Social media analytics, competitive intelligence, and dynamic capabilities in manufacturing SMEs

2022 ◽  
Vol 175 ◽  
pp. 121416
Author(s):  
Abdeslam Hassani ◽  
Elaine Mosconi
Keyword(s):  
Social Media ◽  
Dynamic Capabilities ◽  
Competitive Intelligence ◽  
Social Media Analytics

scholarly journals Examining the Role of Social Media Analytics in Providing Competitive Intelligence

2021 ◽  
Vol 29 (6) ◽  
pp. 1-18
Author(s):  
Jiwat Ram ◽  
Changyu Zhang
Keyword(s):  
Social Media ◽  
Inventory Management ◽  
Information Flow ◽  
Service Industries ◽  
Competitive Intelligence ◽  
Social Media Analytics ◽  
Structured Interviews ◽  
Demand Planning ◽  
Competitor Analysis

This study examines the role of social media analytics (SMA) in providing competitive intelligence (CI). Building on CI theory, the data from qualitative semi-structured interviews with respondents belonging to social media, manufacturing, telecommunication, IT and service industries were analyzed using Nvivo coding and matrix queries. The results show that SMA provides an expanded CI beyond the previous limits of customers/markets and competitors, including insights on supply chains, costs and information-flow. Moreover, SMA-driven CI can provide visibility to supply chain uncertainties enabling improvements in demand planning and inventory management. SMA can provide CI about competitors’ strengths and weaknesses and customers’ dynamics; however, the bi-directional nature of CI could be determinantal if SM-linked customers are not educated/kept informed. Matrix query results illuminate the differences/similarities in respondents’ views. Academically, the study shows that SMA provides expanded CI to businesses beyond previously known scope of competitor analysis.

Gaining competitive intelligence from social media data

2015 ◽  
Vol 115 (9) ◽  
pp. 1622-1636 ◽  
Author(s):  
Wu He ◽  
Jiancheng Shen ◽  
Xin Tian ◽  
Yaohang Li ◽  
Vasudeva Akula ◽  
...  
Keyword(s):  
Social Media ◽  
Business Value ◽  
Competitive Intelligence ◽  
Social Media Analytics ◽  
Data Set ◽  
Social Media Data ◽  
Content Type ◽  
Study Results ◽  
Media Data

Purpose – Social media analytics uses data mining platforms, tools and analytics techniques to collect, monitor and analyze massive amounts of social media data to extract useful patterns, gain insight into market requirements and enhance business intelligence. The purpose of this paper is to propose a framework for social media competitive intelligence to enhance business value and market intelligence. Design/methodology/approach – The authors conducted a case study to collect and analyze a data set with nearly half million tweets related to two largest retail chains in the world: Walmart and Costco in the past three months during December 1, 2014-February 28, 2015. Findings – The results of the case study revealed the value of analyzing social media mentions and conducting sentiment analysis and comparison on individual product level. In addition to analyzing the social media data-at-rest, the proposed framework and the case study results also indicate that there is a strong need for creating a social media data application that can conduct real-time social media competitive intelligence for social media data-in-motion. Originality/value – So far there is little research to guide businesses for social media competitive intelligence. This paper proposes a novel framework for social media competitive intelligence to illustrate how organizations can leverage social media analytics to enhance business value through a case study.

scholarly journals Examining the Role of Social Media Analytics in Providing Competitive Intelligence

2021 ◽  
Vol 29 (6) ◽  
pp. 0-0
Keyword(s):  
Social Media ◽  
Inventory Management ◽  
Information Flow ◽  
Service Industries ◽  
Competitive Intelligence ◽  
Social Media Analytics ◽  
Structured Interviews ◽  
Demand Planning ◽  
Competitor Analysis

This study examines the role of social media analytics (SMA) in providing competitive intelligence (CI). Building on CI theory, the data from qualitative semi-structured interviews with respondents belonging to social media, manufacturing, telecommunication, IT and service industries were analyzed using Nvivo coding and matrix queries. The results show that SMA provides an expanded CI beyond the previous limits of customers/markets and competitors, including insights on supply chains, costs and information-flow. Moreover, SMA-driven CI can provide visibility to supply chain uncertainties enabling improvements in demand planning and inventory management. SMA can provide CI about competitors’ strengths and weaknesses and customers’ dynamics; however, the bi-directional nature of CI could be determinantal if SM-linked customers are not educated/kept informed. Matrix query results illuminate the differences/similarities in respondents’ views. Academically, the study shows that SMA provides expanded CI to businesses beyond previously known scope of competitor analysis.

scholarly journals Examining the role of social media analytics in providing competitive intelligence- The impacts and limitations

2021 ◽  
Vol 29 (6) ◽  
pp. 0-0
Keyword(s):  
Social Media ◽  
Inventory Management ◽  
Information Flow ◽  
Service Industries ◽  
Competitive Intelligence ◽  
Social Media Analytics ◽  
Structured Interviews ◽  
Demand Planning ◽  
Competitor Analysis

This study examines the role of social media analytics (SMA) in providing competitive intelligence (CI). Building on CI theory, the data from qualitative semi-structured interviews with respondents belonging to social media, manufacturing, telecommunication, IT and service industries were analyzed using Nvivo coding and matrix queries. The results show that SMA provides an expanded CI beyond the previous limits of customers/markets and competitors, including insights on supply chains, costs and information-flow. Moreover, SMA-driven CI can provide visibility to supply chain uncertainties enabling improvements in demand planning and inventory management. SMA can provide CI about competitors’ strengths and weaknesses and customers’ dynamics; however, the bi-directional nature of CI could be determinantal if SM-linked customers are not educated/kept informed. Matrix query results illuminate the differences/similarities in respondents’ views. Academically, the study shows that SMA provides expanded CI to businesses beyond previously known scope of competitor analysis.

Social Media Analytics in Education: What Is It, How Is It Useful, and What Does It Tell Us About How Schools Are Discussed in Social Media?

2014 ◽  
Vol 35 (1) ◽  
pp. 7-43 ◽  
Author(s):  
Dick M. Carpenter ◽  
Jenifer Walsh Robertson ◽  
Michele E. Johnson ◽  
Scott Blum
Keyword(s):  
Social Media ◽  
Social Media Analytics

How Social Media Analytics Can Save Businesses

2020 ◽  
Author(s):  
Jay Palmer ◽  
Kyle Revis ◽  
Yves Romain
Keyword(s):  
Social Media ◽  
Social Media Analytics

scholarly journals Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem

2021 ◽  
pp. 089443932110122
Author(s):  
Dennis Assenmacher ◽  
Derek Weber ◽  
Mike Preuss ◽  
André Calero Valdez ◽  
Alison Bradshaw ◽  
...  
Keyword(s):  
Social Media ◽  
Data Sharing ◽  
Algorithm Design ◽  
Computational Social Science ◽  
Evaluation Framework ◽  
Social Media Analytics ◽  
Data Sets ◽  
The Public ◽  
Research Areas ◽  
Media Data

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.

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