Artificial intelligence and SMEs: How can B2B SMEs leverage AI platforms to integrate AI technologies?

Wei, Ruiqi; Pardo, Catherine

doi:10.1016/j.indmarman.2022.10.008

Cited by 50 publications

(17 citation statements)

References 44 publications

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“…When creating a Web-based cloud ERP platform or a customized software solution with a self-learning system, there are significant criteria to meet. As a result, the ERP industry is seeing an increase in AI-enabled software solutions (Goundar et al , 2021; Wei and Pardo, 2022; Wei et al , 2022). The ERP vendors have also begun investigating the potential of using AI to choose ERP customization options during ERP implementation, going beyond just the integration of AI into ERP software solutions.…”

Section: Artificial Intelligence In Enterprise Resource Planning Soft...mentioning

confidence: 99%

Understanding algorithm bias in artificial intelligence-enabled ERP software customization

Parthasarathy¹,

Padmapriya²

2023

JEET

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Purpose Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered. Design/methodology/approach As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm. Findings This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice. Originality/value To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).

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Section: Artificial Intelligence In Enterprise Resource Planning Soft...mentioning

confidence: 99%

Understanding algorithm bias in artificial intelligence-enabled ERP software customization

Parthasarathy¹,

Padmapriya²

2023

JEET

View full text Add to dashboard Cite

show abstract

“…Ethics in data science is a topic that has received a lot of attention recently. 1,3,10,13,23,[27][28][29] It is uncertain if data science companies have the breadth and depth of expertise to easily deliver this training, but they have been advised to provide guidance and support, as well as interactive ethical exams to help staff examine ethical challenges. 5 If data science ethics goes unaddressed, it may introduce new business risks.…”

Section: Review Of Literaturementioning

confidence: 99%

“…In their research on the ethical issues affecting data science projects, many researchers 1,[3][4][5]34,36,37 ; concurred that the elements such as data privacy, data ownership, defining target variables used in a data model, fair evaluation of built-in data model, and foreseeing the potential consequences of the deployed data model are all set to invite ethical concerns or issues in a data science project. However, no practical answer or paradigm addressing these issues has emerged from the aforementioned studies.…”

Section: Review Of Literaturementioning

confidence: 99%

“…The widespread use of data science in recent years has had unfavorable repercussions, including an increase in privacy invasion, data-driven prejudice, and data-driven decision-making without rationale. [1][2][3] The primary focus of data ethics is on what is right and wrong. Numerous data science components related to data privacy are addressed by the General Data Protection Regulation (GDPR) in Europe, including explainability.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the majority of data science projects are made to handle big data and include steps like data cleansing, data model construction, model evaluation, and deployment. The widespread use of data science in recent years has had unfavorable repercussions, including an increase in privacy invasion, data‐driven prejudice, and data‐driven decision‐making without rationale 1‐3 …”

Section: Introductionmentioning

confidence: 99%

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A framework for managing ethics in data science projects

Parthasarathy¹,

Panigrahi

Subramanian

2023

Engineering Reports

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The field of data ethics is concerned with the ethical considerations surrounding data, algorithms, and associated practices, with the aim of identifying ethical solutions. The application of ethical principles to the handling of data, algorithms, and practices can facilitate the identification and delineation of ethical quandaries within the domain of data science. The present study focuses on the topic of data ethics, specifically pertaining to the processes of data collection, data model construction, evaluation, and deployment. This study introduces a comprehensive framework designed to facilitate the management of ethical considerations in data science projects. In order to authenticate the framework, a case study was conducted and our perspectives on its practical implementation were presented. The description of the scope of future research is also provided.

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Maximizing CSR impact: Leveraging artificial intelligence and process optimization for sustainability performance management

Khan,

Mehmood,

Ali

2024

Corp Soc Responsibility Env

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This study, which is anchored to Resource‐Based View (RBV) theory, explores the relationship between artificial intelligence (AI), process optimization, organizational flexibility, and sustainability performance in organizational settings. Leveraging the RBV framework's focus on internal resources as sources of competitive advantage, this research seeks to clarify how AI adoption, process optimization, and organizational flexibility foster sustainable growth. SPSS PROCESS Macro was used to analyze the data from 288 organizations. The findings derived from the empirical data, this study verify the positive relationships between AI and process optimization as well as AI and sustainability performance, revealing the strategic nature of AI as a resource to improve operational effectiveness and environmental responsibility. Further, our results indicate the mediating role of process optimization and the moderating effect of organizational flexibility in determining the link between AI and sustainability outcomes. Moreover, results confirmed the indirect effects of AI on sustainability performance via process optimization under the boundary conditions of the organizational flexibility. These findings add to the developing literature of sustainable operational practices and provide practical suggestions that might be useful for the practitioners aiming at using AI driven by organizational capabilities to deliver sustainability performance.

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Artificial intelligence and SMEs: How can B2B SMEs leverage AI platforms to integrate AI technologies?

Cited by 50 publications

References 44 publications

Understanding algorithm bias in artificial intelligence-enabled ERP software customization

Understanding algorithm bias in artificial intelligence-enabled ERP software customization

A framework for managing ethics in data science projects

Maximizing CSR impact: Leveraging artificial intelligence and process optimization for sustainability performance management

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