IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v54y2020ics0268401219317694.html
   My bibliography  Save this article

Frontline employee empowerment: Scale development and validation using Confirmatory Composite Analysis

Author

Listed:
  • Motamarri, Saradhi
  • Akter, Shahriar
  • Yanamandram, Venkata

Abstract

Empowerment has been argued as a viable strategy to enable frontline employees (FLEs) to manage the complexities of service encounters. Organisations must cascade insights from analytics to frontlines for dynamic (re)bundling of service elements while serving customers. However, very little is known on how FLEs are empowered in analytics-driven services. This study addresses these research gaps, drawing on a systematic literature review and in-depth interviews (n = 30), followed by conceptualisation and validation of an empowerment scale through a pilot (n = 50) and the main study (n = 304). This research confirms empowerment as a second-order construct consisting of six dimensions namely, decision making, discretionary skills, information access, knowledge, tools and training. The predictive power of the scale is validated through PLSc and PLSpredict (k = 10) using a training sample (n = 274) and a holdout sample (n = 30). Theoretically, this work extends FLE empowerment to analytics-driven services. Practically, the study informs managers to complement their investments in technology with an internal orientation program to empower FLEs to effectively link with customers and seize opportunities.

Suggested Citation

  • Motamarri, Saradhi & Akter, Shahriar & Yanamandram, Venkata, 2020. "Frontline employee empowerment: Scale development and validation using Confirmatory Composite Analysis," International Journal of Information Management, Elsevier, vol. 54(C).
  • Handle: RePEc:eee:ininma:v:54:y:2020:i:c:s0268401219317694
    DOI: 10.1016/j.ijinfomgt.2020.102177
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0268401219317694
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijinfomgt.2020.102177?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Akter, Shahriar & Dwivedi, Yogesh K. & Sajib, Shahriar & Biswas, Kumar & Bandara, Ruwan J. & Michael, Katina, 2022. "Algorithmic bias in machine learning-based marketing models," Journal of Business Research, Elsevier, vol. 144(C), pages 201-216.
    2. Hossain, Md Afnan & Akter, Shahriar & Yanamandram, Venkata & Strong, Carolyn, 2024. "Navigating the platform economy: Crafting a customer analytics capability instrument," Journal of Business Research, Elsevier, vol. 170(C).
    3. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris, 2022. "Examining the role of cross-cultural factors in the international market on customer engagement and purchase intention," Journal of International Management, Elsevier, vol. 28(3).
    4. Rameshwar Dubey & David J. Bryde & Cyril Foropon & Gary Graham & Mihalis Giannakis & Deepa Bhatt Mishra, 2022. "Agility in humanitarian supply chain: an organizational information processing perspective and relational view," Annals of Operations Research, Springer, vol. 319(1), pages 559-579, December.
    5. Motamarri, Saradhi & Akter, Shahriar & Hossain, Md Afnan & Dwivedi, Yogesh K, 2022. "How does remote analytics empowerment capability payoff in the emerging industrial revolution?," Journal of Business Research, Elsevier, vol. 144(C), pages 1163-1174.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ininma:v:54:y:2020:i:c:s0268401219317694. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-information-management .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.