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Article
Publication date: 15 June 2023

Denise Chenger and Rachael N. Pettigrew

Companies are turning to big data (BD) programs to help mitigate supply chain (SC) disruptions and risks that are increasing in frequency and severity. The purpose of this paper…

Abstract

Purpose

Companies are turning to big data (BD) programs to help mitigate supply chain (SC) disruptions and risks that are increasing in frequency and severity. The purpose of this paper is to explore exactly how companies translate data into meaningful information used to manage SC risk and create economic value; an area not well researched. As companies are turning to big-data programs to help mitigate supply chain (SC) disruptions and risks that are increasing in frequency and severity, having the capability to internally integrate SC information is cited as the most critical risk to manage.

Design/methodology/approach

Information processing theory and resource-based view are applied to support capability development used to make value-based BD decisions. Semi-structured interviews were conducted with leaders in both the oil and gas industry and logistics SC partners to explore each companies’ BD transformation.

Findings

Findings illuminate how companies can build internal capability to more effectively manage SC risk, optimize operating assets and drive employee engagement.

Research limitations/implications

The oil and gas industry were early adopters of gathering BD; more studies addressing how companies translate data to create value and manage SC risk would be beneficial.

Practical implications

Guidance for senior leaders to proactively introduce BD to their company through a practical framework. Further, this study provides insight into where the maximum benefit may reside, as data intersects with other company resources to build an internal capability.

Originality/value

This study presents a framework highlighting best practices for introducing BD plus creating a culture capable of using that data to reduce risk during design, implementation and ongoing operations. The steps for producing the maximum benefit are laid out in this study.

Article
Publication date: 7 February 2024

Moh’d Anwer AL-Shboul

This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the…

Abstract

Purpose

This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the manufacturing sector in the countries located in North Africa (NA). These are considered developing countries through generating green product innovation (GPI) and using green process innovations (GPrLs) in their processes and functions as mediating factors, as well as the moderating role of data-driven competitive sustainability (DDCS).

Design/methodology/approach

To achieve the aim of this study, 346 useable surveys out of 1,601 were analyzed, and valid responses were retrieved for analysis, representing a 21.6% response rate by applying the quantitative methodology for collecting primary data. Convergent validity and discriminant validity tests were applied to structural equation modeling (SEM) in the CB-covariance-based structural equation modeling (SEM) program, and the data reliability was confirmed. Additionally, a multivariate analysis technique was used via CB-SEM, as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Further, a bootstrapping technique was used to analyze the data. We included GPI and GPrI as mediating factors, while using DDCS as a moderated factor.

Findings

The empirical findings indicated that the proposed moderated-mediation model was accepted due to the relationships between the constructs being statistically significant. Further, the findings showed that there is a significant positive effect in the relationship between reliable BCDA capabilities and CAs as well as a mediating effect of GPI and GPrI, which is supported by the proposed formulated hypothesis. Additionally, the findings confirmed that there is a moderating effect represented by data-driven competitive advantage suitability between GPI, GPrI and CA.

Research limitations/implications

One of the main limitations of this study is that an applied cross-sectional study provides a snapshot at a given moment in time. Furthermore, it used only one type of methodological approach (i.e. quantitative) rather than using mixed methods to reach more accurate data.

Originality/value

This study developed a theoretical model that is obtained from reliable BCDA capabilities, CA, DDCS, green innovation and GPrI. Thus, this piece of work bridges the existing research gap in the literature by testing the moderated-mediation model with a focus on the manufacturing sector that benefits from big data analytics capabilities to improve levels of GPI and competitive advantage. Finally, this study is considered a road map and gaudiness for the importance of applying these factors, which offers new valuable information and findings for managers, practitioners and decision-makers in the manufacturing sector in the NA region.

Details

International Journal of Productivity and Performance Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0401

Keywords

Article
Publication date: 8 June 2023

Gioconda Mele, Guido Capaldo, Giustina Secundo and Vincenzo Corvello

In the landscape created by digital transformation, developing the ability to adapt and innovate by absorbing and generating new knowledge has become a strategic priority for…

1514

Abstract

Purpose

In the landscape created by digital transformation, developing the ability to adapt and innovate by absorbing and generating new knowledge has become a strategic priority for organizations. The theory of dynamic capabilities, especially from a knowledge-based perspective, has proven particularly useful in studying the phenomena of transformation and change. Moving from this premise, this paper aims to map the state of research and to define guidelines for the actualization of dynamic capabilities theory in the digital transformation era.

Design/methodology/approach

A structured literature review of 75 papers, using descriptive, bibliographic and content analysis, was performed to analyze the evolution of dynamic capabilities in the context of digital transformation.

Findings

Studies concerning knowledge-based dynamic capabilities for digital transformation have been clustered into five main research areas: the micro-foundation of dynamic capabilities for digital transformation; dynamic capabilities for value creation in digital transformation; dynamic capabilities for digital transition in specific industries; dynamic capabilities for “data-driven organizations”; and dynamic capabilities for digital transformation in SMEs and family firms. A future research agenda for scholars in strategic management is presented.

Practical implications

A conceptual framework and a future research agenda are presented to highlight directions for this promising research field concerning the renewal of dynamic capabilities in the context of digital transformation.

Originality/value

The originality of the paper lies in the conceptual framework aiming to systematize current research on knowledge-based dynamic capabilities for digital transformation and to provide a new conceptualization of digital dynamic capabilities, clarifying how organizations create and share knowledge in the era of digitalization.

Article
Publication date: 8 December 2022

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.

Details

Benchmarking: An International Journal, vol. 30 no. 10
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 8 September 2023

Xinmeng Liu, Suicheng Li, Xiang Wang and Cailin Zhang

Data transformation has prompted enterprises to rethink their strategic development. Scholars have frequently acknowledged the vast potential value of supply chain data and…

Abstract

Purpose

Data transformation has prompted enterprises to rethink their strategic development. Scholars have frequently acknowledged the vast potential value of supply chain data and realised that simply owning data resources cannot guarantee excellent innovation performance (IP). Therefore, this study focussed on the mediating and moderating issues between data-driven supply chain orientation (DDSCO) and IP. More specifically, the purpose was to explore (1) whether DDSCO promotes enterprise innovation through dynamic and improvisational capabilities and (2) how information complexity (INC) plays a moderating role between capabilities and performance.

Design/methodology/approach

An empirical study was performed using the results of a questionnaire survey, and a literature review was used to build the premises of this study. A sample was conducted on 296 Chinese enterprises, and the data collected were used to test the hypothesis by successive regression.

Findings

This research has implications for the theoretical development of DDSCO, as well as the dynamic capabilities (DC) and improvisation capabilities (IC) in innovation strategic literature. The empirical results show that DDSCO has a direct, positive impact on both DC and IC, which thus positively impact IP. Meanwhile, IC has a negative moderating effect on the path joining DC and IP. Conversely, IC has a positive moderating effect on the path joining IC and IP.

Research limitations/implications

Although this study has limitations, it also creates opportunities for future research. The survey comes from different industries, so the possibility of unique influences within industries cannot be ruled out. Second, the authors' survey is based on cross-sectional data, which allow for more comprehensive data verification in the future. Third, this study also provides opportunities for future research, because it proves that DC and IC, as partial mediators of DDSCO and IP, can mine other paths of the data-driven supply chain in IP. For example, the perspective of the relationship between supply chain members, knowledge perspective, etc.

Practical implications

The research findings offer a novel perspective for enterprise managers. First, enterprises can leverage supply chain data to gain competitive advantages in innovation. Second, it is imperative for enterprises to acknowledge the significance of developing dynamic and IC. This also requires enterprises to acknowledge innovations in DDSCO necessitate a focus on dynamic and IC. Third, it is recommended that managers take into account both sides of IC and encourage enterprises to prioritise the utilisation of IC.

Originality/value

Empirical research results revealed how DDSCO improves IP and is an extension of digital transformation in the supply chain field, providing new opportunities and challenges for enterprise innovation. It can also expand the enterprise's understanding of DDSCO. Second, based on resource-based theory, it is possible to develop and test theoretical arguments regarding the importance of dynamic and IC as intermediaries in the DDSCO-IP. Third, the authors conducted simulations of highly dynamic data environments to develop and test theoretical arguments about the importance of IC as a moderator of capabilities-performance relationships.

Details

European Journal of Innovation Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 10 April 2023

Francesca Conte and Alfonso Siano

Previous research assumes that technologies 4.0, particularly big data, may be highly relevant for organizations to increase human resources (HR) communication strategies, but the…

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Abstract

Purpose

Previous research assumes that technologies 4.0, particularly big data, may be highly relevant for organizations to increase human resources (HR) communication strategies, but the research provides little or no evidence on whether and how these tools are applied in employees and labor market relations. This study intends to offer a first insight on the adoption of data-driven HR/talent management approach, contributing to the ongoing debate on the Industry 4.0. This study aims to investigate the use of 4.0 technologies in HR and talent management functions, focusing also on the adoption of big data analytics for internal and recruitment communication.

Design/methodology/approach

The analysis of the literature enables to define the research questions and an exploratory web survey was carried out through a structured questionnaire. The analysis unit of the empirical survey includes the communication and marketing managers of 90 organizations in Italy, examined in the Mediobanca Report on the “Main Italian Companies.”

Findings

Findings highlight a lack of the use of 4.0 technologies and big data analytics in employee and labor market relations and reveal some sectoral differences in the adoption of 4.0 technologies. Moreover, the study points out that the development of HR analytics is hampered by short-term perspective, data quality problems and the lack of analytics skills.

Research limitations/implications

Due to the exploratory research design and the circumscribed sample from a single country (Italy), further cross-national evidence is needed. This study provides digital communication managers with useful insights to improve the data-driven HR/talent management approach, which is a strategic asset for ensuring a sustainable competitive advantage and optimizing business performance.

Originality/value

The study offers an overview about the use of big data analytics in internal and recruitment communications. Considering the alignment between Italian and European trends in the use of big data and in the adoption of HR analytics, the study can provide insights also for other European organization.

Details

Corporate Communications: An International Journal, vol. 28 no. 4
Type: Research Article
ISSN: 1356-3289

Keywords

Article
Publication date: 5 October 2023

Kaikai Shi, Hanan Lu, Xizhen Song, Tianyu Pan, Zhe Yang, Jian Zhang and Qiushi Li

In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn…

Abstract

Purpose

In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions.

Design/methodology/approach

This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss.

Findings

The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%.

Originality/value

The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 1
Type: Research Article
ISSN: 0961-5539

Keywords

Article
Publication date: 10 October 2023

Moh'd Anwer AL-Shboul

This study attempts to examine the relationship between reliable big and cloud data analytics capabilities (RB&CDACs) and comparative advantages (CA) of manufacturing firms (MFs…

Abstract

Purpose

This study attempts to examine the relationship between reliable big and cloud data analytics capabilities (RB&CDACs) and comparative advantages (CA) of manufacturing firms (MFs) in the Middle East region as developing countries using green product innovation (GPI) and green process innovations (GPrI) mediating factors, further assess the role of data-driven competitive sustainability factor as a moderated factor.

Design/methodology/approach

436 useable online surveys were analyzed using the quantitative approach for the data-gathering process, applying structural equation modeling in the Smart-PLS program as an analysis tool. The sample unit for analysis included all middle- and senior-level managers and employees within MFs. The authors performed convergent validity and discriminant validity tests, bootstrapping also was applied. The authors included GPI and GPrI as mediating factors while using data-driven competitive sustainability as a moderated factor.

Findings

The findings of this study indicated that there is a positive significant effect in the relationship between reliable big and cloud data analytics capabilities and comparative advantages, which is supported by the formulated hypothesis. Furthermore, the findings confirmed that there was a positive and significant effect through the mediating factors (i.e. GPI and GPrI) on comparative advantage, additionally, it confirmed and supported that the moderating factor represented by data-driven competitive advantage suitability has significant effect as well.

Research limitations/implications

This study has some limitations represented by using only one type of methodological approach (i.e. quantitative), further, it was conducted on only Asian countries in the Middle East region.

Originality/value

This piece of work improved the proposed conceptual research model and included several factors such as reliable big and cloud data analytics capabilities, comparative advantage, data-driven competitive sustainability, GPI and GPrI. This research offered new and valuable information and findings for managers, practitioners and decision-makers in the MFs in the Middle East region as a road map and gaudiness for the importance to apply these factors in their firms for enhancing the comparative advantages in their firms. Further, this research fills the gap in SCM literature and makes a bridge of knowledge and contribution to the existence of previous studies.

Article
Publication date: 11 April 2023

Augusto Bargoni, Fauzia Jabeen, Gabriele Santoro and Alberto Ferraris

Few studies have conceptualized how companies can build and nurture international dynamic marketing capabilities (IDMCs) by implementing growth hacking strategies. This paper…

Abstract

Purpose

Few studies have conceptualized how companies can build and nurture international dynamic marketing capabilities (IDMCs) by implementing growth hacking strategies. This paper conceptualizes growth hacking, a managerial-born process to embed a data-driven mind-set in marketing decision-making that combines big-data analysis and continuous learning, allowing companies to adapt their dynamic capabilities to the ever-shifting international competitive arenas.

Design/methodology/approach

Given the scarcity of studies on growth hacking, this paper conceptualizes this managerial-born concept through the double theoretical lenses of IDMCs and information technology (IT) literature.

Findings

The authors put forward research propositions concerning the four phases of growth hacking and the related capabilities and routines developed by companies to deal with international markets. Additional novel propositions are also developed based on the three critical dimensions of growth hacking: big data analytics, digital marketing and coding and automation.

Research limitations/implications

Lack of prior conceptualization as well as the scant literature makes this study liable to some limitations. However, the propositions developed should encourage researchers to develop both empirical and theoretical studies on this managerial-born concept.

Practical implications

This study develops a detailed compendium for managers who want to implement growth hacking within their companies but have failed to identify the necessary capabilities and resources.

Originality/value

The study presents a theoretical approach and develops a set of propositions on a novel phenomenon, observed mainly in managerial practice. Hence, this study could stimulate researchers to deepen the phenomenon and empirically validate the propositions.

Article
Publication date: 2 May 2022

Alaa A. Qaffas, Aboobucker Ilmudeen, Najah Kalifah Almazmomi and Ibraheem Mubarak Alharbi

The emerging attention in big data has led businesses to improve big data analytics talent capability to enrich firm performance. The big data capability pays off for some…

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Abstract

Purpose

The emerging attention in big data has led businesses to improve big data analytics talent capability to enrich firm performance. The big data capability pays off for some companies but not for all, and it appears that very few have achieved a big impact through big data. Rooted in the latest literature on the knowledge-based view, IT capability, big data talent capability and business intelligence, this study aims to examine how big data talent capability impact on business intelligence infrastructure to achieve firm performance.

Design/methodology/approach

The primary survey data of 272 IT managers and big data analysts from Chinese firms was analyzed by using the structural equation modeling and partial least squares (Smart PLS 3.0). The analysis uncovers a positive and significant relationship in the proposed model.

Findings

The finding shows that the big data analytics talent capability positively impacts on business intelligence infrastructure that in turn directs to achieve firm financial and marketing performance.

Originality/value

This study theorized on the multitheoretic lenses, and findings suggest the managers and industry practitioners to develop business intelligence infrastructure capabilities from big data analytics talent capability.

Details

foresight, vol. 25 no. 3
Type: Research Article
ISSN: 1463-6689

Keywords

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