Search results

This paper aims to explain the factors shaping collective action within low-cost housing communities, focusing on parcel holders, through the utilisation of an expanded institutional analysis development (IAD) framework, which extends upon Ostrom’s foundational framework. Additionally, the paper explores four different property management approaches accessible to these communities.

The research employed a mixed-method approach comprising four sequential steps. Firstly, a quantitative inquiry entailed a questionnaire survey administered to 633 parcel holders across four low-cost housing schemes, aimed at discerning factors influencing collective action. Subsequently, a qualitative investigation involved face-to-face interviews with key stakeholders to elucidate the contributing factors of collective action, with a specific focus on Nursa Kurnia (a successful low-cost housing scheme comprising 200 units), accessible via Kuala Lumpur Middle Ring Road II. Thirdly, the study explored the social practice of “commoning the governance”. Lastly, the paper advocated for housing policy interventions, specifically proposing government subsidies for lower-income parcel holders.

Exemplified by the success of Nursa Kurnia, the research findings emphasised the importance of shifting local management’s mindset from a zero-sum approach to a win-win perspective. It highlighted the pivotal role of four factors (resource system, governance system, context and historical development) in shaping collective action and fostering improved property management practices. Moreover, the study highlighted the potential of “commoning the governance” as a new approach capable of addressing collective action challenges in low-cost housing management, presenting a promising avenue for future endeavours.

As more studies utilising the expanded IAD framework become available in the future, there is potential for further refinement and enhancement of the framework.

This study offers valuable insights for policymakers, property developers, local management and local communities, shedding light on challenges associated with the self-organisation of shared resources. Moreover, it highlights the potential of “commoning the governance” as a new property management approach to mitigate the impact of collective action problems.

The well-being of society’s most vulnerable segment is indicative of the overall societal health. This underscores the significance of addressing the interests and needs of these lower-income groups within the broader social context.

Exploring collective action within the context of self-organising low-cost housing, the study delves into an area marked by persistent challenges like free-riding tendencies and vandalism. Despite significant attention given to collective action issues in the past, the novel approach of “commoning the governance” remains unexamined in the realm of low-cost housing maintenance and management.

Interdependence on the global economy and rapid technological changes raised the degree of uncertainty and complexity, leading to innovation challenges. Innovation depends on knowledge, and the solution might rest on how sound firms manage it, particularly in emerging markets such as India. The purpose of this paper is to examine how firms implement knowledge management (KM) in highly innovation-oriented firms (biotechnology and pharmaceuticals) and the factors affecting its implementation by examining knowledge interactions between individuals.

This study consists of a systematic literature review, a case study with embedded units and the use of grounded theory to analyse the data. The factors emerging from the results were examined from an individual and organisational lens. Next, complexity theory (CT) was used to understand the impact of these factors in KM by facilitating its incorporation as a system.

The findings of this paper suggest that constant technology adoption increases human-to-technology interaction, higher circulation of existing knowledge and more controlled environments, discouraging individuals from learning or sharing knowledge. From a system perspective, results of this paper suggest that firms self-organise around technology, indicating that innovation decreases as knowledge creation and sharing tend to reduce with lesser social interactions. This study shows the usefulness of using CT in analysing KM for innovation. The performance of the system is analysed based on its constituents and interactions.

This study contributes to advancing CT in KM in the context of innovation in highly knowledge-intensive firms, as few studies were found in the literature.

Agile project teams are self-managing and self-organizing teams, and these two characteristics are pivotal attributes of emergent leadership. Emergent leadership is thus common in agile teams – however, how these (informal) emergent leaders can be identified in teams remains far from understood. The purpose of this research is to uncover techniques that enable top management to identify emergent agile leaders.

We approached six agile teams from four organizations. We employ social network analysis (SNA) and aggregation approaches to identify emergent agile leaders.

We approached six agile teams from four organizations. We employ SNA and aggregation approaches to identify emergent agile leaders.

Seven emergent leaders are identified using the SNA and aggregation approaches. The same leaders are also identified using the KeyPlayer algorithms. One emergent leader is identified from each of the five teams, for a total of five emergent leaders from the five teams. However, two emergent leaders are identified for the remaining sixth team.

Emergent leadership is a relatively new phenomenon where leaders emerge from within teams without having a formal leadership assigned role. A challenge remains as to how such leaders can be identified without any formal leadership status. We contribute by showing how network analysis and aggregation approaches are suitable for the identification of emergent leadership talent within teams. In addition, we help advance leadership research by describing the network behaviors of emergent leaders and offering a way forward to identify more than one emergent leader in a team. We also show some limitations of the approaches used and offer some useful insights.

This study aims to propose and validate a framework for analyzing management activities in regional strategic networks. It addresses the knowledge gap regarding network management and the lack of focus on specific activities and management modes employed by network actors.

The research methodology involves a literature review to identify essential network management functions, which academic experts and field specialists then evaluate. Based on their feedback, an instrument is refined to assess these activities’ strategic importance and frequency. Data is collected from 86 regional strategic network managers, meeting the required criteria for factor analysis. Principal component analysis with varimax rotation measures the “Network Management” construct and identifies underlying dimensions summarizing observed variables.

The analysis reveals two dimensions within the framework: one encompasses early-stage network development activities (ex ante activities), and the other includes activities associated with more advanced stages (ex post activities). This framework contributes methodologically to measuring and analyzing network management.

This research addresses the need for robust conceptual frameworks in network governance, advancing the understanding of governance drivers and their impact on network outcomes by aligning network phenomena with performance.

The framework provides valuable insights for network managers, enhancing their understanding of management activities’ strategic importance and frequency. This knowledge can inform managerial decisions and improve network management practices.

This study introduces an original framework for analyzing network management activities in regional strategic networks, filling a significant research gap. Despite limitations like specificity to certain network types and subjective responses, the study maintains validity and reliability criteria. Future research opportunities involve applying the framework to different network contexts and conducting longitudinal studies to track network management evolution.

Purpose: The surge in utilising big data, machine learning (ML), and artificial intelligence (AI) has significantly strengthened financial systems, enhancing their robustness, safety, and fairness. AI in finance is profoundly reshaping our lives. This study investigates AI’s role in personal and corporate finance.

Methodology: A comprehensive literature review was conducted to examine studies on the application of AI in personal and corporate finance.

Findings: The financial services sector is undergoing an AI-led transformation, leveraging technologies such as deep learning, collaborative filtering, support vector machines, automation, robotic assistance, and AI-based predictive modelling. In personal finance, AI delivers personalised insights, automates procedures, detects fraud, and enhances financial literacy. It empowers individuals to manage their finances more effectively by tracking expenditures, aiding in budget management, and setting and monitoring financial goals. In corporate finance, AI aids in detecting and analysing credit risks, improving loan underwriting, and minimising financial risk. It also helps reduce financial crimes and enhances the financial performance of accounting firms. AI’s role in bookkeeping, accounting, and financial management significantly benefits entrepreneurs, business executives, investors, and bankers involved in starting and growing organisations.

Practical Implications: In personal finance, AI enhances financial planning, automates transactions, improves credit scoring, and strengthens fraud detection. In corporate finance, AI facilitates advanced risk management, precise financial forecasting, cost reduction through automation, and optimised investment strategies.

The need to optimize the triangle formed by “quality, cost and time” culminated in increasing the focus from product to process quality. By analyzing the evolution of quality and the impact of Industry 4.0 on it, this research seeks, through a technical point of view, to comprehend the state of the art of quality 4.0 and intelligent quality management (IQM) by defining concepts, technologies, challenges and applications.

The review was conducted only in English, on IEEE Xplore, Scopus, Engineering Village and Web of Science databases with a backward citation analysis, having technology and quality as main concepts. In total, 109 papers were reduced to 24, and 11 characteristics were extracted.

Although many authors point to the same 4.0 technologies and the importance of quality for Industry 4.0, they differ in the concept of quality 4.0 and the implementation frameworks to achieve it.

This paper is one of the few studies that have searched for the roots of quality 4.0 and IQM. The work also seeks to identify their differences and their relationship with Industry 4.0.

This study aims to investigate the practical utilisation of Artificial Intelligence (AI) techniques in combating credit card fraud (CCF) within the accounting and finance sectors. It will evaluate the efficacy of machine learning (ML), blockchain and fuzzy logic in detecting fraudulent transactions, aiming to provide valuable insights for professionals including fraud examiners, auditors, accountants, bankers and organisations. The research seeks to determine whether AI and ML methods yield beneficial outcomes in the realm of credit card fraud detection (CCFD). It will focus on applying AI and ML techniques in CCFD, incorporating interviews, cross-country questionnaires and a sample size of 403. The study endeavours to contribute to understanding the optimal mechanism for detecting CCF and its potential for widespread application.

This study examines the motivational processes of charged behavior and collective efficacy driving interdependence and agency in new product development (NPD) teams and the moderating impact of team risk-taking propensity as affective, cognitive and behavioral social processes support team innovation.

Data were collected from 92 NPD teams engaged in B2C and B2B product and service development. Mediating and moderating effects are examined using partial least squares structural equation modeling, referencing social cognitive and collective agency theories as the research framework.

The analysis validates collective self-efficacy and charged behavior as interdependent motivational–affective processes that align cognitive resources and govern team effort toward innovativeness. Teams' risk-taking propensity regulates behavior, and collective efficacy facilitates self-regulated motivational engagement. Charged behavior cultivates the emotional contagion, team identification, cohesion and adaptation required for team functioning. Team potency fosters cohesiveness, while team learning improves adaptability along the innovation journey. The resulting theory asserts that motivational drivers enhance the interplay between cognitive and behavioral processes.

Managers should consider NPD teams as social systems with a capacity for collective agency nurtured through interdependence, which requires collective efficacy and shared competencies to generate motivational purpose and innovativeness. Managers must remain mindful of teams' risk tolerance as regulating the impact of motivational factors on innovativeness.

This study contributes to research on the motivational–affective drivers of NPD charged behavior and collective efficacy as complementary to cognitive and behavioral processes sustaining team innovativeness.

The study aims to identify the essential competencies for Industry 4.0 within the manufacturing sector, to prioritise developing them among the workforce in creating a competitive advantage for the organization.

The study employs the Analytic Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) methodology to prioritize competencies. Literature review and expert input guided the identification of competencies, which were ranked by experts for their relevance, through pairwise comparisons.

Seven competency groups, encompassing 21 sub-groups, were identified as essential for the Industry 4.0 workforce. Digital-technical and industry-specific competencies emerged as the most prominent to be developed on priority, followed by cognitive and business competencies. Despite their smaller representation, core/generic competencies remain the foundation for developing the newer and more specialised competencies.

Recognising the need for empirical studies in early-adopting organisations of Industry 4.0, future research should explore competencies across industries as well as talent development mechanisms, for a nuanced understanding of competency requirements.

The study informs organisations, educators and policymakers guiding workforce training, talent management and development, educational curriculum aligned with the demands of Industry 4.0 to bridge the competency gaps. It can support India’s strategic initiatives like “Make in India” by fostering a digitally ready and competent workforce.

This research provides an empirically validated, structured framework for Industry 4.0 competency prioritization specific to the manufacturing sector in India. It integrates expert inputs with AHP to rank competencies, offering a contextual understanding of competency requirements. It also contributes to human capital theory by advancing competency mapping for Industry 4.0.