Search results

As organizations globalize, they are facing twin challenges of (1) how to develop actionable intelligence from the vast amount of data flowing into their organization and (2) how to effectively manage the increasing risks to their supply chain. Therefore, the purpose of this paper is to bring these two issues on a single platform to understand how firms can effectively predict supply chain risk by developing and using BDA capabilities, through an automated risk alert tool.

The authors used a questionnaire-based survey methodology supported by secondary data to collect information related to managerial perceptions on how firms can develop a risk alert tool by improving BDA capabilities. A database of 213 senior and middle-level managers was developed and used to test the proposed hypothesis. Using econometric techniques, the authors identify the conditions necessary for such an automated risk management tool to be effective.

The results suggest that if organizations focus on developing an effective IT infrastructure supported by a strong BDA capability, they will be able to leverage these capabilities to develop an effective risk management tool. Moderating influences of Upstream and Downstream Supply Chain IT Infrastructure capabilities were also observed on different types of BDA capabilities within a firm. In conclusion, it was argued that the effectiveness of a risk alert tool is dependent on how well firms harness big data analytics capability.

The value of the research stems from the fact that it uses managerial surveys to identify specific BDA capabilities that can enable firms to develop risk resilience capabilities. In addition, the article is one of the few empirical studies that aims to identify how firms can use BDA capabilities within a supply chain context to develop an automated risk alert tool. The article, therefore, contributes to the literature that identifies the value of BDA capabilities within the context of supply chain risk management.

This systematic literature review analyzes the academic literature to understand SC risk and resilience across different organizational sizes and industries. The academic literature has well discussed the causes of supply chain (SC) risk events, the impact of SC disruptions, and associated plans for SC resilience. However, the literature remains fragmented on the role of two fundamental elements in achieving SC resilience: the firm's size and the firm's industry as firms' contingent factors. Therefore, it is important to investigate and highlight SC resilience differences by size and industry type to establish more resilient firms.

Building upon the contingent resource-based view of the firm, the authors posit that organizational factors such as size and industry sector have important roles in developing organizational resilience capabilities. This systematic literature review and analysis is based on the structural and systematic analysis of high-ranked peer-reviewed journal papers from January 2000 to June 2021 collected through three global scientific databases (i.e. ProQuest, ScienceDirect, and Google Scholar) using relevant keywords.

This systematic literature review of 230 high-quality articles shows that SC risk events can be categorized into demand, supply, organizational, operational, environmental, and network/control risk events. This study suggests that the SC resilience plans developed by startups, small and mdium-sized enterprises (SMEs), and large organizations are not necessarily the same as those of large enterprises. While collaboration and networking and risk management are the most crucial resilience capabilities for all firms, applying lean and quality management principles and utilizing information technology are more crucial for SMEs. For large firms, knowledge management and contingency planning are more important.

This study provides a comprehensive review of the literature on SC resilience plans across different organizational sizes and industries, offering new insights into the nature and dynamics of startups', SMEs', and large enterprises' SC resilience in different industries. The study highlights the need for further investigation of SC risk and resilience for startups, SMEs, and different industries on a more detailed level using empirical data. This study’s findings have important implications for researchers and practitioners and guide the development of effective SC resilience strategies for different types of firms.

The purpose of this paper is to explore the important role of supply chain risk management (SCRM) capabilities as pre-factors for SMEs to improve supply chain financing performance (SCFP), also incorporating the effect of supply chain integration (SCI).

From the intersection of SCRM and SCF literature, this paper proposed hypothesis to discuss the impact of SCRM capabilities on SCFP and the role of SCI, aiming at combine SCRM with supply chain financing management. The research model was validated applying structural equation modeling on survey data from 286 Chinese small and medium-sized enterprises (SMEs).

Four dimensions of SCRM capabilities have significant positive effects on SCFP with different significant levels, confirming that they are important pre-factors in supply chain finance (SCF). In addition, the impact of SCRM capabilities on SCFP differ when SCI varies, indicating the promoting effect of SCI.

SMEs should establish SCRM capabilities as supply chain risks greatly influence the evaluation of financial providers and the achievement of SCF. Meanwhile, SCI should be attached for it enables superior SCFP even if SCRM capabilities are relatively limited.

This study represents a pioneering attempt to analyze the pre-factors of SMEs in improving SCFP by combing SCRM with SCF management. Few prior studies have highlighted the importance of SCRM in SCF.

In this paper, the authors emphasize that COVID-19 pandemic is a serious pandemic as it continues to cause deaths and long-term health effects, followed by the most prolonged crisis in the 21st century and has disrupted supply chains globally. This study questions “can technological inputs such as big data analytics help to restore strength and resilience to supply chains post COVID-19 pandemic?”; toward which authors identified risks associated with purchasing and supply chain management by using a hypothetical model to achieve supply chain resilience through big data analytics.

The hypothetical model is tested by using the partial least squares structural equation modeling (PLS-SEM) technique on the primary data collected from the manufacturing industries.

It is found that big data analytics tools can be used to help to restore and to increase resilience to supply chains. Internal risk management capabilities were developed during the COVID-19 pandemic that increased the company's external risk management capabilities.

The findings provide valuable insights in ways to achieve improved competitive advantage and to build internal and external capabilities and competencies for developing more resilient and viable supply chains.

To the best of authors' knowledge, the model is unique and this work advances literature on supply chain resilience.

This article aims to comprehensively review the measurement and management of supply chain performance (SCP) and strategic performance (SP). It strives to identify integrable features regarding frameworks, measurement approaches, practices and emerging research issues in these areas to integrate SCP and SP for measuring and managing performance. It intends to develop a dynamic-integrated-performance-system by incorporating integrable aspects of SCP and SP to link these domains for organizational performance improvement.

Using systematic-literature-review, this study analyzes 154 articles published in selected peer-reviewed international journals from 2000 to 2023 regarding SCP and SP. It assesses existing knowledge regarding research-design followed, challenging areas and imperatives in these critical business domains to investigate the prior conceptual, empirical, case study-based and literature-review-based articles.

The study identifies integrable features regarding key theoretical and measurement frameworks, critical objectives, significant measures, effective practices for measuring and managing SCP and SP and emerging research issues common to these areas. The findings help develop a dynamic-integrated-performance-system that uses the theoretical lenses of resource-based-view/dynamic-capability-theory and adopts a comprehensive framework like DBSC (system-dynamic-model with BSC perspectives). It incorporates identified integrable measures and best practices to monitor, measure, manage and improve organizational performance for sustainable competitive advantage. The article reveals that earlier studies have overlooked analyzing SCP and SP integration aspects.

From the theoretical viewpoint, the present SLR is unique in three ways: first, in investigating both the measurement and management of SCP and SP holistically; second, in identifying integrative features of these two; and third, in proposing a DIPS to link SCP and SP for performance improvement. The study reveals that existing literature has focused on measuring and managing SCP and SP in isolation without attempting a comprehensive and unified approach to integrate the respective domains. The present SLR adopts a holistic approach to link SCP and SP from SCM and strategic-management perspectives. The study proposes a dynamic-integrated-performance-system to measure, manage and improve performance in a unified method.

This study provides SC and strategy practitioners with an understanding of strategy-performance pathways for achieving strategic objectives and executing risk mitigation initiatives to counter disruptions. It enables SC managers to comprehend SC practices and SCP leading to dynamic SC capabilities development. Operationalizing the proposed DIPS will help firms link SCP and SP, align operational SC practices with strategic sustainability and circularity objectives and meet sustainable development goals while benefiting social and environmental stakeholders.

Assessing relationships and identifying a unified approach integrating SCP with SP have not been addressed earlier. This study's uniqueness is finding integrable features of SCP and SP and constructing a dynamic-integrated-performance-system to link these domains for achieving strategic competitiveness.

This research tries to find the blockchain-based resilience strategies that can help the supply chains of micro, small, and medium-sized enterprises (MSMEs) to recover from the disruptions and work effectively in a resource-based view perspective.

Eight broad strategies and 32 sub-strategies are identified from the literature review. Delphi study was carried out, and detailed discussion with 16 experts helped in finalizing these strategies. Further, the best-worst method (BWM) prioritized these strategies.

The findings suggests that “building social capital,” improving “coordination capabilities,” “sensitivity towards market,” “flexibility in process and production,” “reduction in process and lead time,”and “having a resource efficiency and redundancy” are the top strategies on which the top management should focus to overcome the situations of disruptions and enhance performance of MSMEs.

The blockchain-based strategies will enable the companies in tracing the products from the company to customers. Further, the customers will be able to identify their manufacturers, the raw materials used in manufacturing, and the life and quality of raw used materials. Altogether the textile industry will become more sensitive toward environmental practices.

The previous research has not identified and evaluated the blockchain-based resilience strategies, and therefore this study tries to fill this gap. This study used a smaller sample from the experts, so the results may vary if the larger data set is used and hypothesis testing can be done.

Uncertainty management (UM) in projects has been a point of attention for researchers for many years. Research on UM has mainly been aimed at uncertainty analyses in the front-end and managing uncertainty in the construction phase. In contrast, UM components in the design phase have received less attention. This research aims to improve knowledge about the key components of UM in the design phase of large road projects.

This study adopted a literature review and case study. The literature review was used to identify relevant criteria for UM. These criteria helped to design the interview guide. Multiple case study research was conducted, and data were collected through document study and interviews with project stakeholders in two road projects. Each case's owners, contractors and consultants were interviewed individually.

The data analysis obtained helpful information on the involved parties, process and exploit tools and techniques during the design phase. Johansen's (2015) framework [(a) human and organisation, (b) process and (c) tools and techniques)] was completed and developed by identifying relevant criteria (such as risk averse or risk-taker, culture and documentation level) for each component. These criteria help to measure UM performance. The authors found that owners and contractors are major formal UM actors, not consultants. Empirical data showed the effectiveness of Web-based tools in UM.

The studied cases were Norwegian, and this study focussed on uncertainties in the project's design phase. Relevant criteria did not cover all the criteria for evaluating the performance of UM. Qualitative evaluation of criteria allows further quantitative analysis in the future.

This paper gave project owners and managers a better understanding of relevant criteria for measuring UM in the owners and managers' projects. The paper provides policy-makers with a deeper understanding of creating rigorous project criteria for UM during the design phase. This paper also provides a guideline for UM in road projects.

This research gives a holistic evaluation of UM by noticing relevant criteria and criteria's interconnection in the design phase.

The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

This study aims to examine the impact of governance on the double-bottom-line performance of microfinance institutions (MFIs) in Bangladesh.

This study relies on three dimensions of corporate governance (CG) practices, that is, functions of the board of directors (BoD), top-level management activities and external governance mechanisms. This study uses panel data econometrics, particularly pooled OLS, fixed effects and two-stage system generalized method of moments to deal with potential endogeneity concerns. The panel data set covers 1,200 MFI year observations from Bangladesh for the period between 2005 and 2019.

The findings show that the presence of stakeholders on boards plays a critical role in MFIs. The dual goals of MFIs are influenced by board size, board independence and CEO duality. Internal management activities, risk perceptions and external governance also impact MFIs’ performance. Women on board have an inverse association with outreach. The activities of female managers have a significant impact on depth of outreach.

Like many others, this study also admits the data constraint issues in microfinance research. CG data for MFI are mostly unavailable in the public domain; therefore, this study must rely on third-party data sources. This study only includes MFIs that has data for all variables of interest.

Governance attributes in hybrid organizations are constituted differently. To warrant multistakeholder engagement, there is a need to develop a distinctive governance manual for hybrid organizations like MFIs.

This study proposes adopting a Social Director on the BoD to ensure the scope of outreach depth, given the importance of social goals in MFIs.

This study contributes to the ongoing debate on microfinance governance, addresses the issue based on different theoretical aspects using a country-specific data set and uses dynamic panel models to deal with potential endogeneity concerns.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.