Big data analytics adoption: Determinants and performances among small to medium-sized enterprises

Maroufkhani, Parisa; Tseng, Ming‐Lang; Iranmanesh, Mohammad; Ismail, Wan Khairuzzaman Wan; Khalid, Haliyana

doi:10.1016/j.ijinfomgt.2020.102190

Cited by 204 publications

(242 citation statements)

References 137 publications

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“…Prior studies have discovered the of big data components for building the SSCM ( Akter et al, 2016 ; Zhan and Tan, 2020 ). Maroufkhani et al, (2020) proposed a data-driven analysis to obtain the technological-organizational-environmental paradigm to implement the lessening resource utilization and emission reduction solutions in SSCM systems. Majeed et al (2021) developed a modeling structure by uniting big data analytics to additive manufacturing, and sustainable smart manufacturing technologies which is advantageous to the additive manufacturing initiatives.…”

Section: Data Collection and Methodsologymentioning

confidence: 99%

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Bui

Tsai

Tseng

et al. 2021

Sustainable Production and Consumption

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150

111

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Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation.

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Section: Data Collection and Methodsologymentioning

confidence: 99%

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Bui

Tsai

Tseng

et al. 2021

Sustainable Production and Consumption

Self Cite

150

111

View full text Add to dashboard Cite

show abstract

“…These adversities are further exacerbated by a dearth of 5 empirical studies examining the analytics' effect on the performance of SMEs (e.g. Maroufkhani et al, 2020, Liu et al, 2020, Wang et al, 2018.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on the literature review, the following gaps concerning marketing analytics use are identified and will be addressed in this study: First, although studies have established the link between the use of business analytics and improved firm performance such as innovation (e.g., Duan et al, 2020), decision making effectiveness (e.g., Cao et al, 2015), supply chain performance (e.g., Zhan and Tan, 2020), and competitive advantages (e.g., Cao et al, 2019, WangYeoh et al, 2019, the majority of these studies are based on large companies and their findings should not be applied to SMEs directly without further investigation, as SMEs are not smaller versions of larger firms (O'Regan et al, 2005). Aside from only a few studies (e.g., Ferraris et al, 2019, Maroufkhani et al, 2020, Hansen and Bøgh, 2020, Liu et al, 2020, business/marketing analytics use and its effect on the performance of SMEs are yet to be fully investigated (e.g., Maroufkhani et al, 2020, Liu et al, 2020, Wang et al, 2018. Second, although prior studies have used configurational approaches to understand organizational relationships in SMEs (e.g., Hughes et al, 2019, Uwizeyemungu et al, 2018, Poorkavoos et al, 2016, Magistretti et al, 2020, no research has investigated how the configurations of multiple conditions affect business/marketing analytics use in SMEs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…de Haan et al, 2007, Haase andFranco, 2011) and tends to be associated with different patterns of IS investment and use (e.g. Gillon et al, 2014, Thong et al, 1996, Haase and Franco, 2011, Dong and Yang, 2020, almost all prior analytics studies have examined SMEs as one homogeneous group (e.g., Ferraris et al, 2019, Maroufkhani et al, 2020, Hansen and Bøgh, 2020, Liu et al, 2020. Thus, whether or not small and mediumsized firms differ in terms of marketing analytics use remains unexamined in empirical research.…”

Section: Literature Reviewmentioning

confidence: 99%

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Identifying the configurational conditions for marketing analytics use in UK SMEs

Duan

Tian

2021

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PurposeWhile marketing analytics can be used to improve organizational decision-making and performance significantly, little research exists to examine how the configurations of multiple conditions affect marketing analytics use. This study draws on configuration theory to investigate marketing analytics use in small and medium-sized enterprises (SMEs).Design/methodology/approachThis research employs a fuzzy-set qualitative comparative analysis using data collected from a survey of 187 managers in UK SMEs.FindingsThe key findings show that (1) configurations of multiple conditions provide alternative pathways to marketing analytics use, and (2) the configurations for small firms are different from those for medium-sized firms.Research limitations/implicationsThe research results are based on several key configurational factors and a single key-informant method to collect subjective data from UK SME managers.Practical implicationsThe study helps SMEs to understand that marketing analytics use is influenced by the interaction of multiple conditions, that there are alternative pathways to marketing analytics use, and that SMEs should choose the configuration that fits best with their organizational contexts.Originality/valueThe study contributes to the literature by addressing an important yet underresearched area, i.e. marketing analytics use in SMEs, applying a configurational approach to the research phenomenon. It highlights different pathways to marketing analytics use in SMEs. The findings provide empirical evidence on the possibility and implication of marketing analytics use being asymmetrical and different between small and medium-sized firms.

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“…This adoption process is widely studied in different sectors such as healthcare (Chen et al, 2020 ), industrial (McMahon et al, 2020 ), or tourism (Yadegaridehkordi et al, 2020 ) although all of them refer to generic Big Data techniques while there is little literature on the adoption process of Big Data Analytics (Maroufkhani et al, 2020 ) as can be seen check from the appropriate literature review (Inamdar et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

et al. 2021

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The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications.

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Big data analytics adoption: Determinants and performances among small to medium-sized enterprises

Cited by 204 publications

References 137 publications

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Identifying the configurational conditions for marketing analytics use in UK SMEs

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

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