You have not been archiving emails for no reason! Using big data analytics to cluster B2B interest in products and services and link clusters to financial performance

Yang, Yang; See-To, Eric Wing Kuen; Papagiannidis, Savvas

doi:10.1016/j.indmarman.2019.01.016

Cited by 33 publications

(24 citation statements)

References 39 publications

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“…Maintaining strategic fit requires capabilities to monitor demand trends regularly over time using both numeric and textual data sources (Yang, See-To and Papagiannidis, 2019). At the operational level, the SCA has impact on process optimization, capabilities to predict capacity use, awareness considering critical contact points between employees and improved possibilities to experimentation of competing processes constructs (Côrte-Real et al, 2019).…”

Section: Effect Of Data Analytics Utilization On Supply Chain Performancementioning

confidence: 99%

Digitalizing procurement: the impact of data analytics on supply chain performance

Hallikas

Immonen

Brax

2021

SCM

114

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Purpose This study aims to investigate digitalization as a performance driver in supply chains, especially the role of data analytics in the digitalization of procurement. The study investigates how digital procurement capabilities are linked to data analytics capabilities and supply chain operational performance and how this links to business success. Design/methodology/approach Using operational and dynamic capabilities as foundations for data analytics capabilities, this paper studied the digital procurement capabilities and proposed the conceptual model and hypotheses for empirical testing. The collected industry survey data and structural equation method are then applied to test the hypotheses. Findings The study confirms positive and significant relationships among digital procurement capabilities, data analytics capabilities and supply chain performance. Digital procurement capabilities mediate the positive relationship between external data analytics capabilities and supply chain performance. Research limitations/implications This study has some limitations that should be addressed. The empirical study was based on survey data from a questionnaire that was probably challenging for some respondent companies with low levels of digital procurement and data analytics. Also, it is necessary to adopt secondary data to measure business performance in future studies which reduces the effect of subjective bias. Practical implications From the managerial point of view, the findings highlight the importance of gaining knowledge from gathered data and digitalized processes. Managers must focus on data utilization capabilities to improve the operational performance expected from the digitalization of supply chain activities. In addition, managers need to consider exploiting of data through new creative approaches as part of standardized operations. Originality/value The present study contributes to existing knowledge by investigating the mediating role of data analytics capabilities between the digitalization of procurement and supply chain performance. The findings support a positive relationship between the data analytics capabilities and supply chain performance in digital upstream supply chain procurement processes. The present study also clarifies the impact and role of data analytics capabilities in digital supply chain development and success.

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Section: Effect Of Data Analytics Utilization On Supply Chain Performancementioning

confidence: 99%

Digitalizing procurement: the impact of data analytics on supply chain performance

Hallikas

Immonen

Brax

2021

SCM

114

View full text Add to dashboard Cite

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“…In this view, the BSD paradigm allows for contributing to the debate on the role of Big Data under a KM perspective, which is still a topic under researched (Chierici et al, 2018;Yang et al, 2020),…”

Section: Discussionmentioning

confidence: 99%

Defining the big social data paradigm through a systematic literature review approach

Solazzo

Elia

Passiante

2021

JKM

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Purpose This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources. Design/methodology/approach The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques. Findings The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective. Research limitations/implications The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field. Practical implications Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories. Originality/value The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.

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“…Machine Learning (también denominado Data Science o Data Mining de acuerdo al contexto) abarca una serie de técnicas de modelamiento orientadas a distintos requerimientos de las organizaciones (predicción de resultados, análisis de textos, visión artificial, reconocimiento de voz, entre otras), las cuales pueden ser aplicados en distintas tareas en entornos empresariales (Allmendinger et al, 2017;Yang et al, 2019). En el caso del sector educativo, el uso de estas técnicas puede apreciarse en una serie de trabajos (Castrillón et al, 2020;Etxeberria et al, 2017;Ezz & Elshenawy, 2019) orientados a la predicción del rendimiento académico de estudiantes, mientras que Salloum et al 2018presenta un enfoque de minería de textos para extraer información de artículos de investigación.…”

Section: Materiales Y Métodosunclassified

Uso de minería de textos para comparar los contenidos relacionados a calidad y acreditación generados en redes sociales por universidades de Perú y Chile

Núñez

Crisóstomo

Sánchez³

2021

Form. Univ.

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You have not been archiving emails for no reason! Using big data analytics to cluster B2B interest in products and services and link clusters to financial performance

Cited by 33 publications

References 39 publications

Digitalizing procurement: the impact of data analytics on supply chain performance

Digitalizing procurement: the impact of data analytics on supply chain performance

Defining the big social data paradigm through a systematic literature review approach

Uso de minería de textos para comparar los contenidos relacionados a calidad y acreditación generados en redes sociales por universidades de Perú y Chile

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