scholarly journals Supply chain resilience in mindful humanitarian aid organizations: the role of big data analytics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Denis Dennehy ◽  
John Oredo ◽  
Konstantina Spanaki ◽  
Stella Despoudi ◽  
Mike Fitzgibbon
Keyword(s):  
Big Data ◽  
Supply Chains ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Humanitarian Aid ◽  
Humanitarian Relief ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Organizational Mindfulness ◽  
Collective Mindfulness

PurposeThe purpose of this paper is to understand the nomological network of associations between collective mindfulness and big data analytics in fostering resilient humanitarian relief supply chains.Design/methodology/approachThe authors conceptualize a research model grounded in literature and test the hypotheses using survey data collected from informants at humanitarian aid organizations in Africa and Europe.FindingsThe findings demonstrate that organizational mindfulness is key to enabling resilient humanitarian relief supply chains, as opposed to just big data analytics.Originality/valueThis is the first study to examine organizational mindfulness and big data analytics in the context of humanitarian relief supply chains.

How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Surajit Bag ◽  
Pavitra Dhamija ◽  
Sunil Luthra ◽  
Donald Huisingh
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chains ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Hypothetical Model ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Capabilities

PurposeIn 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.Design/methodology/approachThe 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.FindingsIt 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.Practical implicationsThe 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.Originality/valueTo the best of authors' knowledge, the model is unique and this work advances literature on supply chain resilience.

The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamad Bahrami ◽  
Sajjad Shokouhyar
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Firm Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Culture

PurposeBig data analytics capability (BDAC) can affect firm performance in several ways. The purpose of this paper is to understand how BDA capabilities affect firm performance through supply chain resilience in the presence of the risk management culture.Design/methodology/approachThe study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. 167 responses were collected and analyzed using partial least squares in SmartPLS3. The respondents were generally senior IT executives with education and experience in data and business analytics.FindingsThe results show that BDA capabilities increase supply chain resilience as a mediator by enhancing innovative capabilities and information quality, ultimately leading to improved firm performance. In addition, the relationship between supply chain resilience and firm performance is influenced by risk management culture as a moderator.Originality/valueThe present study contributes to the relevant literature by demonstrating the mediating role of supply chain resilience between the BDA capabilities relationship and firm performance. In this context, some theoretical and managerial implications are proposed and discussed.

Challenges with big data analytics in service supply chains in the UAE

2019 ◽  
Vol 57 (8) ◽  
pp. 2124-2147 ◽  
Author(s):  
Mehmood Khan
Keyword(s):  
Big Data ◽  
Supply Chains ◽  
United Arab Emirates ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Second Phase ◽  
Content Type ◽  
Service Staff ◽  
Service Supply Chains ◽  
Practical Implications

Purpose The purpose of this paper is to study the challenges associated with big data analytics (BDA) in service supply chains in the United Arab Emirates (UAE). Design/methodology/approach A comprehensive questionnaire has been developed based on semi-structured interviews with different administrators and IT experts. In the second phase, data (n=164) are collected from procurement, operations, administration and customer service staff in the UAE. In the third phase, responses are examined using principal component analysis to identify eight major challenges for big data. A structural model is developed to examine the significance of these dimensions to the notion of big data challenges in supply chains. Findings The statistical model shows 66 percent variance of response to BDA, which is caused by technical, cultural, ethical, operational, tactical, procedural, functional and organizational challenges. These are positively correlated measurement challenges with BDA in service supply chains. Research limitations/implications Service supply chain professionals and stakeholders believe that catering to the challenges with BDA must be a multi-faceted approach and not limited to specific practices. Practical implications The challenges with BDA should be taken into planning and implementation from a holistic perspective. The framework in this paper can have both theoretical and practical implications. Originality/value The contribution of this paper is to advance the understanding of BDA in service sector by viewing it from the perspective of different stakeholders.

Challenges of using big data for humanitarian relief: lessons from the literature

2019 ◽  
Vol 10 (4) ◽  
pp. 423-446 ◽  
Author(s):  
Pankaj Sharma ◽  
Ashutosh Joshi
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Data Analytics ◽  
Descriptive Analysis ◽  
Big Data Analytics ◽  
Humanitarian Relief ◽  
Content Type ◽  
Digital World ◽  
Disaster Relief Operations ◽  
Relief Operations

Purpose Big data analytics has emerged as one of the most used keywords in the digital world. The hype surrounding the buzz has led everyone to believe that big data analytics is the panacea for all evils. As the insights into this new field are growing and the world is discovering novel ways to apply big data, the need for caution has become increasingly important. The purpose of this paper is to conduct a literature review in the field of big data application for humanitarian relief and highlight the challenges of using big data for humanitarian relief missions. Design/methodology/approach This paper conducts a review of the literature of the application of big data in disaster relief operations. The methodology of literature review adopted in the paper was proposed by Mayring (2004) and is conducted in four steps, namely, material collection, descriptive analysis, category selection and material evaluation. Findings This paper summarizes the challenges that can affect the humanitarian logistical missions in case of over dependence on the big data tools. The paper emphasizes the need to exercise caution in applying digital humanitarianism for relief operations. Originality/value Most published research is focused on the benefits of big data describing the ways it will change the humanitarian relief horizon. This is an original paper that puts together the wisdom of the numerous published works about the negative effects of big data in humanitarian missions.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

An experimental investigation of the impact of using big data analytics on customers’ performance measurement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwa Rabe Mohamed Elkmash ◽  
Magdy Gamal Abdel-Kader ◽  
Bassant Badr El Din
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Data Analytics ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Web Based ◽  
Content Type ◽  
The Impact

Purpose This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study. Design/methodology/approach Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data. Findings The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E). Research limitations/implications This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses. Practical implications This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies. Originality/value This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

Big data analytics capabilities and organizational performance: the mediating effect of dual innovations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofeng Su ◽  
Weipeng Zeng ◽  
Manhua Zheng ◽  
Xiaoli Jiang ◽  
Wenhe Lin ◽  
...  
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Empirical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mediating Effect ◽  
Content Type ◽  
Mediating Role ◽  
The Relationship

PurposeFollowing the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.Design/methodology/approachDrawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.FindingsThe results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.Originality/valueThe conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

Financial fraud detection and big data analytics – implications on auditors’ use of fraud brainstorming session

2019 ◽  
Vol 34 (3) ◽  
pp. 324-337 ◽  
Author(s):  
Jiali Tang ◽  
Khondkar E. Karim
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Idea Generation ◽  
Fraud Detection ◽  
Content Type ◽  
Collective Wisdom ◽  
Fraud Risk ◽  
Collection Data ◽  
Audit Team

PurposeThis paper aims to discuss the application of Big Data analytics to the brainstorming session in the current auditing standards.Design/methodology/approachThe authors review the literature related to fraud, brainstorming sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by applying Big Data analytics at different steps.FindingsThe existing audit practice aimed at identifying the fraud risk factors needs enhancement, due to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such concern as it draws on collective wisdom and encourages idea generation. The integration of Big Data analytics into brainstorming can broaden the information size, strengthen the results from analytical procedures and facilitate auditors’ communication. In the model proposed, an audit team can use Big Data tools at every step of the brainstorming process, including initial data collection, data integration, fraud indicator identification, group meetings, conclusions and documentation.Originality/valueThe proposed model can both address the current issues contained in brainstorming (e.g. low-quality discussions and production blocking) and improve the overall effectiveness of fraud detection.

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