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1 – 10 of over 4000Najah Almazmomi, Aboobucker Ilmudeen and Alaa A. Qaffas
In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive…
Abstract
Purpose
In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive advantage. Though widespread attention has been given to the above concepts, there hasn't been much research done on how it could support achieving competitive advantage.
Design/methodology/approach
This research strongly lies on the theoretical background and empirically tests the hypothesized relationships. The primary survey of 272 responses was analysed by using the partial least squares structural equation modelling (PLS-SEM).
Findings
The findings of this study show a significant relationship for the constructs in the research model except for the third hypothesis. Accordingly, the firm's data-driven culture does not have a significant impact on new product newness.
Originality/value
This study empirically tests the business analytics capability, data-driven culture, and new product development features in the context of a firm's competitive advantage. The findings of this study contribute to the theoretical, practical and managerial aspects of this field.
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Yao Chen, Liangqing Zhang, Meng Chen and Hefu Liu
Drawing on the knowledge-based view, this study investigates how IT–business alignment influences business model design via organizational learning and examines the moderating…
Abstract
Purpose
Drawing on the knowledge-based view, this study investigates how IT–business alignment influences business model design via organizational learning and examines the moderating role of data-driven culture in the relationship between IT–business alignment and business model design via organizational learning.
Design/methodology/approach
Using multi-respondent survey data collected from 597 Chinese firms, mediation and moderated mediation analyses were used to examine this study's hypotheses.
Findings
The mediation test results revealed organizational learning served as a mediator between IT–business alignment and two types of business model design (i.e. novelty- and efficiency-centered). In addition, data-driven culture strengthened the indirect effects of IT–business alignment on these two types of business model design via organizational learning.
Originality/value
This study extends current understandings of the relationship between IT–business alignment and business model design by revealing the mediating role of organizational learning and investigating its indirect effects under various degrees of data-driven culture. As such, it contributes to the literature on the business model and IT–business alignment and provides insights for managers seeking to achieve the expected business model design.
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Yang Liu, Wei Fang, Taiwen Feng and Na Gao
Based on organizational information processing theory, this research explores how big data analytics capability (BDAC) contributes to green supply chain integration (GSCI) and the…
Abstract
Purpose
Based on organizational information processing theory, this research explores how big data analytics capability (BDAC) contributes to green supply chain integration (GSCI) and the contingency role that data-driven decision culture plays.
Design/methodology/approach
Using the two-wave survey data collected from 317 Chinese manufacturing firms, the authors validate the hypotheses.
Findings
The results show that big data managerial capability has positive impacts on three dimensions of GSCI, while big data technical capability has positive impacts on green internal and customer integration. Moreover, green internal integration mediates the impacts of big data technical capability and managerial capability on green supplier and customer integration. Finally, data-driven decision culture alleviates the positive impacts of big data technical and managerial capability on green internal integration.
Practical implications
The findings suggest that firms can leverage big data technical and managerial capability to enhance information processing capability for achieving a higher degree of GSCI. Further, the critical role of data-driven decision culture in affecting the link between BDAC and GSCI should not be overlooked.
Originality/value
This research contributes to literature on green supply chain management by revealing the role of BDAC in improving GSCI.
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Valeriia Boldosova and Severi Luoto
The purpose of this paper is to explore the role of storytelling in data interpretation, decision-making and individual-level adoption of business analytics (BA).
Abstract
Purpose
The purpose of this paper is to explore the role of storytelling in data interpretation, decision-making and individual-level adoption of business analytics (BA).
Design/methodology/approach
Existing theory is extended by introducing the concept of BA data-driven storytelling and by synthesizing insights from BA, storytelling, behavioral research, linguistics, psychology and neuroscience. Using theory-building methodology, a model with propositions is introduced to demonstrate the relationship between storytelling, data interpretation quality, decision-making quality, intention to use BA and actual BA use.
Findings
BA data-driven storytelling is a narrative sensemaking heuristic positively influencing human behavior towards BA use. Organizations deliberately disseminating BA data-driven stories can improve the quality of individual data interpretation and decision-making, resulting in increased individual utilization of BA on a daily basis.
Research limitations/implications
To acquire a deeper understanding of BA data-driven storytelling in behavioral operational research (BOR), future studies should test the theoretical model of this study and focus on exploring the complexity and diversity in individual attitudes toward BA.
Practical implications
This study provides practical guidance for business practitioners who struggle with interpreting vast amounts of complex data, making data-driven decisions and incorporating BA into daily operations.
Originality/value
This cross-disciplinary study develops existing BOR, storytelling and BA literature by showing how a novel BA data-driven storytelling approach can facilitate BA adoption in organizations.
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Franziska Franke and Martin R.W. Hiebl
Existing research on the relationship between big data and organizational decision quality is still few and far between, and what does exist often assumes direct effects of big…
Abstract
Purpose
Existing research on the relationship between big data and organizational decision quality is still few and far between, and what does exist often assumes direct effects of big data on decision quality. More recent research indicates that such direct effects may be too simplistic, and in particular, an organization’s overall human skills are often not considered sufficiently. Inspired by the knowledge-based view, we therefore propose that interactions between three aspects of big data usage and management accountants’ data analytics skills may be key to reaching high-quality decisions. The purpose of this study is to test these predictions based on a survey of US firms.
Design/methodology/approach
The authors draw on survey data from 140 US firms. This survey has been conducted via MTurk in 2020.
Findings
The results of the study show that the quality of big data sources is associated with higher perceived levels of decision quality. However, according to the results, the breadth of big data sources and a data-driven culture only improve decision quality if management accountants’ data analytics skills are highly developed. These results point to the important, but so far unexamined role of an organization’s management accountants and their skills for translating big data into high-quality decisions.
Practical implications
The present study highlights the importance of an organization’s human skills in creating value out of big data. In particular, the findings imply that management accountants may need to increasingly draw on data analytics skills to make the most out of big data for their employers.
Originality/value
This study is among the first, to the best of the authors’ knowledge, to provide empirical proof of the relevance of an organization’s management accountants and their data analytics skills for reaching desirable firm-level outcomes. In addition, this study thus adds to the further advancement of the knowledge-based view by providing evidence that in contemporary big-data environments, interactions between tacit and explicit knowledge seem crucial for driving desirable firm-level outcomes.
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Mauricius Munhoz de Medeiros, Norberto Hoppen and Antonio Carlos Gastaud Maçada
This paper aims to identify the benefits of data science (DS) for organizations, highlighting the challenges and opportunities related to developing this capability.
Abstract
Purpose
This paper aims to identify the benefits of data science (DS) for organizations, highlighting the challenges and opportunities related to developing this capability.
Design/methodology/approach
Initially, a literature review was performed. Later, empirical data were collected through a structured electronic interview answered by 211 informants, who are most experienced managers of medium and large organizations from different economic sectors, and data were submitted to content analysis.
Findings
The most frequently observed benefits are as follows: support for data analysis and insight generation with agility; creation of a data-driven culture; improvement of data quality; facilitating the understanding of the business environment, opportunity sensing; and organizational performance management. The most observed challenges are as follows: data-driven culture; DS training; allocation of investments in analytical technologies; and data governance and strategy.
Research limitations/implications
In addition, to mapping the state of the art on the subject, it contributes to the expansion of scientific knowledge through the identification and disclosure of 11 benefit indicators and 16 challenge indicators associated with analytical capabilities.
Practical implications
To transform data into information and add value to the business, organizations need to make efforts to enable executive mindset change, the formulation of strategies and governance mechanisms gave the renewal of workforce competencies and the allocation of investments in information technology.
Originality/value
A vast body of empirical evidence is gathered that consolidates different views on the benefits and challenges associated with DS for business.
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The role of supply chain members is often relegated to an ancillary role in brand building. Do distributors serve only as a conduit for movement of products confined to the…
Abstract
The role of supply chain members is often relegated to an ancillary role in brand building. Do distributors serve only as a conduit for movement of products confined to the business-to-business (B2B) market or do they possess the capability to transcend boundaries and help build the brand of the products that they distribute? Using a case study methodology, an exploration has been carried out on the success journey of Al Seer Group, one of the biggest and oldest Fast Moving Consumer Goods distributors in the United Arab Emirates, driven by the vision to be a brand-building partner. The organization is propelled by a robust people strategy, a process-based operational framework, a data-driven culture and a strategic reorientation that helped them to introduce the brand-building perspective to their stakeholders. This study encourages further research interest on employee retention strategies focused on the Millennial and Gen Z workforce, challenges of data-driven organizations in implementation of emerging technologies, the role of C-suite executives in organizational strategic orientation, and the brand-building perspective of B2B distributors.
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Eunhwa Yang and Ipsitha Bayapu
This paper aims to investigate data elements, transfer, gaps and the challenges to implement data analytics in facilities management. The goal is not to search for a definite…
Abstract
Purpose
This paper aims to investigate data elements, transfer, gaps and the challenges to implement data analytics in facilities management. The goal is not to search for a definite solution but to gather necessary information, understand the challenges faced and develop a proper foundation for future study.
Design/methodology/approach
This paper used a case study approach with a qualitative method. The case of the Georgia Institute of Technology was investigated by having a semi-structured interview with six relevant personnel. The recorded interview content was analyzed and presented based on six work processes.
Findings
Higher education institutions are taking initiatives but facing challenges in implementing data analytics. There were 36 software tools used to manage different aspects of facilities at Georgia Tech. Identified data elements and data processing indicated that major challenges for data-driven decision-making were inconsistency in data input and structure, the issue of interoperability among different software tools and a lack of software training.
Research limitations/implications
The authors only interviewed individuals who work closely with data gathering, transfer and processing. Thus, the study did not explore the perspective of individuals in the leadership level or the user group level.
Originality/value
Facilities management departments in higher education institutions perform multi-disciplinary functions, including building automation, continuous commissioning and preventative maintenance, all of which are data- and technology-intensive. Managing this overwhelming amount of information is often a challenge, but well-planned data analytics can be used to draw keen insights about any aspect of facilities management and operations and assist in evidence-based decision-making.
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This study mainly aims to explore the causal nexus between big data-driven organizational capabilities (BDDOC) and supply chain innovation capabilities (SCIC) and innovation…
Abstract
Purpose
This study mainly aims to explore the causal nexus between big data-driven organizational capabilities (BDDOC) and supply chain innovation capabilities (SCIC) and innovation performance (IP), then explore the indirect effect of SCIC and also test the moderating effects for both internal supply chain integration (ISCI) and external supply chain integration (ESCI) into the relationship between BDDOC and SCIC.
Design/methodology/approach
In order to test the conceptual model and the hypothesized relationships between all the constructs, the data were collected using a self-reported questionnaire by workers in Jordanian small and medium manufacturing enterprises. Partial least squares-structural equation modeling (PLS-SEM) was employed to test the model.
Findings
The paper reached a set of interesting results where it was confirmed that there is a positive and statistically significant relationship between BDDOC, SCIC and IP in addition to confirming the indirect effect of SCIC between BDDOC and IP. The results also showed that there is a moderating role for both ESCI and ISCI.
Originality/value
This study can be considered the first study in the current literature that investigates these constructs as shown in the research model. Therefore, the paper presents an interesting set of theoretical and managerial contributions that may contribute to covering part of the research gap in the literature.
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Mohammad Osman Gani, Yoshi Takahashi, Surajit Bag and Muhammad Sabbir Rahman
This study examines the relationships between collaboration capability (CC) and supply chain risk management (SCRM) and the mediating role of supply chain alignment (SCA) between…
Abstract
Purpose
This study examines the relationships between collaboration capability (CC) and supply chain risk management (SCRM) and the mediating role of supply chain alignment (SCA) between CC and SCRM. It also investigates the moderating role of data-driven culture (DDC) on the path joining CC and SCA.
Design/methodology/approach
A survey was conducted via a structured questionnaire among the 297 managers of Business-to-business (B2B) firms. The data were analyzed using partial least squares structural equation modeling (PLS-SEM).
Findings
The result found a positive and significant relationship between CC and SCRM, CC and SCA, and SCA and SCRM. The research found a significant mediating role of SCA between CC and SCRM. The study also found a significant moderating role of DBC between CC and SCA.
Practical implications
The outcomes demonstrate the management and societal relevance of the study from the viewpoint of businesses in developing nations. To cope with dynamic shifts, managers and decision makers need to take initiative for collaboration among the supplier, to align with their supply chain operations and enhance preparedness of DDC to overcome supply chain-related risks in the future.
Originality/value
The results of this empirical study have the significant potential to provide valuable guidance and insights about the B2B firms’ CC to develop SCA to enhance SCRM as risk management for the supply chain can aid in loss prevention and provide an edge over competitors. To the best of the authors’ knowledge, these relationships based on the dynamic capability view (DCV) add to existing studies on B2B firms’ supply chains in a novel way.
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