02/02/2024
By Mary Lou Kelly

The Department of Operations and Information Systems at the Manning School of Business invites you to attend a doctoral dissertation proposal by Esi Adeborna on “Gamification and Fairness in Enterprise Systems: Addressing User Experience Gap and Algorithmic Bias.”

Candidate Name: Esi Adeborna
Defense Date: Wednesday, Feb. 14, 2024
Time: 12:30 – 2 p.m. EST
Location: Virtual (Zoom)
Thesis/Dissertation Title: Gamification and Fairness in Enterprise Systems: Addressing User Experience Gap and Algorithmic Bias

Committee Members:

  • Luvai Motiwalla (Chair), Ph.D., Department of Operations & Information Systems, UMass Lowell
  • Berk M Talay, Ph.D., Department of Marketing Entrepreneurship and Innovation, UMass Lowell
  • Xiaobai (Bob) Li, Ph.D., Department of Operations & Information Systems, UMass Lowell
  • Jennifer Xu, Ph.D., Department of Computer Information Systems, Bentley University

Abstract:
In today's fast-paced and data-driven business environment, Enterprise Systems (ES) like Enterprise Resource Planning (ERP) or Customer Relationship Management (CRM) systems are the backbone of organizations. These multifaceted platforms enable seamless integration of various business processes, from finance and human resources to supply chain management and customer relations. Two critical challenges often lurk beneath the surface of these highly complex enterprise systems: 1) Lack of user experiential outcomes and 2) Algorithmic Bias in ES. Our research offers two approaches (chapter 1 and chapter 2) to addressing the issues of experiential outcome and algorithmic bias in ES. We employ a strategy that aims to improve both the instrumental and experiential aspects of ERP by introducing gamification in Chapter 1. Chapter 2 provides empirical ways to uncover algorithmic bias to introduce fairness in CRM recommender systems.

The User Experiential Gap (Chapter 1)
"It's just not engaging enough." This is a sentiment often echoed by employees when dealing with ERP systems (Gartner Research 2018). ERP interfaces tend to prioritize functionality over user-friendliness, resulting in improved instrumental outcome while neglecting experiential aspects. Recently, the integration of gamification into ES has gained increasing attention, particularly for its effectiveness in user training and education. Yet, the application of gamification in Enterprise Resource Planning (ERP) systems is still in its infancy, with significant challenges in its practical and systematic design and implementation. Gamification is defined as “the use of game design elements in non-game contexts” (Deterding et. al. 2011). This study introduces a gamified web application (GWA) for ERPs, developed using a design science framework (Peffers et al., 2007). Our objective is to improve both the instrumental and experiential aspects of ERP task completion, ensuring an engaging user experience (Liu et. al, 2017). We base our approach on existing literature to develop gamification design elements specifically for an enterprise system, demonstrated through GWA integrated with SAP system. Our study involves a mixed-method evaluation of GWA's effectiveness, combining quantitative data from a survey, GWA usage analytics, and qualitative feedback from expert interviews. This study seeks to contribute to the field of gamification in the ERP domain by providing practical insight to seamlessly incorporate experiential outcomes into ERP systems design. We also provide a set of gamification design elements that effectively enhance both the practical and experiential aspects of performing ERP tasks and finally our mixed-method evaluation approach contributes to bridging methodological gaps in the field of ERP gamification research.

The Algorithmic Bias Dilemma (Chapter 2)
With the increase in the use of algorithms in decision making in different spheres of life including ES, the need for “fairness” in these machine learning (ML) algorithms has become a progressively important concern. However, any remedy to bias must start with awareness that bias exists as it helps guide toward a solution. This work is an empirical study that aims to uncover data and subsequent algorithmic bias in one of such ES ML applications, CRM. Specifically, we consider recommender systems (RS) in CRMs, one of the most successful applications of ML technology in practice today, and answer three main questions: (1) Does bias exist in RS input data? (2) Does the use of RS promote diversity of recommendation or reinforce existing data bias? and (3) Does the application of RS over the years improve the recommendation diversity and reduce bias? We use cell phones and accessories rating dataset from Amazon CRM for our empirical study. We employ the Long Tail phenomenon and quantify the shape of purchase distribution by calculating Gini coefficient from the Lorenz curve to determine the presence of bias. In the end, our findings from our experiments showed the presence of bias in both data and the RS algorithm, even year­ over­ year. This study seeks to uncover ways in which bias is introduced in CRM recommendation by detecting underlying data and subsequent algorithmic bias. We calculate the influence of the RS algorithm on the input dataset and compare the effect of RS revenue distribution using the Gini coefficient derived from the category’s Lorenz curve. We also ascertain the longitudinal effect of RS on the Long Tail by comparing the year over year Gini Coefficient to determine if bias diminished over the years or vice versa.

All interested students and faculty members are invited to attend.