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Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

In academia, assessment is often narrow in its focus on research productivity, its application of a limited number of standardised metrics and its summative approach aimed at selection. This approach, corresponding to an exclusive, subject-oriented concept of talent management, can be thought of as at odds with a broader view of the role of academic institutions as accelerating and improving science and scholarship and its societal impact. In recent years, open science practices as well as research integrity issues have increased awareness of the need for a more inclusive approach to assessment and talent management in academia, broadening assessment to reward the full spectrum of academic activities and, within that spectrum, deepening assessment by critically reflecting on the processes and indicators involved (both qualitative and quantitative). In terms of talent management, this would mean a move from research-focused assessment to assessment including all academic activities (including education, professional performance and leadership), a shift from focus on the individual to a focus on collaboration in teams (recognising contributions of both academic and support staff), increased attention for formative assessment and greater agency for those being evaluated, as well as around the data, tools and platforms used in assessment. Together, this represents a more inclusive, subject-oriented approach to talent management. Implementation of such changes requires involvement from university management, human resource management and academic and support staff at all career levels, and universities would benefit from participation in mutual learning initiatives currently taking shape in various regions of the world.

This paper aims to examine the mediating role of knowledge sharing (KS) on the relationship between the transformational leadership (TL) and innovation in banks.

Quantitative analysis was conducted by using the structural equations modeling with AMOS 24 to examine the influence of the mediating role of KS on the TL–innovation relationship. Data were collected from 310 employees at 27 banks in Lebanon.

The research highlights that leaders exhibiting transformational behavior were able to promote knowledge-sharing culture that enhances the generation of new ideas, products and processes. The findings confirmed that KS mediates the association of TL and innovation.

The findings point to how TL mobilizes employees to engage in innovative products and processes by encouraging a knowledge-sharing culture.

The research findings advance the understanding of how TL stimulates innovation and highlights the benefits gained by cultivating KS to generate more innovative outcomes.

Quality environment is argued to be essential for ensuring food security. The effect of environmental degradation on agriculture has thus gained the attention of researchers. However, the analyses of aggregate and sectoral effect of carbon dioxide emissions on agricultural development are limited in the literature. Consequently, this study examines the effect of aggregate and sectoral carbon emissions on Ghana's agricultural development.

Time-series data from 1971 to 2017 are employed for the study. Regression analysis and a variance decomposition analysis are employed in the study.

The results show that the country's agricultural development is negatively affected by aggregate carbon emission while financial development, labour and capital increases agricultural development. Further, industrial development and emissions from transport sector, industrial sector and other sectors adversely affect Ghana's agriculture development. The contribution of carbon emission together with other explanatory variables to the changes in agricultural development generally increases over the period.

This study analyses the aggregate and sectoral carbon dioxide emission effect on Ghana's agricultural development.

Companies are increasingly implementing business process management systems (BPMSs) to support their processes. However, there is a gap in the literature regarding whether users also use BPMSs to manage the knowledge needed for processes to be completed. This study aims to analyze the factors that cause users to use BPMSs to manage the knowledge required in business processes.

The paper proposes an original model that integrates two successful information system models applied to BPMSs and knowledge management systems. To test the hypotheses derived from this new model, data were collected from 242 mature BPMS users from 12 Spanish and Latin American companies. Structural equation modeling with AMOS was used to examine the model.

Users’ perceived usefulness of a BPMS when using it for knowledge management (KM) is the only factor influencing them to use it for KM.

This study has practical implications for managers wishing to successfully implement a BPMS to support processes and for employees to use the knowledge embedded in the tool. The latter will only happen if users perceive the tool’s usefulness for KM.

To the best of the authors’ knowledge, this model is the first empirically validated model to successfully analyze BPMS users’ tendency to use BPMSs as a tool to support necessary KM in processes.

This study aimed at investigating employee training, employee participation and organizational commitment (OC) and the moderating effect of workplace ostracism among bank employees.

The study used a descriptive and cross-sectional design with the aid of a standard scale constructed into a questionnaire. Cluster, convenience and simple random sampling techniques were used to select 1,067 respondents, of which 870 were deemed fit for the study. The theories underpinning the study were the social exchange theory (SET) and social identity theory (SIT). Four hypotheses were developed and tested using hierarchical multiple regression analysis, and moderation using PROCESS macro.

The study found that employee training and employee participation had a significant positive relationship with organizational commitment, while organizational ostracism had a significant but negative relationship with organizational commitment among bank employees. The study also found that workplace ostracism moderated the relationship between organizational climate and organizational commitment The study recommended that organizational commitment requires management training their workforce, allowing employee participation in decisions, and minimizing or outrightly eradicating the practice of organizational ostracism. It is, therefore, concluded that workers place great value on training and participation in decision-making and frown at organizational ostracism.

This paper fills in the gaps left by the paucity of empirical investigation of the moderating role that workplace ostracism plays between employee training, employee participation and organizational commitment – a feat that is lacking in developing countries. It serves as a reminder to management to prevent or entirely eliminate workplace ostracism to allay an employee's impression of being a threat to an organization when commitment is low.

This study aims to empirically investigate the connection between Islamic finance and economic growth in Turkey using the endogenous growth model.

It applies quantile regression with the Markov chain marginal bootstrap resampling technique by adopting total Islamic financing as the main exogenous explanatory factor in the endogenous growth model, while the gross domestic product (GDP) is employed as a measure of economic growth. The sample consists of all full-fledged participation (Islamic) banks operating in Turkey spanning from 2013Q4 until 2019Q4. The study uses academic literature, official financial reports from the Participation Banks Association of Turkey, REDmoney Group, Islamic Financial Services Board (IFSB) and the International Monetary Fund (IMF) database.

The results show that Islamic finance is promoting economic growth in Turkey, which mirrors the success of the New Turkish Economy Program (2019–2021) which aims at boosting economic growth by enhancing the Islamic finance share in the Turkish banking sector and the global market.

Turkey has a dual banking system (conventional and participation (Islamic)) and both can influence the country's real economy. This study is limited to the influence of Islamic banking on Turkish economic growth. The study also restricts its size and coverage from 2013Q4 to 2019Q4, to cover the years over which data for all variables included in the research are available.

This paper suggests the adoption of the Turkish successful experiment as a path to reach economic growth by increasing the Islamic finance share in the banking industry for countries that seek to promote economic growth by Islamic finance, as the findings of this paper support.

This study is the first that examines the influence of Islamic finance on economic growth under a new theoretical framework of the endogenous growth model in Turkey using a robust non-parametric approach.

To help ensure that children with social and behavioral health problems get the support services they need, organizations collaborate in cross-sectoral networks. In this article, the authors explore and compare the structure of these complex child service delivery networks in terms of differentiation (composition) and integration (interconnection). In particular, the authors investigate the structure of client referral and identify which organizations are most prominent within that network structure and could therefore fulfill a coordinating role.

The authors used a comparative case study approach and social network analysis on three interorganizational networks consisting of 65 to 135 organizations within the Dutch child service delivery system. Semi-structured interviews with the network managers were conducted, and an online questionnaire was sent out to the representatives of all network members.

The networks are similarly differentiated into 11 sectors with various tasks. Remarkably, network members have contact with an average of 20–26 organizations, which is a fairly high number to be handled successfully. In terms of integration, the authors found a striking diversity in the structures of client referral and not all organizations with a gatekeeper task hold central positions.

Due to the scarcity of comparative whole network research in the field, the strength of this study is a deeper understanding of the differentiation and integration of complex child service delivery systems. These insights are crucial in order to deliver needed services and to minimize service silos and fragmentation.

There is a gap in knowledge about the Gulf Cooperation Council (GCC) because most studies are undertaken in countries outside the Gulf region – such as China, India, the US and Taiwan. The stock market contains rich, valuable and considerable data, and these data need careful analysis for good decisions to be made that can lead to increases in the efficiency of a business. Data mining techniques offer data processing tools and applications used to enhance decision-maker decisions. This study aims to predict the Kuwait stock market by applying big data mining.

The methodology used is quantitative techniques, which are mathematical and statistical models that describe a various array of the relationships of variables. Quantitative methods used to predict the direction of the stock market returns by using four techniques were implemented: logistic regression, decision trees, support vector machine and random forest.

The results are all variables statistically significant at the 5% level except gold price and oil price. Also, the variables that do not have an influence on the direction of the rate of return of Boursa Kuwait are money supply and gold price, unlike the Kuwait index, which has the highest coefficient. Furthermore, the height score of the variable that affects the direction of the rate of return is the firms, and the accuracy of the overall performance of the four models is nearly 50%.

Some of the limitations identified for this study are as follows: (1) location limitation: Kuwait Stock Exchange; (2) time limitation: the amount of time available to accomplish the study, where the period was completed within the academic year 2019-2020 and the academic year 2020-2021. During 2020, the coronavirus pandemic (COVID-19), which was a major obstacle, occurred during data collection and analysis; (3) data limitation: The Kuwait Stock Exchange data were collected from May 2019 to March 2020, while the factors affecting the stock exchange data were collected in July 2020 due to the corona pandemic.

The study used new titles, variables and techniques such as using data mining to predict the Kuwait stock market. There are no adequate studies that predict the stock market by data mining in the GCC, especially in Kuwait. There is a gap in knowledge in the GCC as most studies are in foreign countries, such as China, India, the US and Taiwan.

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.