance firm performance by cost-effectively minimising risks and using time and resources to gain a competitive advantage (JalalKarim, 2013). Furthermore, previous studies explored that risk-management practices have a strong impact on firm performance. Risk management refers to enterprise risk management, the authority by which a corporation in a particular industry gains access to, controls, exploits, and monitors risks from all sources to maximize the firm's long-term and short-term value to its stakeholders, according to Shad et al. (2019). 2.3. Hypothesis development and the relationship between variables Several research hypotheses were developed based on the research model shown in Figure 1 The following discusses them. Figure 1 Research Framework. https://www.malque.pub/ojs/index.php/msj Mohammed et al. (2024) 3 2.3.1. Total quality management and organizational performance Numerous researchers have investigated the relationship between Total Quality Management (TQM) and organizational performance across various dimensions, including financial, innovation, operational, and quality performance. While the impact of TQM on different aspects of performance may vary, there is a consensus that TQM positively influences quality performance. Advocates of TQM argue that its implementation not only improves overall organizational performance but also leads to higher-quality products. Several studies in the literature consistently support a positive and significant relationship between TQM and performance, as demonstrated by research conducted by Arumugam et al. (2008), Corredor and Goñi (2011), Faisal et al. (2011), Gunday et al. (2011), Hendricks and Singhal (2001), Joiner (2007), Miyagawa & Yoshida (2010), Shenaway et al. (2007), and Yeung & Chan (1998). Consequently, the following hypothesis is proposed for examination: H1. The implementation of TQM has a consistent positive and significant impact on organizational performance. 2.3.2. Green management and organizational performance Several studies have demonstrated that adopting Green Manufacturing (GM) practices significantly enhances organizational reputation and public image (Khan & Qianli, 2017; Zhu et al., 2007). Particularly in emerging economies, numerous studies have highlighted the pivotal role of green manufacturing in bolstering the competitiveness of manufacturing firms in the foreseeable future (e.g., Diabat and Govindan, 2011; Rehman et al., 2013; Rehman et al., 2016; Masri & Jaaron, 2017; Zaid et al., 2018; Afum et al., 2020). Afum et al. (2020) investigated the mediating effect of green supply chain practices on the relationship between GM practices and sustainable performance (economic, social, and environmental) in Ghanaian manufacturing SMEs, confirming the significant impact of GM practices on sustainable performance and the mediating role of green supply chain practices. In light of this expanding body of literature, the following hypothesis is proposed: H2: There is a statistically significant correlation between the GM Practices and OP. 2.3.3. Risk management and organizational performance Despite the increasing adoption of risk management, debates persist regarding its benefits, particularly concerning its negative net present value in frictionless capital markets where investors can mitigate risk through diversification (Beasley et al., 2008). However, risk management, including Enterprise Risk Management (ERM), has been associated with various potential benefits such as reducing expected corporate tax schedules, mitigating underinvestment, managing managerial compensation, minimizing costs of financial distress, and easing external financing (Pagach & Warr, 2011). Several studies (e.g., Barton et al., 2002; Lam, 2003; Stulz, 2003; COSO, 2004; Nocco and Stulz, 2006; Gordon et al., 2009; Hoyt and Liebenberg, 2011) have acknowledged the potential of risk management to enhance firm performance across diverse industries. ERM, in particular, has been linked to the creation of shareholder value and the maximization of wealth, aligning with the overarching goals of corporate entities (Bowen et al., 2006; Nocco & Stulz, 2006; Block et al., 1989; Brealey et al., 2006). Given the mixed findings surrounding these benefits, it is imperative to investigate the impact of risk management on organizational performance. Thus, the following hypothesis is proposed: H3: Risk management has a significant impact on organizational performance. 2.4. IT infrastructure IT infrastructure refers to the essential components necessary for the operation of IT systems and IT-enabled activities. These consist of software, composite hardware, network services, and resources. The infrastructure facilitates the delivery of services and solutions to consumers, partners, and employees by enterprises. In order to conduct corporate operations on a global scale, the presence of information technology (IT) is essential and crucial. IT plays a crucial role in international transactions and cannot be ignored. Multiple studies have also demonstrated the significance of information technology (IT) in corporate operations (Van Der Zed et al., 1999). Inter-organizational IT infrastructure brings several advantages, including enhanced operating performance for organizations that have information technologies that align with their supply chain (Malhotra et al., 2007; Obal & Lancioni, 2013). 2.4.1. IT infrastructure and organizational performance Bharadwaj (2000) emphasizes that the combination of IT with complementary resources can result in long-lasting competitive advantages, eventually improving organizational performance. The development of IT capabilities in organizations enhances their distinctiveness and provides them with a competitive advantage (Dehning & Stratopoulos, 2003; Santhanam & Hartono, 2003). Furthermore, according to Teece et al.'s (1997) theory of dynamic capabilities, resources by themselves do not automatically result in competitive advantages. Instead, they must be converted into dynamic capabilities in order to enhance performance. Based on these observations, this paper proposes that: H4: There is a statistically significant correlation between the IT infrastructure and OP. https://www.malque.pub/ojs/index.php/msj Mohammed et al. (2024) 4 2.5. The moderating role of IT infrastructure IT infrastructure refers to a collection of shared, tangible IT resources that establish a framework for business applications (Broadbent & Weill, 1997; Duncan, 1995). These resources encompass platform technology, including hardware and operating systems, network and communication technologies, and essential software applications (Duncan, 1995; Bhatt et al., 2005). The effectiveness of IT infrastructure is contingent upon factors such as scalability, modularity, and compatibility, enabling the support of multiple business applications (Chen et al., 2015). This capability fosters efficient communication and facilitates information sharing across various departments within organizations (Byrd & Turner, 2001). Therefore, we propose: H5: IT infrastructure positively moderates the relationship between TQM and organizational performance. H6: IT infrastructure positively moderates the relationship between Green Management and organizational performance. H7: IT infrastructure positively moderates the relationship between Risk-Taking and organizational performance. 3. Methodology and Research Design 3.1. Sampling design and data collection procedures This study employed a quantitative research method to collect data and test hypotheses, aligning with the post-positivist worldview that values respondents' personal experiences. Utilizing a structured questionnaire survey method, the primary aim was to gather cross-sectional data from owner/managers of manufacturing SMEs in Nigeria in a quantitative, exploratory, and causal manner. Specifically focusing on the North-west region of Nigeria, encompassing Kano, Kaduna, and Jigawa states, known for their concentration of businesses ranging from large companies to small enterprises. These states were chosen for their homogeneity and status as industrial hubs within the region. The research commenced with face validity interviews with industry players and associations to ensure the clarity and validity of the questionnaires. Subsequently, a pilot test involving 30 respondents was conducted to assess the validity and reliability of the questionnaires. Following satisfactory pilot test results, a field survey was undertaken. Employing a systematic sampling technique, 400 respondents were sampled, resulting in a response rate of 95%, with 361 usable questionnaires retained for data analysis after discarding incomplete responses. 3.1.1. Measures for organizational performance The measurement of organizational performance was conducted using a single-attribute approach, as proposed by Gupta and Govindarajan in 2000. The key informants were requested to evaluate their organizational performance using a seven-point Likert scale, rating 10 specific aspects. These aspects, as identified by Asiaei and Jusoh (2015, 2017) and Asiaei et al. (2018), include return on investment, profit, cash flow from operations, cost control, the success of new product development, sales volume, market share, market developments, personnel advancements, and political-public affairs. The Likert scale ranges from 1, indicating significantly below average, to 7, denoting significantly above average. This scale has been extensively utilized and verified in the context of strategic management (Govindarajan and Fisher 1990). 3.1.2. Measures for TQM The measurement of TQM is conducted using a ten-item instrument that was adopted by Claver-Cortés, Pereira- Moliner, Tarí, and Molina-Azorín in 2008. The research that was considered for this paper provided the basis for defining the ten TQM practices discussed here. The survey in this study utilized a seven-point Likert scale response, with 1 representing "totally disagree" and 7 representing "totally agree". The poll consisted of ten items. The Likert scale is a well-established research technique consisting of multiple items that allows for flexibility in scoring. 3.1.3. Measures for green management The measurement of green management consisted of 14 components, which were derived from the research conducted by Adelegan (2018). These items were derived from the research conducted by Weng, Chen, and Chen (2015); and Videen (2010). The green management components were adopted from the work of Adelegan (2018). The section should be completed by the owner or management of the firm. However, if the firm is part of a large organization, the respondent is required to answer the questions specifically regarding their own manufacturing firm. The survey in this study utilized a seven-point Likert scale response, ranging from 1 (completely disagree) to 7 (absolutely agree), for the evaluation of these 14 topics. 3.1.4. Measures for risk-taking RT was measured with 4 items adapted from the work of Herzog and Leker (2010) and Tellis et al. (2009). These 4 items employed a seven-point Likert scale response (1 = totally disagree to 7 = totally agree) for the survey of this study. The Likert scale response is an established multi-item research tool that gives room for scoring variability. https://www.malque.pub/ojs/index.php/msj Mohammed et al. (2024) 5 3.1.5. Measures for IT-infrastructure IT infrastructure was assessed using a set of 12 items adapted from the research of Benitez, Ray, and Henseler (2018). These items were originally derived from the work of Benitez-Amado and Ray (2013), which built upon earlier scales developed by Chang, Wong, and Fang (2014); Miller and Friesen (1983); and Khandwalla (1977). The study utilized these items to measure Information Technology infrastructure, maintaining consistency with previous research by Benitez et al. (2018). Respondents rated these 12 items on a seven-point Likert scale (1 = totally disagree to 7 = totally agree), providing a well-established and versatile tool for capturing nuanced responses. The proposed research employed partial least squares (PLS) modeling using SmartPLS version 4 (Becker et al., 2023), a methodology gaining popularity in social sciences and management due to its ability to analyze complex connections between observed and latent variables, overcoming limitations of traditional statistical analysis techniques. Notably, the variance-based PLS approach aims to enhance the explained variance of the outcome variable (Hair et al., 2014), making it suitable for this study. Guenther et al. (2023) highlighted a significant increase in the adoption of PLS over the past decade across various fields. PLS-SEM was chosen for its versatility in handling limited sample sizes, complex models, abnormal data, and various types of research purposes, including exploratory, predictive, explanatory, and confirmatory studies (Benitez et al., 2020). To mitigate potential Common Method Bias in the collected data, thresholds proposed by Kock & Lynn (2012) and Kock (2015) were applied, ensuring full collinearity among variables. The Variance Inflation Factor (VIF) values among constructs were within the suggested threshold of Org -0.108 -0.086 0.747 0.228 0.338 0.155 3.241 0.002 0.471 Not Supported Performance Rsk -> Org -0.105 -0.112 1.170 0.122 0.291 0.043 2.640 0.002 0.615 Not Supported Performance TQM -> Org 0.423 0.434 5.597 0.000 0.317 0.556 2.210 0.002 0.627 Supported Performance IT x Rsk -> Org -0.041 -0.044 0.971 0.167 1.000 0.005 0.500 Not Supported Performance IT x Green Mgt -> Org 0.349 0.356 5.990 0.000 1.000 0.133 0.445 Supported Performance IT x TQM -> Org -0.185 -0.192 3.330 0.001 1.000 0.131 0.417 Not Supported Performance 6. Interaction plot The significant interaction (H5) is plotted in the interaction plot (Figure 4) as recommended by Dawson (2014). Figures 4 reveal that when IT infrastructure is high, the positive relationship of GM and OP becomes stronger. Figures 3 shows that when IT infrastructure is high, the positive relationships for GM and OP of SME owner/managers become stronger. 7. Conclusion The present study represents a pioneering empirical effort aimed at advancing our understanding of organizational performance (OP) by considering Total Quality Management (TQM), Green Management (GM), risk management (RSK), and the moderating role of IT infrastructure. This research holds significance on multiple fronts. Firstly, it contributes to enhancing comprehension and insight into the collective impact of TQM, GM, RSK, and IT management. Notably, there is a dearth of studies examining these factors in conjunction, particularly within manufacturing firms, especially in developing economies. The results align with Resource-Based View (RBV) theory, affirming the strategic value of these constructs in fostering competitive advantage. Furthermore, this study sheds light on the moderating influence of IT infrastructure, a pivotal intangible asset in contemporary organizational landscapes, on the relationships between TQM, GM, RSK, and OP. Notably, prior research has largely overlooked IT infrastructure as a potential factor that can amplify the effects of TQM, GM, and RSK on OP. The findings underscore the significance of TQM, GM, RSK, and IT in enhancing organizational performance, offering valuable insights for managerial decision-making. This study carries managerial implications, informing strategic planning and decision- making processes by highlighting the crucial role of IT infrastructure as a critical success factor in organizational performance enhancement. 7.1. Implications for theory and practice This study introduces a foundational research framework comprising three predictors (TQM, GM, RSK), a moderator (IT infrastructure), and a criterion (SME organizational performance). Delving into the rationale behind these factors and their interrelationships, the study rigorously examines them using partial least squares analysis. The insights gleaned from this research offer invaluable guidance for SME manufacturers in shaping their strategic plans. Additionally, the tested model serves as a roadmap for investors, policymakers, and future researchers seeking to empower SMEs in optimizing their performance. 7.2. Limitation and future research directions While the research population is confined to Nigeria, the findings offer a fresh perspective and broad insights into the journey of bolstering Total Quality Management (TQM), Green Management (GM), risk management (RSK), and IT infrastructure towards enhancing organizational performance for SMEs in similarly developing countries. This study lays the groundwork for future research endeavors exploring additional variables, such as various marketing environments and performance metrics, which could further enhance the achievements of manufacturing SMEs. Expanding upon these avenues https://www.malque.pub/ojs/index.php/msj Mohammed et al. (2024) 11 could yield valuable contributions to the field and provide actionable insights for stakeholders seeking to foster SME growth and success in similar contexts. Ethical considerations This article complies with the ethical standards set forth by [journal/publisher name] and adheres to the principles outlined in the [relevant ethical guidelines, such as the Committee on Publication Ethics (COPE) guidelines]. The authors also disclose any potential conflicts of interest that might have influenced the research or its outcomes. Furthermore, the work acknowledges and properly cites the contributions of others, acknowledging intellectual property rights and giving due credit to prior research in the field. Conflict of Interest The authors of this article declare that they have no conflicts of interest that could influence or be perceived to influence the research and its outcomes. Funding The research reported in this article was conducted without external financial support. The authors declare that they did not receive any funding, grants, or financial contributions from any organization, public or private, during the course of this study. References Abboh, U. A., Majid, A. H., Fareed, M., & Abdussalaam, I. I. (2022). High-performance work practices lecturers’ performance connection: Does working condition matter?. Management in Education, 0(0). https://doi-org.eserv.uum.edu.my/10.1177/089202062110514 Alshourah S (2021) Total quality management practices and their effects on the quality performance of Jordanian private hospitals. Management Science Letters, 11(1), 67–76. https://doi.org/10.5267/j.msl.2020.8.029 Becker, J. M., Cheah, J. H., Gholamzade, R., Ringle, C. M., & Sarstedt, M. (2023). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 35(1), 321–346. https://doi.org/10.1108/IJCHM-04-2022-0474 Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information and Management, 57(2). https://doi.org/10.1016/j.im.2019.05.003 Cain, M. K., Zhang, Z., & Yuan, K. H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), 1716–1735. https://doi.org/10.3758/s13428-016-0814-1 Cheah, J., Leong, S. Y., & Fernando, Y. (2022). Innovation strategies and organisational performance: the moderating role of company size among small- and medium- sized companies. Benchmarking: An International Journal, 2018(1), 1–15. https://doi.org/10.1108/BIJ-03-2021-0139 Claver-Cortés, E., Pereira-Moliner, J., Tarí, J. J., & Molina-Azorín, J. F. (2008). TQM, managerial factors and performance in the Spanish hotel industry. Industrial Management and Data Systems, 108(2), 228–244. https://doi.org/10.1108/02635570810847590 Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates. Conway, J. M., & Lance, C. E. (2010). What reviewers should expect from authors regarding common method bias in organizational research. Journal of Business and Psychology, 25, 325–334. Daddi, T., Heras-Saizarbitoria, I., Marrucci, L., Rizzi, F., & Testa, F. (2021). The effects of green supply chain management capability on the internalisation of environmental management systems and organisation performance. Corporate Social Responsibility and Environmental Management, 28(4), 1241–1253. https://doi.org/10.1002/csr.2144 El-Kassar, A. N., & Singh, S. K. (2019). Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices. Technological Forecasting and Social Change, 144(November 2017), 483–498. https://doi.org/10.1016/j.techfore.2017.12.016 Fareed, M., Isa, M. F. M., Ahmad, A., & Laeeq, H. (2016). Performance management system (PMS) as a predictor of employee performance of private bank managers in (Pakistan). The Social Sciences, 11(20), 5035-5042. Faeq, D. K., Garanti, Z., & Sadq, Z. M. (2021). The Effect of Total Quality Management on Organizational Performance: Empirical Evidence from the Construction Sector in Sulaymaniyah City, Kurdistan Region – Iraq. UKH Journal of Social Sciences, 5(1), 29–41. https://doi.org/10.25079/ukhjss.v5n1y2021.pp29-41 Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2023). Improving PLS-SEM use for business marketing research. Industrial Marketing Management, 111, 127–142. Hahn, E. D., & Ang, S. H. (2017). From the editors: New directions in the reporting of statistical results in the Journal of World Business. In Journal of World Business (Vol. 52, Issue 2, pp. 125–126). Elsevier. Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications. Kalogiannidis, S. I. (2021). The Effects of Total Quality Management Practices and Marketing on Performance of SMEs. A Case of Selected Manufacturing Industries, Greece. Business Management and Strategy, 12(1), 48. https://doi.org/10.5296/bms.v12i1.17995 Kenny, D. A. (2021). Mediation: Effect size of the indirect effect and the computation of power. Preuzeto s Http://Davidakenny. Net/Cm/Mediate. Htm.(1.4. 2014.). Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101 https://www.malque.pub/ojs/index.php/msj Mohammed et al. (2024) 12 Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7). Kline, R. B. (2015). Principles and practices of structural equation modelling. In Methodology in the social sciences (4th editio). New York.: The Guilford Press. Meyer, H. (1996). A business approach to effective information technology risk analysis and management. Computers & Security, 15(4), 321. https://doi.org/10.1016/0167-264048(96)88961-9 Laeeq, H., Shahzad, A., Sri Ramalu, S., & Fareed, M. (2016). Determinants of organizational performance: a case of Punjab Police. Sukkur International Journal of Management and Business, 3(2), 30-60. Mirčetić, V., Ivanović, T., Knežević, S., Arsić, V. B., Obradović, T., Karabašević, D., … Špiler, M. (2022). The Innovative Human Resource Management Framework: Impact of Green Competencies on Organisational Performance. Sustainability (Switzerland), 14(5), 1–19. https://doi.org/10.3390/su14052713 Pambreni, Y., Khatibi, A., Ferdous Azam, S. M., & Tham, J. (2019). The influence of total quality management toward organization performance. Management Science Letters, 9(9), 1397–1406. https://doi.org/10.5267/j.msl.2019.5.011 Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569. Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, \& Computers, 36, 717–731. Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0. An Updated Guide and Practical Guide to Statistical Analysis Xinhua, B., & Heda, Z. (2008). An Empirical research on relationship between information technology capability and firm performance: the evidence from listed companies and informatization power 500 in China. Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008, 5(1), 540–544. https://doi.org/10.1109/CSSE.2008.12 https://www.malque.pub/ojs/index.php/msj