Does Supply Chain Analytics Enhance Supply Chain Innovation and Robustness Capability?

Shamout, Mohamed Dawood

doi:10.2478/orga-2019-0007

Cited by 36 publications

(24 citation statements)

References 53 publications

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“…Also, Correa et al 64 provide a concrete implementation of web mining techniques in the food delivery services as a more specialized e‐commerce platform. Similarly, Shamout 65 examines the effectiveness of supply chain analytics in enhancing firms' supply chain innovation and robustness capability in the Arabian context by using variance‐based structural equation modeling (PLS‐SEM) to model the association between supply chain analytics, supply chain innovation and robustness capability. He concludes that supply chain analytics can help managers access timely and useful data for greater innovation.…”

Section: Review Of Related Literaturementioning

confidence: 99%

“…Makhabel et al 10 explore data mining techniques and show how to apply different mining concepts to various statistical and data applications in a wide range of fields, including description and implementation of a suite of social media mining techniques. Although these studies 10,60‐65 provide implementations of Web analytics, they differ from our approach in our choice of selected algorithms and goal.…”

Section: Review Of Related Literaturementioning

confidence: 99%

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Mining web content usage patterns of electronic commerce transactions for enhanced customer services

Ehikioya

Zeng

2021

Engineering Reports

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A successful business intelligence solution can help organizations improve the quality and speed of their decision‐making processes by analyzing the consolidated information collected from their websites. Using the current Web server log standard, which indicates only the locations of served Web pages, may lead to inaccurate business analysis for data driven and frequently updated static content Web pages. A properly defined Web content usage data warehouse that captures both dynamic and static contents of web pages provides rich data source for discovering interesting business rules among users' activities. This paper demonstrates the simplicity of data extract, transform and load procedures to import raw Web content usage log to various data models for data analysis, reporting and data mining tools. In this paper, we use two data mining techniques (expected maximization and the prefixspan algorithms) for visitor grouping and path analysis to find interesting patterns in Web content usage log data, an important component of e‐commerce Web sites traffic analysis. Visitor grouping uses data clustering to group visitors/sessions with similar selected attributes' value. Path analysis mines common visiting path sequence. The ultimate goal is to enable the online merchant provide enhanced and differentiated marketing services generally to its existing and potential customers and tell what items customers are interested in instead of ambiguous URLs.

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Section: Review Of Related Literaturementioning

confidence: 99%

Section: Review Of Related Literaturementioning

confidence: 99%

Mining web content usage patterns of electronic commerce transactions for enhanced customer services

Ehikioya

Zeng

2021

Engineering Reports

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“…The survey items were adopted from previous research and modified to fit the context of interest. We contacted two translators to back-translate the items from English to Arabic and vice versa (Jahmani et al, 2018;Shamout, 2019). Then, a pilot study was conducted with 15-employees working in a distribution company to control for and eliminate ambiguity.…”

Section: Sampling Context and Data Collectionmentioning

confidence: 99%

“…Only 271 firms agreed to participate in the study. In line with prior studies (Shamout, 2019), line managers in supply and logistics departments were asked to participate in the study because they control most of the supply chains operations in their firms. At the end of the survey, 271 forms were returned and forms with missing data were discarded.…”

Section: Sampling Context and Data Collectionmentioning

confidence: 99%

Supply chain data analytics and supply chain agility: a fuzzy sets (fsQCA) approach

Shamout

2020

IJOA

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Purpose Practitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such as supply chain performance and agility. The purpose of this paper is to examine a configural combination (i.e. causal recipes) subsuming supply chain data analytics, firmsize, age and annual sales to predict supply chain agility based on knowledge-based theory. Design/methodology/approach Survey data (n = 215) were obtained from firms operating in the United Arab Emirates. Consequently, fuzzy sets qualitative comparative analysis (fsQCA) technique was applied to the data to establish causal recipes that are necessary and sufficient to achieve high scores of supply chain agility. Findings Results from fsQCA support the major tenets of complexity theory that several configural combinations (i.e. supply chain data analytics, firm size, firm age and annual sales) are sufficient and necessary conditions for achieving higher scores of supply chain agility. Originality/value This study is first of its kind in understanding the association between supply chain data analytics and agility with fsQCA technique. This research also offers a headway for supply chain managers in identifying configural combinations of antecedents manifesting high scores for supply chain agility. Implications for theory and practice are illustrated as well as future research course.

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“…Innovation can be one of the most important determinants of competitiveness (Kovačič, 2007;Shamout, 2019). Smaller countries can through clusters and updated strategies achieve that their relatively smaller country size becomes advantage (Pitelis, 2008).…”

Section: Introductionmentioning

confidence: 99%

Drivers of Global Competitiveness in the European Union Countries in 2014 and 2017

Marčeta

Bojnec

2020

Organizacija

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Background and Purpose: The main purpose of this study is to find the key drivers of Global Competitiveness Index (GCI) in the European Union (EU-28) countries from the aspect of country’s global competitiveness: institutions, macroeconomic environment, infrastructure, higher education, market effectiveness, market size, technological readiness, innovation and business sophistication.Methodology: This paper investigates global competitiveness of the EU-28 countries with the use of GCI in the periods 2014-2015 and 2017-2018. The correlation analysis and regression analysis are applied for testing the set two hypotheses.Results: The empirical results confirmed our hypotheses that GCI is particularly significantly positively correlated with innovation and business sophistication, and universities-industry collaboration in researches, and clusters development.Conclusion: The paper contributes to the literature of global competitiveness, by examining the relationship of sub-indexes of competitiveness of the EU-28 countries, pointing out the influence of universities-industries collaboration in researches and cluster development with geographic concentration of companies. The results and findings can be relevant for science, economic and research policy, and managerial practices that enhance innovation and business sophistication for research in collaboration of companies, universities, higher education institutions, and decision makers. The implications of this study can be important for better understanding of drivers of the EU-28 countries global competitiveness.

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Does Supply Chain Analytics Enhance Supply Chain Innovation and Robustness Capability?

Cited by 36 publications

References 53 publications

Mining web content usage patterns of electronic commerce transactions for enhanced customer services

Mining web content usage patterns of electronic commerce transactions for enhanced customer services

Supply chain data analytics and supply chain agility: a fuzzy sets (fsQCA) approach

Drivers of Global Competitiveness in the European Union Countries in 2014 and 2017

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