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The significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning artificial intelligence (AI) and machine learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field.

In this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology to analyze related papers.

In business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis.

While this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024.

This work addresses a significant gap by employing a pioneering approach to introduce challenges in BPM alongside AI/ML techniques and integrated tools. Hence, it offers comprehensive guidelines that elucidate the alignment between ML methods and solutions to current challenges across the BPM life-cycle, including process enhancement and process improvement. Additionally, by detailing various aspects of the life-cycle phases and highlighting ML technique characteristics, this research demonstrates potential approaches for future exploration, thereby enhancing applicability for both process analysts and researchers in this context.

The local food tourism in Pakistan is increasing rapidly, and it attracts scholars to determine the factors affecting local food tourists' buying choices. Particularly, the authors aim to investigate the role of food consumption values on predicting domestic tourists' attitude toward local food and its effect on the intention to try local food with the moderating effect of personality traits (neophobia and neophilia).

The authors tested the study model on 250 completed responses from local food tourists. They collected the data from three tourism locations (Islamabad, Rawalpindi and Peshawar) in Pakistan. Their study utilizes the consumption value theory within the limits of Pakistan's local food tourism.

The empirical findings show that consumption values, such as price, emotion, interaction, epistemic value, location value and variety value, effectively explain the domestic tourists' attitude toward local food. The authors further report that food neophilia strengthens the local tourists' positive reception toward the local food. However, food neophobia weakens the direction between local tourists' attitude toward local food and the intention to try local food.

This study provides insights pertaining to tourists' local food consumption values (LFCVs) to a local destination owner and marketing manager to strategically work on LFCVs that are crucial for domestic tourists to derive their intention to try local food. Practitioners should work on domestic tourists who possess food neophobia trait and enquire them for their rejection or avoidance of a particular local destination. This will enable practitioners to bring innovation and development in the local destination, which ultimately promote local food tourism.

This study is the first to incorporate the variety and local value in tourists' LFCVs to predict local tourists' attitude toward local food. Additionally, the authors contribute to local food tourism by empirically studying the moderating role of personality traits (food neophilia and food neophobia) to examine the direction between local tourists' attitude and intention to local food.

As perceived corporate social responsibility (CSR) has become one of the long-term sustainable development strategies for many companies, this paper investigates the interrelationships between sustainable values (SVs), perceived CSR and customer behaviour in the restaurant industry.

To gather data needed to meet the study aims, we conducted an online survey of restaurant patrons in China, and used partial least squares structural equation modelling (PLS-SEM) to analyse the proposed models and test the hypotheses.

The obtained findings confirm that the three studied SV dimensions (equality, respect for nature and shared responsibility) have positive effects on perceived CSR, which in turn significantly affects word of mouth (WOM). Moreover, perceived CSR mediates the relationship between the three SVs and WOM but has no impact on the relationship between SVs and revisit intention.

The results of this study have practical implications for managers in the restaurant industry. Restaurant managers can prioritize their CSR efforts based on the relative importance of SV dimensions, which is useful in generating positive WOM.

Even though the importance of CSR is widely recognised, it is insufficiently studied from the perspective of restaurant customers. This research not only addresses this gap, but also expands the current understanding of SVs and their impact on CSR.

This study aims to investigate the mediating role of challenging tasks, organizational identification and technological training seeking behavior in the relationship between information sharing as a human resource practice and employees’ feelings of competency at work.

Structural equation modeling was used to analyze data collected from a three-wave online survey conducted in an Italian aerospace manufacturing company (n = 294).

The results reveal an indirect path between information sharing and feelings of competence, mediated by organizational identification and training seeking behavior. However, no total indirect path was observed between feelings of competency and information sharing through challenging tasks and training seeking.

This research contributes to the understanding of the impact of information sharing on individuals’ feelings of competency in the context of organizational change, particularly through the introduction of the concept of proactive training seeking as a novel dimension of proactive behavior.

This study aims to investigate the nature of the relationship between the state and civil society after the 2011 uprising.

The study adopted Mygdal’s approach to analyze the relationship between the state and civil society and identify their ability to control the rules of the political game. The study also draws on the theoretical framework of the hypotheses introduced by a number of scholars on the forms of potential relations between the state and civil society, and the impact of these forms on advancing the process of democratization.

This study argues that despite some important changes in favor of civil society vis-à-vis the state, it is too early to conclude that a dramatic change has occurred in this relationship, due to a discernable unbalanced power in favor of the state. The state revealed after 2011 that these organizations acted against the state’s stability and against its fundamentals.

To the best of the author’s knowledge, this research is the first to study the relationship between the state and the civil society in Egypt after 2011 events.

This study aims to investigate the role of social capital in Iranian agricultural students' acquisition of generic skills. For this purpose, the effect of various social capital dimensions on students' generic skills development was examined.

A survey was conducted among 190 third- and fourth-year undergraduate students in one of the colleges of agriculture and natural resources in Iran. The partial least square method was used to examine the relationships among various social capital dimensions (i.e. social values, social trust, social networks, social cohesion, social participation, social communications and information sharing) with students' generic skills.

The findings showed that social networks and social participation are effective factors in the generic skills development of students. A model designed for the development of students' generic skills based on their social capital level predicted up to 33% of generic skills' variances. Furthermore, the multi-group analysis showed that males and females vary on how various social capital dimensions affect their generic skills. In this respect, the social participation dimension had a significantly greater impact on female students' generic skills, whereas the generic skills of male students were influenced more by the social cohesion dimension.

Developing generic skills through social capital can be considered as an effective strategy in countries that do not have formal programs for developing students' generic skills. Additionally, higher education policymakers should present a more supportive approach for developing generic skills of female students through social participation in the campuses.

So far, no study has examined the relationships among various social capital dimensions and students' generic skills in Iran. The picture is even more unclear when it comes to the differences between male and female students. The results of this study confirmed the importance of social networks and social participation in the universities to support students and to improve their generic skills and, consequently, their employability competencies. Furthermore, it could be inferred that male and female students have similarities and also differences in terms of the effect of social capital on developing generic skills that can provide a path for future studies.

Growing technology adoption is a significant opportunity for the growth of the mobile gaming market. Rising smartphones allow game developers to reach more players and make more money. The study offers guidance for mobile game developers to increase profits and extend the longevity of their games. It aims to assist in making better marketing policy decisions by providing valuable insights into effective approaches for both early and subsequent market penetration stages.

This paper presents a model for analyzing factors that influence the attraction and retention of mobile gamers, which is based on a literature review of mobile gaming and uses the Bass diffusion model and system dynamics method for modeling and simulation.

In the initial stages of a game release, influencer marketing can be more effective than other marketing tools in increasing visibility. As the game progresses, incorporating referral programs can enhance user engagement, promote the game further, and reduce the number of uninstalls. Influencer marketing can also help the games reach a wider audience, generating more interest, and increasing installations for more tremendous success in the market. However, referral programs can extend the lifespan of games, mainly when gamers are engaged.

The study used new marketing strategies in the mobile game industry to gain insights into the growth and evolution of this industry. Development managers can use insights for effective policies and success. The model aids in understanding the dynamics of mobile games and informed decisions for game developers.

The rapid proliferation of Internet of Things (IoT) devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet. Fog computing, which brings computation resources closer to IoT devices, has emerged as a promising solution. An automatic service placement framework is needed to use fog computing resources efficiently.

In this study, first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers, facilitating automatic application deployment. Next, an enhanced version of the equilibrium optimizer (EO) algorithm, inspired by physics, is designed for service placement in fog computing environments.

Simulations reveal that the proposed approach surpasses existing methods, achieving a 99% success rate compared to the closest alternative’s 93%. The algorithm also significantly reduces waiting and planning times for service placement, proving its efficiency and effectiveness in optimizing IoT service deployment in fog computing.

One of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads. Although improved EO offers improvements in placement efficiency, it may not be fully optimized for highly fluctuating environments. Another important limitation is the uncertainty in node resources. Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes. This uncertainty can affect the algorithm’s ability to consistently make optimal decisions for IoT service placement.

From a practical perspective, the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems. Organizations that rely on IoT networks, particularly those with critical real-time requirements, can benefit from reduced service placement times and lower failure rates. This can lead to better resource utilization, reduced operational costs and improved overall performance of IoT systems. The commercial impact is evident in industries such as smart cities, healthcare, where fast data processing is crucial.

Our proposed framework has important implications for real-world IoT applications, particularly in areas requiring low latency processing, such as healthcare, smart cities. By reducing service delays and optimizing resource allocation, the framework can significantly improve the quality and reliability of services. Additionally, improved resource management leads to cost savings and better system efficiency, making the technology accessible to a wider range of applications.

Existing resource placement strategies have shown inadequate performance, highlighting the need for more advanced algorithms. This study introduces a three-layer automatic framework for enhancing the application deployment of a fog system beside a novel improved EO algorithm to offer a robust solution for assigning IoT applications to fog nodes.

The model and existing practice of the construction supply chain (CSC) in the United Kingdom (UK) and Australia was presented in this chapter. The policies and reports that support the practice of the CSC were examined in both countries. It was discovered from the review of literature that the UK has a more detailed report targeted at improving the CSC than Australia. However, both countries have a common factor affecting their CSC which originates from fragmentation experienced within their supply chain. Construction stakeholders in the UK and Australia believe that collaboration and integration are vital components for improving performance. The majority of the contractors in both countries embrace collaborative working for the sole purpose of risk sharing, access to innovation and response to market efficiency. However, most of the models developed for managing the CSC in the UK are built around building information modelling (BIM). Also, the reviewed studies show that supply chain management practice will be effective following the following principle: shared objectives, trust, reduction in a blame culture, joint working, enhanced communication and information-sharing. Finally, the UK has a more established framework and more CSC models compared to Australia.

Based on social exchange theory and social identification theory, I investigated how employee organizational identification affects the effectiveness of commitment-based human resource (HR) practices. I focused on employee attitudes (job satisfaction) and behaviors (proactive knowledge seeking) as HR practices’ outcomes.

Using a structural equation modeling analytical approach, I tested the hypotheses with data from a web-based cross-sectional survey of 208 specialists and engineers of manufacturing subsidiaries in Poland.

Results showed that the positive relationship between commitment-based-HR practices and job satisfaction is weakened for employees strongly identified with the organization. Simultaneously, the connection between seeking knowledge and job satisfaction is stronger and more important for people who identify moderately to strongly.

The study limitations regard mainly its cross-sectional design and single cultural and industrial context.

From the managerial perspective, the study suggests that to enhance proactive employee behavior, companies need to increase employee organizational identification and ensure that employees have a positive perception of the implemented HR practices.

The study contributes to the ongoing discussion on whether individual contingencies affect the effectiveness of commitment-based HR practices in the form of individual attitudinal and behavioral outcomes. The findings revealed that the contingent effect of organizational identification depends on the type of individual outcomes, suggesting that the strength of organizational identification affects how employees decide to reciprocate the organization’s attention and investment.