Abstract
This paper examines the relation between bank profit performance and business models, using a machine learning–based approach. The analysis contributes to the literature on this relation by considering the bank portfolio’s ability to yield profits as the identification criterion of strategic profiles and by including all the components of the business model simultaneously in the identification process. Our research strategy is applied to the European Union banking system from 1997 to 2021. The paper’s primary finding indicates that specialization seems to be a strategy that results in banks adopting business profiles with better profit performance, particularly if the banks specialize in the standard retail-oriented model.

Introduction

      What characteristics of bank business models enhance profitability? The search for answers has been among supervisory authorities’ priorities since the end of the Great Recession and is likely to maintain its relevance in the aftermath of the COVID-19 pandemic. The reasons for such interest lie in the fact that the relation between business models and profitability can reveal the viability of banks’ strategic plans and, hence, the viability of banks themselves. In this regard, the Anon (2016) links poorly adapted business models to low bank profitability in advanced economies, identifying the former as an eroding agent of bank resilience over time. However, concern about the profitability of banks’ business profiles has been especially strong in the European Union (EU), as reflected by the fact that the European Central Bank launched a thematic review in 2016 to assess the relation between profitability and business models (European Central Bank, 2018).

      Banking research has examined this relation in two broad and different ways, using regression analysis to explore the effects of the characteristics of the bank business profile on profitability or grouping banks into business models by means of clustering techniques.

Conclusion

     The ability of business models to generate profits is an ongoing concern for banking authorities, especially in the EU. Closely related to this concern, we propose a new methodological strategy to identify business models and to assess their relation to profit performance. Previous works on business models have assumed the business models as a given or have identified them using similarities in balance sheet components. In either of these cases, profit performance is a byproduct of the business model. By contrast, in our identification strategy, profitability is at the core of the definitions of banks’ strategic choices. In particular, business models are obtained using the contributions of the bank portfolio components to profitability as the instruments of clustering analysis.

      The empirical exercise focuses on the EU banking system in 1997–2021. According to the classification of business profiles based on their overall contributions to profit performance, the best profile is a standard retail-oriented model, the second-best one is a standard wholesale model, and the worst profile, which has only equity as a characteristic component, does not match any standard model. Therefore, specialization seems to be a strategy that results in banks adopting business profiles that perform better in terms of their contributions to profitability, particularly if the banks specialize in the standard retail-oriented model.

Abstract

     Green innovation is an essential and burning topic for environmental and organizational performance. Therefore, this research aims to examine the effect of green innovation on environmental performance, which leads to organizational performance. Another objective is to measure the impact of two dimensions of green innovation, such as green process & green product measures, on green innovation. The second prime aim of this research is to evaluate the moderation of management commitment & human resource practices in an association between green innovation and organizational & environmental performance. A total of 320 employees provided their perspectives on a self-administrated questionnaire from the textile industry of Pakistan. We have employed SEM-based multivariate modeling to examine the data. This research has measured the reflective indicators measurement model through confirmatory factor analysis, an obvious choice of structural equation modeling to examine observed and unobserved variables and indicators using PLS-SEM (partial least square-structural equation modeling). The research findings reveal a positive & significant effect of product & process innovation on green innovation. Further, green innovation significantly impacts environmental and organizational performance. A two-way interaction (moderation) of human resource practices & green innovation does not have a cogent moderating effect on organizational & environmental performance. However, management commitment has a significant moderation between green innovation & organizational performance. A three-way interaction (moderated moderation) model finds a substantial effect on organizational attainment but an insignificant impact on environmental performance. The research outcomes significantly contribute and suggest that practitioners and policymakers must institutionalize green innovation practices in their organizations to enhance their organizational and environmental performance. HR practitioners play a vibrant role in creating green norms and organizational culture. The study findings also suggest that management commitment to green innovation advocates organization-level transformations toward adopting green practices.

Introduction

     With the advent of technology and scientific advancements, the world has provided immense exposure to artificial resources, making life leisurely. However, artificial resource opulence has come at the cost of sustainability and the environment. The world has thus been facing issues with balance acquisition between sustainable usage of resources and economic development [1,2]. Managing both have been a great concern for the economies as one comes at an expense and tradeoff. Researchers have observed that economic growth and sustainability cannot go together [3,4]. Green growth and general innovation are well recognized for their interrelationship [5]. It is essential to observe that national economic development is established through soaring industrial activities [6]. In this essence, Segerstrom [7] validated through his proposed model that firm-level innovations immensely support economic growth. Green innovation is beneficial to prevent the environment from pollution, help in recycling waste, and could also save non-biodegradable energy [8,9]. Moreover, green technologies could achieve sustainable development [10,11]. In order to implement and achieve the target of green innovativeness, firms need to adopt teamwork within their organizational ecosystems (Muisyo et al., 2022). The said process can be run by key parameters such as the organizations’ access to large-scale data, the commitment of the management, and proper usage of human reserves [12,13], among other considerations. The outcomes demonstrated that firms are enticed to accept green innovation practices when internally and externally motivated organizations. Internal motives combine management’s commitment and supplier’s partnership, whereas external motives are regulations and demand pressures from customers. Thus, there is a direct relationship between the two [14]; (Parmar et al., 2022). Organizations can enhance their performance through green innovation as green practices guide them to devise strategies that can help them use fewer resources, reduce the chances of harm, and create a carbon footprint in the organization [15]. The green revolution is a unique strategy that permits organizations to create new techniques and systems that help organizations enhance their performance [16,17].

Conclusion 

     This study evaluated the connotation between green product & process innovations and green innovation. Moreover, the study examined the impact of green innovation in affiliation with organizational & environmental attainment. The study’s findings explored all the hypotheses devised to meet study objectives. Moderation effects of management commitment towards greening and green human resource practices were also examined between the primary relationship. Primary responses were obtained from the mid-career and upper management of the textile sector of Pakistan. This research revealed that product & process innovations have a cogent and affirmative effect on green innovation. The findings further demonstrated that green innovation exerted an affirmative and cogent effect on organizational & environmental performance. Thus, the senior management should seriously consider green innovation projects to increase the firm’s environmental performance and the long run, sustainable competitive growth. While evaluating the moderating effect of HR practices on environmental attainment, the findings are not supported.

Abstract

The Balanced Scorecard (BSC) is a performance management tool that analyzes the relationship between tangible and intangible assets. Its ability to render business strategy visible has earned it the distinction of being a strategic management model. Sustainability has become an important part of daily and business life today. With the increasing importance given by stakeholders to environmental and social issues, companies find it necessary to evaluate sustainability activities in their performance evaluations. In order to meet this need, the Sustainability Balanced Scorecard (SBSC) emerged with the addition of a sustainability perspective or parameters to BSC. The number of academic studies pertaining to the application of BSC, which numerous companies and subjects to research have utilized, is inadequate. Due to its hierarchical structure and the necessity to evaluate numerous criteria, BSC is suitable for using multi-criteria decision-making (MCDM) methods. In this paper, an integrated approach consisting of SBSC, the Picture Fuzzy Analytic Hierarchy Process (PF-AHP), and the Objective Matrix (OMAX) method is proposed for the performance measurement of companies. The PF-AHP method has been reorganized for ease of application in determining the perspectives, strategic objectives, and Key Performance Indicators (KPIs) weights within SBSC. Additionally, the OMAX method has been employed for calculating the performance scores. This proposed approach was implemented in a factory operating in the glass industry. The SBSC created for the company includes five perspectives, 16 strategic objectives, and 34 KPIs. PF-AHP method was used to determine the weights of perspectives, strategic objectives, and KPIs to be used in the performance score calculation with OMAX. The weight of financial perspective is 0.267, the weight of the customer perspective is 0.247, the weight of the internal process perspective is 0.191, the weight of the learning and development perspective is 0.161, and the weight of the sustainability perspective is 0.134. The evaluations made by the decision-makers indicate that the company’s performance measurement still adheres to the traditional framework. The performance score of the company is 6.14 out of 10.

Introduction

The concept of sustainability, which includes economic, social, and environmental perspectives, has become an important issue in today’s world in order to cope with global challenges such as increasing poverty and climate change. This situation has brought pressure from stakeholders on terms of sustainability. At this point, the concept of corporate sustainability has emerged. Corporate sustainability is defined as meeting the needs of its direct and indirect stakeholders without compromising its ability to meet their future needs, taking into account both the administrative and operational activities of the organization (Nicoletti Junior et al., 2018). In addition, corporate sustainability also stands out as systematic management efforts for companies to voluntarily integrate environmental and social issues into management (Hansen and Schalteggar, 2016).

National and international legislation on sustainability perspectives is being developed (Kaplan and Norton, 2004), but reporting of social and environmental activities is still voluntary for companies (Huang et al., 2014). However, the importance given to the concept of sustainability will create a difference among competitors (Nicoletti Junior et al., 2018).

For a company to be successful in terms of corporate sustainability, it is not enough to be successful in a single perspective of sustainability. Corporate sustainability aims for companies to make progress in all three perspectives of sustainability (economic, environmental, and social) (Eifert and Julmi, 2022). Companies adopt environmental management systems practices to achieve the goals they set in order to meet the social and environmental expectations of stakeholders. However, these applied environmental management systems cannot fully reveal the contribution of sustainability performance to economic performance (Tsalis et al., 2013). As technological developments towards sustainability are increasing, companies need an effective tool to achieve their sustainability goals (Al-Mawali, 2023).

Conclusion

In order to succeed in their competitive environment, companies should embrace strategic management and measure the effectiveness of their management systems. The BSC has emerged as a performance measurement method to assess this success. The traditional BSC consists of four perspectives: financial, customer, internal processes, and learning and growth. With the growing importance of the concept of sustainability, which defines the measurement of environmental and social performance, the sustainability perspective has been added as the fifth perspective to the BSC, giving rise to the concept of SBSC. Since its introduction to the literature, the BSC has maintained its popularity and applied in numerous companies and investigated in various research studies. The multi-criteria nature of the BSC has led researchers to integrate it with MCDM.

In this paper, the modified PF-AHP method was used to weight the criteria in the SBSC. Additionally, the SBSC was combined with the OMAX method to express the performance numerically. Firstly, a team was selected within the company to create the SBSC, and strategic objectives, KPIs and indicators were identified through references from the literature and the company’s knowledge base. Existing data was collected for calculating the performance score, and based on this data, targets were established. The weights for SBSC perspectives, objectives, and KPIs were calculated using the PF-AHP method with the input of opinions from five DMs. The obtained weight values were then utilized in the OMAX method to determine the company’s performance score.

Abstract

The relationship between corporate income tax rates and innovation is a critical international issue. Existing studies often overlook the potential mechanisms of market competition and productivity heterogeneity. This paper addresses these gaps by integrating these elements into a unified mathematical analysis framework. Using China’s corporate income tax reform as a quasi-natural experiment, this study empirically tests the model. The findings reveal that decreases in tax rates significantly foster innovation, while increases have the opposite effect. The underlying mechanism involves market competition, with the impact of tax changes being more pronounced in firms with higher productivity. This study deepens the understanding of the nexus between corporate income tax and innovation, providing new insights to refine tax policies that promote innovative economic development globally.

Introduction

The adjustment of corporate income tax rates significantly impacts economic dynamics, influencing corporate investment strategies and innovation activities. Prior research underscores innovation’s crucial role in driving long-term economic growth and delineates the foundational contributions of tax policies to shaping economic landscapes, highlighting the substantial influences of tax adjustments on both resource allocation and innovative outputs [1–4]. Particularly, China’s monumental corporate income tax reform (CITR) in 2008, which standardized tax rates across domestic and foreign entities, provides a unique empirical setting to explore these effects due to its global economic stature and rapid development. This study critically evaluates how CITR influences corporate innovation by dissecting the direct and indirect mechanisms through which tax changes affect firms’ operational and strategic behaviors. Leveraging China’s 2008 CITR as a quasi-natural experiment, this research not only enhances understanding of the dynamic interactions between tax policies and corporate innovation activities but also informs precise, data-driven policy making to stimulate economic and innovative growth. Through a comprehensive analysis, informed by the empirical data from the CITR [5], this investigation is pivotal for its potential to guide global economic policy, given the scale of China’s economy and the extensive implications of its tax reforms.

Previous studies, such as those by Atanassov & Liu (2020) and Akcigit et al. (2022), have extensively explored how changes in corporate income tax rates impact corporate innovation directly, primarily through adjustments in funding and cost structures [6,7]. However, these studies have tended to overlook the indirect pathways through which tax policies affect innovation, particularly the mediating role of market dynamics. Additionally, there is a notable gap in understanding how these tax effects vary among firms with different levels of productivity. This oversight forms the basis of our research motivation, leading us to investigate the broader and more nuanced impacts of tax rate changes.

Results and discussion

4.1 Basic regression
Baseline results. In our baseline regression analysis, we rigorously evaluated the overarching impact of CITR on innovation, as detailed in Table 3. Initial results from columns (1)–(3), utilizing PAC as the dependent variable, reveal that the beneficial impact of a tax decrease (Tax-down#Post08) on PAC, though somewhat diminished with the progressive inclusion of control variables and bidirectional fixed effects, remains statistically significant at the 1 % level. This suggests that the reduction in corporate tax rate notably enhances corporate innovation, evidenced by a 7.31 % increase in PAC. Conversely, the detrimental impact of a tax increase (Tax-up#Post08) on PAC progressively intensifies and is also significant at the 1 % level, reflecting a substantial 9.08 % reduction in PAC due to the heightened corporate tax rate. Further analyses presented in columns (4)–(6), with PGC as the dependent variable, align with these findings. A decrease in the corporate income tax rate (Tax-down#Post08) significantly boosts PGC by 8.89 %, whereas an increase (Tax-up#Post08) correspondingly depresses PGC by 6.91 %.

Abstract

Inadequate information technology (IT) management can lead to system ineffectiveness and operational stagnation within enterprises. While the application of IT governance provides a means for companies to validate IT functionality, oversee IT operations, and mitigate IT-associated risks, there is a paucity of research examining its implications of IT governance on IT controls, particularly within the context of a firm’s Internal Audit Function (IAF). Addressing this gap in the literature, this research delves into the relationship between the characteristics of the IAF and IT governance within the IAF. It further probes the linkage between IT governance associated with the IAF and IT control activities. We analyze survey data from 414 internal auditors across various Taiwanese companies using partial least squares regression. The findings suggest that IT knowledge and internal auditing roles have a significantly positive relationship with the quality of the IAF-IT relationship and IT governance processes. Similarly, IT audit competencies exhibit a significantly positive relationship with IT governance processes. Furthermore, properly structured IT governance processes and a high-quality IAF-IT relationship demonstrate a positive association with the effectiveness of general controls. This research amalgamates and extends prior investigations into IT governance and internal auditing, underlining their critical role in successfully implementing superior IT controls.

Introduction

Post the Enron scandal in 2000, the enactment of the Sarbanes-Oxley Act in the U.S. necessitated companies to take measures that ensure the efficacy of their internal controls over financial reporting (section 404). As information technology (IT) plays a pivotal role in ensuring the precision of a company’s financial reports, IT controls have thus become an integral component of Sarbanes-Oxley compliance initiatives. To facilitate IT governance, which pertains to formalizing strategic IT decisions and essential IT oversight processes, the Information Systems Audit and Control Association (ISACA) introduced the Control Objectives for Information and Related Technology (COBIT) in 2019. However, despite these measures, public companies still report material weaknesses in IT controls. A 2021 analysis of business processes and material weaknesses among public companies registered with the U.S. Securities and Exchange Commission by the accounting firm KPMG found that IT control material weaknesses accounted for 35 % of all material weaknesses in 2020 (KPMG, 2021a).

Given their critical role in corporate and financial security, material weaknesses in IT controls pose a significant threat to the legitimacy of companies (Haislip et al., 2016). Furthermore, such weaknesses can negatively impact the quality of the information in systems, potentially misleading management and resulting in improper decisions (Li et al., 2012). Stoel and Muhanna (2011) observe that companies with material weaknesses in IT internal controls underperformed those without such issues. In an era characterized by big data and sophisticated digital technologies, material weaknesses in IT internal controls can lead to substantial losses, thus emphasizing the need for effective IT controls.

Conclusion and suggestions

5.1. Analyses and discussion
The potential for significant financial losses due to inadequate IT controls necessitates substantial attention to IT controls by businesses (Association of Certified Fraud Examiners, 2016; Stoel & Muhanna, 2011). The findings of this research illustrate a positive correlation between the involvement of Internal Audit Functions (IAFs) in IT governance and improvements in IT controls, thus offering contributions to scholarly discourse on IT governance. Our study broadens the conversation regarding the relationships among IAF quality, information security, and information security controls by integrating the dimension of IAF-IT relationship quality (Steinbart et al., 2013, 2018). Additionally, the results augment the research landscape on the interplay between IAF characteristics, their involvement in IT governance, and IT controls (Merhout and Havelka, 2008).

The IAF-IT relationship quality is significantly influenced by IT knowledge and internal audit roles. This aligns with the findings of Steinbart et al. (2013), who assert that a comprehensive understanding of information security can bolster the relationship quality between IAFs and information security. However, Steinbart et al. (2013) find no significant impact of the internal audit role on the relationship quality between IAFs and information security, which is a discrepancy with our findings. Our study further illustrates that IT knowledge, internal audit roles, and IT audit competencies significantly affect the IAF-IT governance process. This finding is in accordance with Merhout and Havelka (2008), who establish that the involvement of IAFs in IT governance is significantly associated with IT personnel and IT training or certification.

Abstract

Machine learning can be effectively used to generate models capable of representing the dynamic of production processes of small and medium-sized enterprises. These models enable the estimation of key performance indicators, and are often used for optimizing production processes. However, in most industrial applications, modeling and optimization of production processes are currently carried out as separate tasks, manually in a very costly and inefficient way. Automated machine learning tools and frameworks facilitate the path for deriving models, reducing modeling time and cost. However, optimization by exploiting production models is still in infancy. This work presents a methodology for integrating a fully automated procedure that embraces automated machine learning pipelines and a multi-objective optimization algorithm for improving the production processes, with special focus on small and medium-sized enterprises. This procedure is supported on embedding the generated models as objective functions of a reference point based non-dominated sorting genetic algorithm, resulting in preference-based Pareto-optimal parametrizations of the corresponding production processes. The methodology was implemented and validated using data from a manufacturing production process of a small manufacturing enterprise, generating highly accurate machine learning-based models for the analyzed indicators. Additionally, by applying the optimization step of the proposed methodology it was possible to increase the productivity of the manufacturing process by 3.19 % and reduce its defect rate by 2.15 %, outperforming the results obtained with traditional trial and error method focused on productivity alone.

Introduction

Nowadays, generalized adoption of the new manufacturing paradigms involves the assimilation of key technologies such as intelligent data analysis and machine learning (ML), among others, aiming at the digital transformation of enterprises [ 46 , 56 ]. This digital transformation is crucial to keep up with the competition, especially in the case of small and medium-sized manufacturing enterprises (SMEs). One contemporary target is to create a highly reconfigurable, decentralized, dynamic, self-organizing, and real-time (or near real-time) decision-making infrastructure enabling to analyze customer expectations and reach their targets [ 20 ]. By applying these transformations, SMEs should be capable of monitoring and improving their key performance indicators (KPIs) [ 67 ]. However, in practice, SMEs face several difficulties in applying these technologies, mostly related to the transition and maintenance costs, innovation complexity, and personnel training [ 50 ]. Additionally, due to limited human and computational resources, time constraints and complexity of the optimization processes, SMEs usually focus their efforts on a single productivity objective, despite being more desirable to consider and optimize multiple objectives, which leads not only to more efficient but also a more sustainable and environmentally friendly production. Due to these obstacles, a large number of SMEs don’t count yet with the necessary tools to continue with their digital transformation. In this context, the development of tools for generating useful information and smart recommendations of production systems in SMEs is almost mandatory in a high-competitive market and has a large number of potential adopters [ 12 , 42 ].

Conclusions

This work presents an automated machine learning methodology for optimizing manufacturing processes in SMEs by combining the standard tasks of AutoML tools, such as data preprocessing, feature selection, model training, and hyperparameter optimization, with preference-based multi-objective optimization. For this purpose, the basic AutoML workflow is used to generate models for each of the KPIs of the production process and, then, a new automated optimization step is introduced for using the generated models as objective functions, resulting in optimal parametrizations of the production process. By simplifying the way of interacting with this methodology, it is possible that manufacturing SMEs with low availability of highly-skilled personnel or limited computing power can benefit from advanced technologies making easier the digitalization and application of Industry 4.0 paradigm.

The methodology was implemented and validated in a production process where, firstly, the most relevant features for modeling each key performance indicator were automatically selected based on the Pearson’s correlation coefficient, allowing to reduce the dimensionality of the data. Then, models of key performance indicators were generated and their architecture/hyperparameters optimized. Generated models were compared to models obtained through other AutoML frameworks offering similar results, with values of MSE = 2.585 and R2 = 0.999, and MSE = 6 × 10−5 and R2 = 0.735, respectively. Finally, the models were used as objective functions in the R-NSGA-II algorithm for finding optimal parametrizations of the production process, yielding an improvement in both KPI, reducing scrap by 2.15 % and increasing throughput by 3.19 %, with regard to the baseline of conventional parametrization considering only a single productivity target. These improvements contribute to a higher production rate while, at the same time, the number of defective components is reduced, which underscore the potential of the proposed methodology to significantly boost overall efficiency and profitability for SMEs by optimizing their production processes more holistically.

Abstract

This study aims to analyze the role of green transformational leadership and human resource management in organizational performance with the mediating effect of green innovation. The study also proposes the moderating role of commitment to management change. The study used a quantitative research method with a random sampling technique and surveyed 235 managers from the Cement industry across Pakistan. The statistical techniques used to assess the study’s hypotheses were partial least squares structural equation modeling (PLS-SEM) and SPSS. The findings show that green transformational leadership and HRM positively impact organizational performance, with green innovation partially mediating effect. Furthermore, affective commitment to management change strongly moderates the relationship between green innovation and organizational performance. In addition, the proposed relationship is based on the resource-based view (RBV) theory. Therefore, Managers and executives should adopt green principles and practices and build an environmentally friendly culture that encourages employees to engage in green behaviors and activities to improve organizational performance. To successfully implement green practices in a developing country like Pakistan, the Cement sector must understand how the cement industry improves its performance and whether employees demonstrate green innovation.

Introduction

In recent decades, scholars have recognized the significance of Green HRM and green leadership in shaping the operational practices in modern firms [1]. In this regard, and for the efficient and effective management of operations, technology advancements, and rising stakeholder pressure have motivated firms to discover and implement cost-effective and long-term methods to improve organizational performance [2,3]. Enhancing performance has emerged as a primary problem for businesses, requiring the effective utilization of organizational resources and adopting environmentally sustainable practices in Green HRM and eco-friendly activities [4]. The significant rise in globalization and social responsibilities demands justifications for adopting optimal Green HRM practices and integrating data analytics to enhance decision-making [5,6]. To ensure the sustainability and advancement of green business activities, dominant policies encompassing Green HRM, innovation, leadership, environmental quality, and social well-being are essential. Businesses can mitigate their adverse environmental and societal impacts by implementing GHRM, green leadership, and innovative practices [7]. Green HRM comprises three crucial components: motivating employees to enhance their engagement, providing them with green opportunities, and assisting in developing their green skills, which are essential abilities [8]. The underlined components of Green HRM address staff recruitment, selection, development, and training with an environmentally conscious focus; the potential for adopting green practices encompasses employee involvement and green leadership [9].

Discussion

Our empirical work has examined the interactions among variables such as green transformational leadership, HRM, innovation, affective commitment to management change, and organizational performance of the cement industry in Pakistan using theoretical insights from RBV theory. Our study has thoroughly explained all three hypotheses and their sub-hypotheses, showing that the performance of an organization can be improved through green transformational leadership and HRM. This improvement is achieved through the mediating role of green innovation and the moderating role of affective commitment to management change. There is a strong and positive association between all three hypotheses.

Our study found that green transformational leadership influences firm performance by establishing a clear environmental vision, motivating individuals to adopt green practices, increasing employee commitment to achieving environmental goals, and encouraging staff to create and disseminate new green practices; these results are similar to past findings [41]. The results demonstrate that transformational leaders who foster their employees’ green practices to motivate them have excellent potential to execute green prospects effectively.

Abstract

The project selection criteria are required for an effective project lifecycle, and the selected projects should correspond with organizational aims and objectives; yet, the organization lacks severely in its ability to act decisively on the problem. Hence, this study aims to highlight the best project selection criteria that are related to organizational success. In this manner, a quantitative survey approach was used, with closed-ended questions distributed to employees in Pakistan’s construction industry. The relationship between project selection criteria and organizational performance was evaluated using structural equation modelling in SmartPLS through measurement and structural models. The findings reveal that a significant positive correlation was found for all the project selection criteria, except for the financial criteria. Furthermore, the results demonstrate that project selection criteria are critical for organizational success; hence, the findings will assist organizations in making better project selection decisions.

Introduction

Organizations choose projects based on the many potential prospects that is why it is regarded as one of the most difficult decisions to make [1] . There are various aspects associated with project selection, such as cost, financial, and time-based criteria [2] and any mistreatment may result in an outbreak of an obstacle [3] , [4] . An organization’s project selection problems develop when there are inadequate resources to take advantage of; hence, project selection criteria are devised to select the cost-effective projects [5] , [6] . A significant part of project management-related research revolves around organizational performance [7] which is critical for the success and survival of today’s numerous commercial and economic operations due to market rivalry for cash, consumers and inputs [8] . Organizational performance is central to today’s industrial activity [7] , [9] and is considered a collection of all the businesses accomplishments which are then committed towards a structural goal for organizations in a given period. The notion of organizational performance is linked with the persistence and achievements of a firm [8] , [10] where several constructs have been linked and studied afterwards [11] . Organizational performance measures comprise several financial measures including budgeting, cash flow cash flow, cost-cutting, and profitability [12] .

The criteria used to select projects can vary depending on the organization’s goals, industry, and available resources. The relationship between project selection criteria and organizational performance is complex and multidimensional [13] , [14] . The influence of project selection on organizational performance has been studied in various fields, including information technology, engineering, and construction. A few studies suggest that selecting projects based on strategic alignment and financial feasibility can improve project outcomes and overall organizational performance [15] , while other studies have found that selecting projects based on risk and organizational capability can lead to better project outcomes and performance [16] . Hsu [17] investigated the relationship between project selection and organizational performance in the construction industry and found that project selection criteria, such as financial viability, strategic fit, and technical feasibility, had a significant impact on project success. Similarly, Lind and Culler [18] investigated that the effect of project selection on the performance of information technology projects and found that effective project selection was positively associated with project success and organizational performance. Beldek, et al. [19] studied the financial, social, technical, and risk criteria in the context of Turkish industries, however, the study was limited to the number of selection factors. Hence, adding upon on their outcomes, the current research has added two more criteria namely environmental and management support criteria in the context of Pakistani industries as supported by Bhatti, et al. [11] .

Conclusion

The relationship between project selection criteria and organizational performance is complex and multidimensional. Few studies have investigated this relationship in service industries and identified institutional, financial, risk and technical criteria as key criteria for project selection, however, still, there is a gap in the body of knowledge that has been covered by this work. Hence, this research aimed to investigate the impact of project selection criteria on organizational performance through a quantitative analysis. Based on the assessment, the general significant finding is the positive relationship between project selection criteria and organizational performance, which confirms previous studies’ findings emphasizing the crucial role of project selection criteria for individual and organizational performance. Overall, the results support the constructs and validate the positive relationships between the five project selection criteria mentioned above and organizational performance. Only the relationship between financial criteria and organizational performance was insignificant in this study. The findings of this research can aid higher-ups in construction organizations in better project selection for their organizations. This research contributes to previous literature by adding two more criteria namely environmental and management support criteria in the context of the construction industry.

Abstract

Exploiting the global financial crisis of 2007–08 as an exogenous shock that resulted in a significant decline in the ownership of foreign institutional investors (FIIs) in the Indian equity market, we find evidence of a causal link between FIIs’ ownership and different dimensions of board monitoring. Specifically, the empirical results suggest that higher FIIs ownership leads to lower board size, busyness, network size, CEO power, CEO pay, and improved board diligence. However, we also document a negative link between FIIs’ ownership and board independence, indicating that FIIs do not view independent directors as effective monitors. In terms of implications, our results suggest that improved board monitoring, induced by higher FIIs’ ownership, leads to higher firm valuation and innovation activities.

Introduction

Although boards are recognized as a powerful internal corporate governance mechanism, their effectiveness can vary significantly (Adams et al., 2010, Tung, 2011).1 This has spurred research into the underlying causes and strategies for enhancing board effectiveness. Implementing effective internal corporate governance mechanisms that enhance board effectiveness is associated with reducing information asymmetry and agency risks (Jensen and Meckling, 1976, Renders et al., 2010). Existing literature provides support to this conjecture (e.g., Brick and Chidambaran (2010) and Conheady et al. (2015)) by showing a positive association between board monitoring, its effectiveness, and firm value.2

Our study adds to this growing area of literature by examining the influence of foreign institutional investors (FIIs) as a source of external corporate governance mechanism on the internal corporate governance mechanism, namely board effectiveness. Gillan and Starks (2003) offer a theoretical argument that growth in FIIs’ ownership should result in improved board monitoring and overall governance of the host firm.3 The literature highlights the role of FIIs in shaping corporate governance due to their substantial ownership in firms. FIIs are able to monitor board and management activities through voting rights and incentives via cash-flow rights. For instance, Bena et al., 2017, Ferreira and Matos, 2008, and Aggarwal et al. (2011) show that FIIs’ monitoring efforts are primarily directed toward enhancing long-term firm performance, demonstrating that FIIs can curb managers’ incentives to overinvest and improve governance, particularly in countries with weak shareholder protection. FIIs act as independent monitors, being non-domestic investors with limited ties to the host firms, thereby reducing agency costs and improving board monitoring quality (Gillan and Starks, 2003, Tsang et al., 2019). They are also more likely to exit investments in firms with subpar governance (Leuz et al., 2009). FIIs’ independence from local political pressure allows them to perform arms-length monitoring and resist decisions that do not maximize shareholder value. Moreover, their deep understanding of global corporate governance practices, experience, demand for information disclosure, and superior monitoring technologies empower them to foster better governance practices and board monitoring (Grinblatt & Keloharju, 2000). Unsurprisingly, anecdotal evidence suggests that FIIs influence board monitoring by removing directors whose decisions do not maximize their wealth and expropriate minority shareholders.4

Conclusion

One of the key trends in the global financial market during the financial crisis of 2007–08 was the “flight of capital” from emerging markets to developed economies. India, one of the largest emerging economies, also witnessed a substantial outflow of foreign capital in the aftermath of the crisis. From an empirical identification point of view, this crisis represents an unexpected negative shock to FIIs’ ownership in India, making it an ideal set-up to investigate the role of FIIs in influencing the monitoring role of boards. In this study, we focus on the four years pre-crisis and post-crisis beginning in 2008 and use different proxies of board monitoring to evaluate the impact of FIIs on the board monitoring of the firms in which they invest.

The literature on corporate governance notes that FIIs, being informed and sophisticated investors, have the incentive and ability to improve board monitoring. Our study adds to this literature by providing causal evidence of FIIs’ influential role in enhancing the effectiveness of board monitoring. Consistent with economic arguments, the results show that firms with higher FIIs’ ownership are associated with lower board size, busyness, network size, CEO power, CEO pay, and higher board diligence. Interestingly, we also find that FIIs prefer lower board independence in India. However, our result on board independence is counter-intuitive but not surprising, given the empirical evidence that managers in emerging markets may appoint directors independent from the regulators’ point of view. However, they are still connected and sympathetic to the existing management. We also find that FIIs improve the performance of the firms through their improved board monitoring role. Specifically, we find that the enhanced board monitoring by FIIs improves both firm value and corporate innovation measures.

Abstract

This study presents a novel financial performance forecasting method that combines the threshold technique with Artificial Neural Networks (ANN). It applies the threshold regression method to identify the factors within the board of directors that influence the financial performance of traditional industries in Taiwan. The findings indicate that the ANN method effectively predicts financial performance by using relevant board structure data. Furthermore, the empirical results suggest that boards with more members demonstrate increased profitability. Additionally, a more significant presence of board members with accounting expertise contributes to more consistent profits. In contrast, an increased presence of members with financial expertise has a more pronounced impact on profitability.

Introduction

Traditional industries, including sectors such as metal machinery, the chemical industry, and subsistence industries, have played pivotal roles in Taiwan’s economic development, trade, and industrial evolution over the years. However, in recent years we have witnessed the emergence of economic powerhouses and the rapid progress of regional economic and trade partnerships like ASEAN, resulting in significant changes to the global economic and trade landscape. As a result, these industries are now operating within a new and highly competitive environment. In this era of rapid transformation, these industries must identify the essential factors that will enable them to maintain and enhance their positions on the international stage.

Investors seek to achieve multiple objectives through the election of a company’s board of directors. They aim to enhance corporate governance, safeguard the rights and interests of shareholders, and anticipate that the board will contribute expertise to improve the decision-making prowess of the board itself. Ultimately, this is expected to enhance the company’s overall performance. Consequently, whether the board of directors can positively impact corporate performance has remained a significant research focus in management and academia.

While many studies have explored the relationship between the board of directors’ characteristics and company performance, a notable need exists for more in-depth articles examining the professional backgrounds of the board members and CEOs and how their leadership qualities influence financial performance. This gap in research underscores the need for a more comprehensive understanding of the intricate dynamics between leadership, professional expertise, and corporate financial outcomes.

Conclusions

The research results show that, for companies in traditional industries that are listed on the Taiwanese stock market, the composition of the board of directors impacts the business’s financial performance.

Boards with more members may have a greater capacity for overseeing corporate governance. A greater representation of board members with accounting expertise tends to lead to more consistent corporate profits, whereas an increased presence of members with financial expertise results in more pronounced profit fluctuations. Additionally, in the case of companies with lower profits, managers may face pressure from loan contracts or receive limited attention from investors, potentially leading to heightened practices related to profit management. Managers may aim to maximize their benefits. At this time, the return on assets is high, and the managers obtain the expected benefits. Thus, to restrain the profit management behavior of managers, enterprises need to have a broader view and have specific judgments and analyses in each enterprise to build organizational structure and procedures. The operation of the board of directors is more effective because of the industry’s characteristics, the enterprise’s size, the capital structure, and the performance of the enterprise. Finally, this study used deep learning and big data techniques to build an Artificial Neural Network (ANN) model that specializes in predicting the ROA of companies based on most factors related to board structure, with a prediction accuracy of over 78%.

Abstract

Artificial intelligence (AI) and other advanced technologies are increasingly recognized as essential catalysts for enhancing productivity due to their capability to transform nearly all operations within and outside firms. However, the empirical research on how AI assimilation may promote firm-level outcomes such as absorptive capacity (AC), customer agility (CA), and firm performance (FP) is still in its infancy. Drawing from the dynamic capability view and using 417 valid responses collected through cross-sectional methods from small and medium-sized enterprises (SMEs) in Lebanon, this study examines the effect of AI assimilation on firm performance. The mediating roles of AC and CA were investigated. The moderating role of organizational agility (OA) was also explored. The findings support the hypothesized assumptions that continual advancement of technology evolves the industrial organizations’ performance with CA and AC as parallel mediators, partially mediating the link between AI assimilation and FP and OA as a moderator, moderating the positive relationship between AI and CA and between AI and FP. The findings provide crucial insights for practitioners and advance the dynamic capability view framework. They provide compelling evidence that enriches the understanding of AI assimilation, demonstrating its positive impact on critical organizational outcomes and yielding performance benefits for SMEs.

Introduction

The modern era requires modern solutions, and existing businesses are deploying technologies to meet customers’ expectations [1]. Customers want quick responses, which necessitates the use of fast-moving systems and responsiveness. From this standpoint, firms are introducing AI technologies to establish a CA system that can quickly respond to an ever-changing business environment. Through such technologies, firms can improve problem-solving abilities, boost productivity and efficiency in customer service and other fields [2], and improve their performance and productivity [[3], [4], [5], [6], [7], [8], [9]].

The use of AI technology to assist organizations in attaining and maintaining a competitive edge relies on the complete assimilation of such technology throughout business processes [10]. This is due to IT assimilation’s crucial role in enabling end-to-end business operations to develop and maintain business value [10,11]. Existing literature has acknowledged that it can be counted on to enhance organizational performance [12] and foster supply chain agility and business value [13]. However, most of the existing literature focuses on only two facets of assimilation: the crucial factors of assimilation [10,14] and IT-based outcomes [10,15].

Conclusion, limitations, and future research

6.1. Conclusion
This article contributes to emerging studies on AI at the organization level by examining the effect of AI assimilation on FP, which so far has received limited attention, particularly in the context of developing economies, which remains under-researched, calling into question the generalizability of the findings and theories in this research area. Our study is an initial effort to address this issue. This study also offers insights into the mediating role of CA and AC on the link between AI assimilation and FP. We found that OA moderates the link between AI assimilation and CA and AI assimilation and FP in the SME context.

6.2. Limitations and directions for future research
This article has some limitations that future studies should take note of. First, the sample obtained is limited to SMEs in Lebanon because of cultural and technological disparities between nations; the conclusion of the present research may not be generalizable to other nations. Hence, future research should collect data from multi-cultural nations to boost the generalizability of the findings. Second, a complimentary qualitative or longitudinal study that gives more information about the causal relationship between AI assimilation and FP via CA could enhance our understanding of the topic. Third, several contextual constructs, such as uncertainty, structure, and culture, could exist between AI assimilation and FP and moderate this relationship. Finally, research regarding AI technologies in obtaining business value is still very scarce in developing economies; more empirical studies are required to broaden our understanding.

Abstract

     The deployment of Artificial Intelligence (AI) has been accelerating in several fields over the past few years, with much focus placed on its potential in Business-to-Business (B2B) marketing. Early reports highlight promising benefits of AI in B2B marketing such as offering important insights into customer behaviors, identifying critical market insight, and streamlining operational inefficiencies. Nevertheless, there is a lack of understanding concerning how organizations should structure their AI competencies for B2B marketing, and how these ultimately influence organizational performance. Drawing on AI competencies and B2B marketing literature, this study develops a conceptual research model that explores the effect that AI competencies have on B2B marketing capabilities, and in turn on organizational performance. The proposed research model is tested using 155 survey responses from European companies and analyzed using partial least squares structural equation modeling. The results highlight the mechanisms through which AI competencies influence B2B marketing capabilities, as well as how the later impact organizational performance.

Introduction

     The deluge of data combine with the availability of processing power and storage on digital devices has created a renewed interest in artificial intelligence (AI) in multiple fields over the past years (Enholm et al., 2021). Intense competition among organizations all over the world has also accelerated the need to deploy AI in order to gain an edge over rivals (Ransbotham et al., 2018). AI is not perceived by most C-level executives as a core competence that organizations must foster to remain competitive in the long-run (Kietzmann & Pitt, 2020). One key area of AI use within organizational operations has been B2B marketing (Mikalef et al., 2021). Intelligent solutions to augment B2B marketing capabilities are necessary in a complex business environment, as B2B operations often deal with massive informational complexity and the requirement to make quick decisions. In this regard, AI has the potential to revolutionize how conventional activities are performed due to the ability to process ever-increasing volumes of data, and provide rich insights on key business partners and customers (Bag et al., 2021). Furthermore, AI applications have been suggested to enable automation of many manual processes which can help alleviate bottlenecks and increase operational efficiency in B2B operations (Paschen et al., 2020). In fact, a recent survey on business executives conducted by Garner indicated that the majority believe that AI is likely to be a key development in their business within the next years (Shin & Kang, 2022).

Analysis

     To examine the validity and reliability of our proposed research model, we built on a partial least square based structural equation modeling (PLS-SEM) analysis. We used SmartPLS 4 as the software to run analyses (Ringle et al., 2015). For the type of analysis we conduct PLS-SEM is deemed as an appropriate technique as it allows the examination of the relationships between dependent, independent, and mediating variables (Hair et al., 2011). As PLS-SEM is a variance-based approach, it allows for (i) flexibility concerning normality, (ii) use of reflective and formative constructs, (iii) analysis of models with smaller samples, and (iv) the potential of theory building (Nair et al., 2017). Over the past years, PLS-SEM has widely been used for the analysis of models with complex relationships between constructs in several subject areas (Ahammad et al., 2017, West et al., 2016). Furthermore, PLS-SEM enables the identification of indirect and total effects, which makes it making it possible to not only simultaneously assess the relationships between multi-item constructs, but also to reduce the overall error associated with the model (Astrachan et al., 2014). The sample of 155 responses of this study surpasses both the requirements of: (1) ten times the largest number of formative indicators used to measure one construct, and (2) ten times the largest number of structural paths directed at a particular latent construct in the structural model (Hair et al., 2011). Furthermore, as the research model we examine builds on exploratory theory building instead of confirming, we consider that PLS-SEM is the best alternative.

Abstract

     This paper investigates the transformation of information sharing and multi-tier supply chain management (MSCM) from the perspective of small and medium-sized enterprises (SMEs) in the context of Industry 4.0. SMEs are typically sharing less information digitally, are less active in conducting MSCM, but must be integrated in digital supplier networks to achieve supply chain transparency or traceability. For this purpose, this paper empirically analyzes SMEs from the manufacturing industry in Austria and Germany are investigated regarding information sharing and MSCM. The responses are analyzed to test three hypotheses, using 81 replies from manufacturing SMEs in Austria and Germany. The three hypotheses investigate the upstream and downstream MSCM practices of SMEs as well as which kind of information is shared with and by SMEs. The results help to understand and reveal potential barriers for information sharing besides technical possibilities to SMEs in order to be actively involved in MSCM.

Introduction and background

     Digital information sharing across supply networks represents a relevant topic since it enables data-based potentials such as in the concept of Industry 4.0 or for using Artificial Intelligence [1,2]. However, many companies do not recognize the importance of data availability and data quality yet. This is particularly true for small and medium-sized enterprises (SMEs), which are often impeded by missing trust, standardization, data exchange interfaces, or technological readiness [3-6] and do not recognize the relevance of digital technologies for their business [7]. Likewise, digital information sharing in multi-tier supply chain management (MSCM) is still in its early stages of development.

     This research investigates information sharing and subsequent data analysis in MSCM, focusing on SMEs. It aims at determining basic characteristics, e.g., size and vertical network position (up- or downstream), of early adopters of data-based MSCM. Raw material suppliers and upstream suppliers have a higher probability of being pressured by large buyer companies, which demand information sharing [2]. Hence, we argue that data based MSCM is more likely to be implemented in the upstream supply chain (H1). Moreover, five categories of information were evaluated if they are shared with SMEs or by SMEs (H1a and H1b): a) inventory level, b) state of delivery, c) expected delay, d) forecast data, and e) production plan

Discussion and Conclusion

Interpretation of Results

     From the interviews, we derived the motives of SMEs for carrying out MSCM and to share data. Mainly, SMEs aim to serve customer requests and to be able to fulfil their own production plan. This is in line with Arend and Wisner [14], who found that conducting supply chain management (SCM) is negatively related to SME performance since they do not implement SCM thoroughly enough or do it because they are forced by more powerful partners while not having benefits for themselves. Hence, before SMEs are able to conduct profitable MSCM, data exchange must be established with mutual benefits for supply chain partners [2]. In addition, we found that not all kinds of information may be relevant for partners in the supply network. For SMEs, binary notes would often suffice, e.g., whether the delivery is on time or not.

     In summary, it can be argued that SME must invest primarily in the area of data structures and availability in order to create the basis for data-based analyses. Particularly regarding network partners that are not directly connected to the company, there is a great need for data so that the entire structures can be analyzed and optimized. It is noticeable that there are differences between the companies in many areas of analysis, which result from the size of the company. Differences are particularly noticeable regarding data availability and the application of analyses in the supply chains. Larger companies are better equipped in terms of data availability and perform more analytics – presumably because they work more intensively with data and therefore also rate the potential higher than smaller ones. Likewise, larger companies have more power in the network and can therefore de facto secure better data availability from their network partners.