Improving Invoice Allocation in Accounting—An Account Recommender Case Study Applying Machine Learning

Esswein, Markus; Mayer, Jörg H.; Sedneva, Diana; Pagels, Daniel; Albers, Jean-Paul

doi:10.1007/978-3-030-47355-6_10

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…In everyday accounting activities, directly matching invoices to the associated purchase orders can be a challenge due to missing order numbers, differences in terms of the invoice amount, quantity and/or quality. ML can be used to build a recommender system for semi-automated matching (Esswein et al, 2020).…”

Section: Finance and Accountingmentioning

confidence: 99%

AI-enabled Enterprise Information Systems for Manufacturing

Zdravković

Panetto

Weichhart

2021

Enterprise Information Systems

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Research work presented in this paper considers the contemporary Enterprise Information Systems (EIS) functional architecture and carries out the literature review of AI applications integrated in Customer Relationship Management, Supply Chain Management, Inventory and logistics, Production Planning and Scheduling, Finance and accounting, Product Lifecycle Management and Human Resources, with special attention to the manufacturing enterprises. As a review outcome, the enhanced capabilities are identified in each of those processes and proposed as AI services. AI-enablement is related to the capabilities of the enterprise to implement improved decision making or automation by using (a) Machine Learning (ML) models or (b) logicbased systems. It is a process of the enterprise transformation leading to the convergence of the 4 major disruptive technologies, namely Industrial Internet of Things (or Cyber-Physical Systems), Agent-based Distributed Systems, Cloud Computing and Artificial Intelligence (non-symbolic AI like Machine Learning, Neural Networks; symbolic AI like Reasoning and Logic/Model-based AI).

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Section: Finance and Accountingmentioning

confidence: 99%

AI-enabled Enterprise Information Systems for Manufacturing

Zdravković

Panetto

Weichhart

2021

Enterprise Information Systems

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Consumers’ Behavioural Intentions to Reuse Recommender Systems: Assessing the Effects of Trust Propensity, Trusting Beliefs and Perceived Usefulness

2021

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Consumers’ Behavioural Intentions to Reuse Recommender Systems: Assessing the Effects of Trust Propensity, Trusting Beliefs and Perceived Usefulness

Acharya

Sassenberg

Soar

2022

JTAER

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Recommender systems (RSs) are widely utilised across industries as tools to provide users with recommendations based on their preferences. This paper reports on an examination of the influence of trusting beliefs on behavioural intentions to reuse RSs, emphasising the effects of trust propensity, perceived usefulness and product type. A distinctive contribution of this study is the research model, which integrated perceived usefulness as an antecedent of trusting beliefs. Data collected in Australia with 366 participants were used. A new approach (MICOM and PLS-MGA) was performed to assess the moderating effect of product type. The research results indicate that trust propensity has a positive and significant direct effect on consumers’ trusting beliefs in an ongoing relationship. The results also suggest that consumers’ trusting beliefs and perceived usefulness of RSs positively and significantly affect their intention to reuse RSs. Perceived usefulness of RSs is more important compared to trusting beliefs in predicting consumers’ behavioural intention to reuse RSs. Trusting beliefs partially mediate the impact of perceived usefulness on behavioural intentions to reuse RSs. The results also showed an insignificant difference between the effect of different product types on Australian consumers’ behavioural intention to reuse RSs. These results have implications for the design of RSs.

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Improving Invoice Allocation in Accounting—An Account Recommender Case Study Applying Machine Learning

Cited by 3 publications

References 26 publications

AI-enabled Enterprise Information Systems for Manufacturing

AI-enabled Enterprise Information Systems for Manufacturing

Consumers’ Behavioural Intentions to Reuse Recommender Systems: Assessing the Effects of Trust Propensity, Trusting Beliefs and Perceived Usefulness

Consumers’ Behavioural Intentions to Reuse Recommender Systems: Assessing the Effects of Trust Propensity, Trusting Beliefs and Perceived Usefulness

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