Assessing stakeholder network engagement

Article


Okazaki, S., Plangger, K., Roulet, T. and Menendez Benito, H. 2021. Assessing stakeholder network engagement. European Journal of Marketing. 55 (5), pp. 1359-1384. https://doi.org/10.1108/EJM-12-2018-0842
TypeArticle
TitleAssessing stakeholder network engagement
AuthorsOkazaki, S., Plangger, K., Roulet, T. and Menendez Benito, H.
Abstract

Purpose: With the popularity of social media platforms, firms have now tangible means not only to reach out to their stakeholders, but also to closely monitor those interactions. Yet, there are limited methodological advances on how to measure a firm’s stakeholder networks, and the level of engagement firms have with these networks. Drawn upon the customer engagement and stakeholder theory literature, this study proposes an approach to calculate a firm’s Stakeholder Network Engagement (SNE) index.
Design: After deriving the SNE index formula mathematically, we illustrate how the SNE index functions using eight firms’ online Corporate Social Responsibility (CSR) networks across four diverse industries.
Findings: We propose and illustrate a new approach of capturing the SNE in a stakeholder network for use by academic and practical researchers.
Research limitations/implications: Researchers can use the SNE index to assess engagement in stakeholder networks in various contexts.
Practical implications: Managers can use the SNE index to assess, benchmark and improve the nature and quality of their CSR strategies to derive greater return on their CSR investments.
Originality: Building on the stakeholder, communication and network analysis literatures, we conceptualise SNE in four theoretical dimensions: diffusion, accessibility, interactivity, and influence. Then, we mathematically derive and empirically illustrate an index that measures SNE.

KeywordsStakeholder relationships, Twitter, Corporate social responsibility, Social media,Metric development, Stakeholder multiplicity theory, Stakeholder network engagement
PublisherEmerald Publishing Limited
JournalEuropean Journal of Marketing
ISSN0309-0566
Publication dates
Online30 Dec 2020
Print11 May 2021
Publication process dates
Submitted09 Dec 2018
Accepted24 Oct 2020
Deposited13 Apr 2021
Output statusPublished
Accepted author manuscript
File Access Level
Open
Copyright Statement

© 2020, Emerald Publishing Limited. This AAM is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher

Digital Object Identifier (DOI)https://doi.org/10.1108/EJM-12-2018-0842
LanguageEnglish
Permalink -

https://repository.mdx.ac.uk/item/894z4

Download files


Accepted author manuscript
  • 124
    total views
  • 140
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Hashing fuzzing: introducing input diversity to improve crash detection
Menendez Benito, H. and Clark, D. 2022. Hashing fuzzing: introducing input diversity to improve crash detection. IEEE Transactions on Software Engineering. 48 (9), pp. 3540-3553. https://doi.org/10.1109/TSE.2021.3100858
Output sampling for output diversity in automatic unit test generation
Menéndez, H., Boreale, M., Gorla, D. and Clark, D. 2022. Output sampling for output diversity in automatic unit test generation. IEEE Transactions on Software Engineering. 48 (1), pp. 295-308. https://doi.org/10.1109/TSE.2020.2987377
Clustering: finding patterns in the darkness
Menendez Benito, H. 2021. Clustering: finding patterns in the darkness. Open Journal of Machine Learning. 1 (1), pp. 1-28. https://doi.org/10.46723/ojml.v1i1.4
Malware: the never-ending arm race
Menendez Benito, H. 2021. Malware: the never-ending arm race. Open Journal of Cybersecurity. 1 (1), pp. 1-25. https://doi.org/10.46723/ojc.1.1.3
Software testing or the bugs’ nightmare
Menendez Benito, H. 2021. Software testing or the bugs’ nightmare. Open Journal of Software Engineering. 1 (1), pp. 1-21. https://doi.org/10.46723/ojse.1.1.1
Getting ahead of the arms race: hothousing the coevolution of VirusTotal with a Packer
Menendez Benito, H., Clark, D. and T. Barr, E. 2021. Getting ahead of the arms race: hothousing the coevolution of VirusTotal with a Packer. Entropy. 23 (4). https://doi.org/10.3390/e23040395
Diversifying focused testing for unit testing
Menendez Benito, H., Jahangirova, G., Sarro, F., Tonella, P. and Clark, D. 2021. Diversifying focused testing for unit testing. ACM Transactions on Software Engineering and Methodology. 30 (4), pp. 1-24. https://doi.org/10.1145/3447265
Designing large quantum key distribution networks via medoid-based algorithms
Garcia-Cobo, I. and Menendez Benito, H. 2021. Designing large quantum key distribution networks via medoid-based algorithms. Future Generation Computer Systems. 115, pp. 814-824. https://doi.org/10.1016/j.future.2020.09.037