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Purpose: The paper empirically compares employee motivation and its impact on performance in Ghanaian Mining Companies, where in measuring performance, the job satisfaction model is used. Design/methodology/approach: The study employed exploratory research design in gathering data from four large-scale Gold mining companies in Ghana with regards to their policies and structures in the effectiveness of motivational tools and strategies used by these companies. Findings: The study observed that, due to the risk factors associated with the mining industry, management has to ensure that employees are well motivated to curb the rate at which employees embark on industrial unrest which affect performance, and employees are to comply with health and safety rules because the industry contribute hugely to the Gross Domestic Product (GDP) of the country. Research limitations/implications: Limitation to the present study include the researcher’s inability to contact other mining companies. However, the study suggests possibilities for future research including contacting other mining companies, expanding the sample size, managers ensuring that the safety and health needs of staff are addressed particularly those exposed to toxic and harmful chemicals. Originality/value: A lot of studies have been done on mining companies in the past. This paper fills a gap perceived that employees in this sector are highly motivated in spite of the challenges being faced by them, and knowing more about what keeps employees moving is still of national interest.

Exploration of the political roles firms play in society is a flourishing stream within corporate social responsibility (CSR) research. However, few empirical studies have examined multiple levels of political CSR at the same time from a critical perspective. We explore both how the motivations of managers and internal organizational practices affect a company's choice between competing CSR approaches, and how the different CSR programs of corporate and civil society actors compete with each other. We present a qualitative interpretative case study of how a French children's clothing retailer develops CSR practices in response to accusations of poor working conditions and child labor in its supply chain. The company's CSR approach consists of superficial practices, such as supplier audits by a cooperative business-organized nongovernmental organization (NGO) and philanthropic activities, which enable managers to silence more radical alternative models defended by other NGOs, activists, and trade unions. By this approach, the core business model based on exploitative low-cost country sourcing remains intact through self-regulated CSR. Through the case study, we develop a framework of dynamism in competing CSR programs. We discuss the implications of our study for CSR researchers, company managers, and policy makers.

Pattern mining is a data mining technique used for discovering significant patterns and has been applied to various applications such as disease analysis in medical databases and decision making in business. Frequent pattern mining based on item frequencies is the most fundamental topic in the pattern mining field. However, it is difficult to discover the important patterns on the basis of only frequencies since characteristics of real-world databases such as relative importance of items and non-binary transactions are not reflected. In this regard, utility pattern mining has been considered as an emergent research topic that deals with the characteristics. In real-world applications, meanwhile newly generated data by continuous operation or data in other databases for integration analysis can be gradually added to the current database. To efficiently deal with both existing and new data as a database, it is necessary to reflect increased data to previous analysis results without analyzing the whole database again. In this paper, we propose an algorithm called HUPID-Growth (High Utility Patterns in Incremental Databases Growth) for mining high utility patterns in incremental databases. Moreover, we suggest a tree structure constructed with a single database scan named HUPID-Tree (High Utility Patterns in Incremental Databases Tree), and a restructuring method with a novel data structure called TIList (Tail-node Information List) in order to process incremental databases more efficiently. We conduct various experiments for performance evaluation with state-of-the-art algorithms. The experimental results show that the proposed algorithm more efficiently processes real datasets compared to previous ones.

PurposeThe purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.Design/methodology/approachThe extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.FindingsA process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.Research limitations/implicationsThe study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.Practical implicationsThe maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.Social implicationsThis research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.Originality/valueThis paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

Construction project managers (CPMs) play a crucial role in project management. Using nearly 250,000 online resumes, this study aims to identify the major aspects and explore the prevalence trend of competences required by CPMs in real work. A data mining approach, Dynamic Topic Model (DTM), was adopted and ten aspects of CPMs’ competences are disclosed. The results of Mann-Kendall tests suggest that among technical skills, work experience and the ability of information technology application are gaining increasing attention rather than professional skill. Moreover, human skills, key managerial competences (i.e., procurement management, risk management and site management) and organizational skill are highlighted. Theoretically, the results provide a systematic review on the real-world competence requirements and their changing trends for CPMs. In practice, these findings can not only be utilized to help students in relevant major and practitioners benchmark their own competences with real-world requirements, but also assist construction firms in formulating more informed talent strategies.

Artificial Intelligence (AI) applications streamline workflows, automate tasks, and require adaptive strategies for effective integration into business processes. This research explores the transformative influence of AI on various industries, such as software engineering, automation, education, accounting, mining, legal services, and media. We investigate the relationship between technological advancements and the job market to identify relevant skills for individuals and organizations for implementing and managing AI systems and human–machine interactions necessary for actual and future jobs. We focus on the essential adaptations for individuals and organizations to flourish in this era. To bridge the gap between AI-driven demands and the existing capabilities of the workforce, we employ the Rapid Review methodology to explore the integration of AI in businesses, identify crucial skill sets, analyze challenges, and propose solutions in this dynamic age. We searched the Scopus database, screening a total of 39 articles, of which we selected 20 articles for this systematic review. The inclusion criteria focused on conference papers and journal articles from 2020 or later and written in English. The selected articles offer valuable insights into the impact of AI on education, business, healthcare, robotics, manufacturing, and automation across diverse sectors, as well as providing perspectives on the evolving landscape of expertise. The findings underscore the importance of crucial skill sets, such as technical proficiency and adaptability, to successfully adopt AI. Businesses respond strategically by implementing continuous skill adaptation and ethical technology to address challenges. The paper concludes by emphasizing the imperative of balanced skill development, proactive education, and strategic integration to navigate the profound impact of AI on the workforce effectively.

PurposeA construction project is complex and requires dynamic modelling of a range of factors that deters time performance because of uncertainty and varying operating conditions. In construction project systems, the system components are the interconnected stages, which are time-dependent. Within the project stages are the activities which are the subsystems of the system components, causing a challenge to the analysis of the complex system. The relationship of construction project time management (CTM) with the construction project time influencing factors (CTFs) and the adaptability of the time-varying system is a key part of project effectiveness. This study explores the relationship between CTM and CTF, including the potentials to add dynamical changes on every project stage.Design/methodology/approachThis study proposed a dynamic Bayesian network (DBN) model to examine the relationship between CTM and CTF. The model investigates the time performance of a construction project that enhances decision-making. First, the paper establishes a model of probabilistic reasoning and directed acrylic graph (DAG). Second, the study tests the dynamic impact (IM) of CTM-CTF on the project stages over a specific time, including the adaptability of time performance during disruptive CTF events. In demonstrating the effectiveness of the model, the authors selected one-organisation-single-location road-improvement project as the case study. Next, the confirmation of the model internal validity relied on conditional probabilities and the project knowledge experts' selected from the case company.FindingsThe study produced structural dependencies of CTM and CTF with probability observations at each stage. A predictive time performance analysis of the model at different scenarios evaluates the adaptability of CTM during CTF uncertain events. The case demonstration of the model application shows that CTFs have effects on CTM strategy, creating the observations to help time performance restorations after disruptions.Research limitations/implicationsAlthough the case company experts' panel confirms the internal validity of the results for managing time, the model used conditional probability table (CPT) and project state values from a project contract. A project-wide application then will require multi-case data and data-mining process for generating the CPTs.Practical implicationsThe study developed a method for evaluating both quantitative and qualitative relationships between CTM and CTF, besides the knowledge to enhance CTM practice and research. In construction, the project team can use model observations to implement time performance restorations after a predictive or reactive disruption, which enhances decision-making.Originality/valueThe model used qualitative and qualitative data of a complex system to generate results, bounded by a range of probability distributions for CTM-CTF interconnections during time performance disruptions and restorations. The research explores the approach that can complement the mental CTM-CTF modeling of the project team. The CTM-CTF relationship model developed in this research is fundamental knowledge for future research, besides the valuable insight into CTF influence on CTM.

Amid the rapid advancement of artificial intelligence (AI) and increasing environmental uncertainty, enterprises are facing unprecedented challenges in sustaining innovation. As a key enabler of digital transformation, AI enhances resource allocation efficiency and knowledge acquisition, offering new avenues for continuous innovation under dynamic conditions. Innovation resilience—defined as a firm’s ability to maintain and restore innovation activities during external shocks—has emerged as a critical indicator of organizational adaptability. Leveraging its advantages in data processing, process optimization, and organizational learning, AI is increasingly regarded as a pivotal driver of innovation resilience. This study develops a theoretical framework linking AI technology, dynamic capabilities, and innovation resilience. Using panel data from Chinese A-share listed companies between 2013 and 2023, we conduct an empirical analysis with a two-way fixed effects model. The results reveal that AI technology significantly enhances innovation resilience; dynamic capabilities partially mediate this relationship; and financial constraints positively moderate the effect of AI on innovation resilience. By adopting a dual perspective of technological enablement and capability construction, this research uncovers the internal mechanism through which AI fosters resilient innovation and provides practical insights for enterprises seeking capability upgrading under resource limitations.

The rapid increase of textual data has transformed the way we understand and forecast financial market behavior. Investor sentiments, often swayed by news, are pivotal in determining stock prices. Analyzing a dataset of 192.582 Indonesian financial news articles published between 2018 and 2023. This study investigates the complex connections between news sentiment and stock market behavior of Indonesian companies. We leverage AI-based sentiment analysis and natural language processing techniques, including identity recognition, network analysis, and correlation assessment, to explore how news sentiment affects stock prices at the levels of individuals, industries, and news co-occurrence clusters. While earlier research has addressed the effect of sentiment on stock prices at both the company and industry levels, there is a significant lack of studies focused on media co-occurrence clusters, which is vital for comprehending the collective media portrayal of interconnected firms. Our results show that sentiment-price correlations strengthen hierarchically, with individual companies at 0.26, industry groupings at 0.30, and news co-occurrence clusters at 0.43. This research introduces a unique analytical framework that explores sentiment across various levels, highlighting co-occurrence clusters that reflect business relationships beyond traditional industry lines. It demonstrates that companies frequently mentioned together in the news exhibit stronger and more stable sentiment-price correlations, offering a new analytical perspective for AI-driven investment strategies and underscoring the potential of big data analytics in Indonesia's capital market.

The circular economy concept is popular because it encourages resource efficiency, sustainable production, a shift in economic thinking, and the creation of higher-skilled jobs. We are unavoidably used to the traditional linear economy cradle-to-cradle model of production and consumption in our contemporary life. This study aims to determine the elements that support and impede Malaysian manufacturing enterprises’ adoption of the big data analytics and circular economy business model, given the discrepancy in developing countries in Southeast Asia. The circular economy business model is used to analyze the impacts of sustainable performance. Using the lenses of dynamic capability theory (DCT) and covariance-based structural equation modeling (CB-SEM), this study assesses the responses of 241 respondents from various sectors of the manufacturing sector having environmental management systems (EMS) within Malaysia. Therefore, survey-based primary data was gathered to understand the effect of big data analytics on sustainable performance via moderate mediation of circular economy practices, brand reputation, and environmental dynamics. Findings of distal mediation revealed that big data analytics have a significant positive effect on the sustainable performance of manufacturing firms. Furthermore, it is revealed that environment dynamics at each level of mediation moderate the relationship significantly; hence, it is important for the firms to understand the dynamics of the environment, either internal or external, where the firms are operating to effectively implement the big data analytics (BDA), circular economy practices (CEP) to achieve sustainable performance.