An Empirical Investigation on Business Analytics in Software and Systems Development Projects

2023 | Information Systems Frontiers | Citations: 0

Authors: Ahmad, Muhammad Ovais; Ahmad, Iftikhar; Rana, Nripendra P.; Khan, Iqra Sadaf

Abstract: To create competitive advantages, companies are leaning towards business analyti ... Expand

Abstract: To create competitive advantages, companies are leaning towards business analytics (BA) to make data-driven decisions. Nevertheless, users acceptance and effective usage of BA is a key element for its success. Around the globe, organizations are increasingly adopting BA, however, a paucity of research on examining the drivers of BA adoption and its continuance is noticeable in the literature. This is evident in developing countries where a higher number of systems and software development projects are outsourced. This is the first study to examine BA continuance in the context of software and systems development projects from the perspective of Pakistani software professionals. The data was collected from 186 Pakistani software professionals working in software and systems development projects. The data were analyzed using partial least squares - structural equation modelling techniques. Our structural model explains 45% variance on BA continuance intention, 69% variance on technological compatibility, and 59% variance on perceived usefulness. Our results show that confirmation has a direct impact on BA continuance intention in software and systems projects. The study has both theoretical and practical implications for professionals in the field of business analytics. Collapse

Topics: perceived usefulness IT development project IT project systems development IT workforce

Methods: survey partial least squares path modeling machine learning conceptual modelling data transformation

Theories: expectation confirmation theory

TEA-IS: A hybrid DEA-TOPSIS approach for assessing performance and synergy in Chinese health care

2023 | Decision Support Systems | Citations: 0

Authors: Antunes, Jorge; Hadi-Vencheh, Abdollah; Jamshidi, Ali; Tan, Yong; Wanke, Peter

Abstract: This paper presents an assessment of the Chinese healthcare system in 31 provinc ... Expand

Abstract: This paper presents an assessment of the Chinese healthcare system in 31 provinces for a 10-year period in light of relevant physical and human resource variables. First, a novel TEA-IS (Trigonometric Envelopment Analysis for Ideal Solutions) model is developed to assess healthcare efficiency at the province level. Machine learning methods are also employed to predict high-low performance and the synergistic Chinese healthcare province in terms of contextual variables. The results indicate that synergy has played a pivotal role in the Chinese healthcare systems, not only by triggering higher performance levels due to the progressive adoption of best practices over the course of time, but also by being closely related to different socioeconomic and demographic variables, such as the illiteracy rate. It is possible to claim that healthcare performance has remained stable in China over the past two decades, performance and synergy at the province level are still heterogeneous. Collapse

Topics: health information system IT productivity human resource management service quality productivity

Methods: TOPSIS trade-off analysis method machine learning artificial neural network literature study

Measuring the Actual Office Workspace Utilization in a Desk Sharing Environment Based on IoT Sensors

2023 | DESRIST | Citations: 0

Authors: Arz von Straussenburg, Arnold F.; Blazevic, Mevludin; Riehle, Dennis M.

Abstract: Businesses that follow the trend of flexible work arrangements with home offices ... Expand

Abstract: Businesses that follow the trend of flexible work arrangements with home offices and hybrid work models are increasingly utilizing desk sharing to allocate available desks in the office efficiently. In this work, we investigate the utilization of flexible work arrangements, specifically the implementation of desk sharing in hybrid work models. To optimize the occupancy rate of shared desks and simultaneously meet demand and save costs, it is crucial to have real-time knowledge of their availability which can be obtained by recording occupancy over time. We utilize the Design Science Research (DSR) methodology to develop a prototype solution for real-time monitoring of shared desks occupancy in hybrid work models. The proposed prototype is a low-cost solution utilizing Internet of Things (IoT) technology consisting of a motion sensor and accelerometer that collect occupancy data and transmit it via Long Range Wide Area Network (LoRaWAN). Signal processing utilizing cloud computing is employed to enhance the quality and accuracy of the data, which is subsequently made available through a web dashboard. The prototype’s effectiveness is evaluated through a small-scale pilot deployment in a real-world environment over a two-week period, with reference values obtained during periods of non-use serving as a benchmark for the evaluation. Collapse

Topics: mobile system internet of things digital dashboard application programming interface smart wearable device

Methods: design artifact wearables based research design science experiment descriptive statistic

Counterfactual Explanations for Incorrect Predictions Made by AI Models

2023 | International Conference on Information Systems | Citations: 0

Authors: Asrzad, Amir; Li, Xiaobai

Abstract: Advanced AI models are powerful in making accurate predictions for complex prob ... Expand

Abstract: Advanced AI models are powerful in making accurate predictions for complex problems. However, these models often operate as black boxes. This lack of interpretability poses significant challenges, especially in high-stakes applications such as finance, healthcare, and criminal justice. Explainable AI seeks to address the challenges by developing methods that can provide meaningful explanations for humans to understand. When black box models are used for prediction, they inevitably produce errors. It is important to appropriately explain incorrect predictions. This problem, however, has not been addressed in the literature. In this study, we propose a novel method to provide explanations for misclassified cases made by black box models. The proposed method takes a counterfactual explanation approach. It builds a decision tree to find the best counterfactual examples for explanations. Incorrect predictions are rectified using a trust score measure. We validate the proposed method in an evaluation study using real-world data. Collapse

Topics: artificial intelligence explainable artificial intelligence local interpretable model agnostic explanation decision making enterprise information system

Methods: machine learning decision tree classification literature study random forest classification experiment

Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning

2023 | Information Systems Frontiers | Citations: 0

Authors: Azri, Abderrazek; Favre, Cécile; Harbi, Nouria; Darmont, Jérôme; Noûs, Camille

Abstract: The proliferation of rumors on social media has become a major concern due to it ... Expand

Abstract: The proliferation of rumors on social media has become a major concern due to its ability to create a devastating impact. Manually assessing the veracity of social media messages is a very time-consuming task that can be much helped by machine learning. Most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. Moreover, prior works have used many classical machine learning models to detect rumors. However, although recent studies have proven the effectiveness of ensemble machine learning approaches, such models have seldom been applied. Thus, in this paper, we propose a set of advanced image features that are inspired from the field of image quality assessment, and introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features by exploring various machine learning models. Moreover, we demonstrate the effectiveness of ensemble learning algorithms for rumor detection by using five metalearning models. Eventually, we conduct extensive experiments on two real-world datasets. Results show that MONITOR outperforms state-of-the-art machine learning baselines and that ensemble models significantly increase MONITOR’s performance. Collapse

Topics: Twitter social network Python microblog logistics management

Methods: computational algorithm machine learning experiment meta learning ensemble learning

Single-Sourcing vs. Multisourcing: An Empirical Analysis of Large Information Technology Outsourcing Arrangements

2023 | Information Systems Research | Citations: 0

Authors: Bapna, Ravi; Gupta, Alok; Ray, Gautam; Singh, Shweta

Abstract: As the information technology (IT) services landscape matures, clients are incre ... Expand

Abstract: As the information technology (IT) services landscape matures, clients are increasingly adopting multisourcing arrangements that involve multiple vendors. Although a large body of information systems (IS) literature addresses issues of whether to outsource (to a single vendor), what types of contracts to use, and how to achieve optimal relational governance, little is known about the antecedents and consequents of the single versus multisourcing decision. Moreover, while conceptual and analytical models of single-sourcing versus multisourcing have been developed, there is no empirical IS research using a large-scale data set with rigorous econometric analysis that examines the antecedents and consequents of multisourcing in the IT context. This paper fills this void, using the transaction cost economic lens and a data set of 49,057 large IT outsourcing arrangements that spans multiple industries and dates back 25 years. We find that there is a curvilinear relationship between number of IT services in an IT outsourcing arrangement and the likelihood of multisourcing. This relationship increases as the number of IT services increases to up to five services and then decreases. For managers who plan to multisource IT outsourcing arrangements, this research provides guidance to minimize exchange hazards through a better understanding of the relationship between sourcing choice, client IT outsourcing capabilities, the competitiveness of the vendor landscape, and the number of IT services in an IT outsourcing arrangement. We provide empirical evidence that the choice between single-sourcing and multisourcing is material to the performance of outsourcing contracts as an incorrect sourcing choice is likely to result in negative contract outcomes. History: Paul A. Pavlou, Senior Editor; Ning Su, Associate Editor. Collapse

Topics: outsourcing system integration IS management service desk data center

Methods: XGBoost robustness check machine learning k-nearest neighbor statistical hypothesis test

Theories: transaction cost economics game theory

Too Good to Be True? An Empirical Study of ChatGPT Capabilities for Academic Writing and Implications for Academic Misconduct

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Busch, Peter André; Hausvik, Geir Inge

Abstract: This study evaluated the capabilities of ChatGPT, a conversational artificial i ... Expand

Abstract: This study evaluated the capabilities of ChatGPT, a conversational artificial intelligence (AI) agent, to generate quality answers to exam questions that are indistinguishable from human-generated text, normally requiring high-level cognitive abilities. This potential capacity is a major threat to academic integrity since ChatGPT may be used for academic misconduct. To determine its capabilities, we asked 15 faculty members to assess a total of ten answers (two each) to an actual IS exam question, of which students wrote five, and ChatGPT generated five. The study found that ChatGPT can generate answers of generally good quality that may pass as human-written text by examiners. The contributions of the study lay in providing empirical results in a novel research area, providing recommendations for teachers, and suggesting ways forward for research. Collapse

Topics: artificial intelligence smartphone evaluation criteria smartphone addiction mobile system

Methods: chatGPT machine learning descriptive statistic correlation analysis personal interview

Divergent Innovation: Directing the Wisdom of Crowd to Tackle Societal Challenges

2023 | International Conference on Information Systems | Citations: 0

Authors: Cao, Chuhan; Zhu, Jiantao; Xiong, Bingqing; Lim, Eric; Liu, Hefu; Cai, Zhao; Tan, Chee-Wee

Abstract: Crowdsourcing is acknowledged as a promising avenue for addressing societal cha ... Expand

Abstract: Crowdsourcing is acknowledged as a promising avenue for addressing societal challenges by drawing on the wisdom of the crowd to offer diverse solutions to complex problems. Advancing a new conceptual framework of 'divergent innovation' which delineates between topic and quality divergence as focal metrics of performance when crowdsourcing for solutions to societal challenges, this study investigates the impacts of four ideation stimuli on divergent innovation. These four stimuli include task description concreteness, resource richness, topic entropy, and judging criteria comprehensiveness. Empirical analysis based on data sourced from an online crowd-ideation platform reveals that task description concreteness negatively affects topic divergence but positively influences quality divergence, whereas resource richness positively affects topic divergence but negatively influences quality divergence. Additionally, the relationship between topic entropy and topic divergence is U-shaped, with no significant impact on quality divergence. These findings contribute to extant literature on crowdsourcing and offer invaluable insights for practitioners. Collapse

Topics: innovation management societal challenge crowdsourcing evaluation criteria media richness

Methods: BERT literature study statistical hypothesis test regression analysis method language model

Deploying Artificial Intelligence to Combat Covid-19 Misinformation on Social Media: Technological and Ethical Considerations

2023 | HICSS | Citations: 0

Authors: Cartwright, B.; Frank, R.; Weir, G.; Padda, K.; Strange, S.

Abstract: This paper reports on AI research into online misinformation pertaining to the ... Expand

Abstract: This paper reports on AI research into online misinformation pertaining to the COVID-19 pandemic within the Canadian context. This is part of our longerterm goal, i.e., development of a machine-learning tool to assist social media platforms, online service providers and government agencies in identifying and responding to misinformation on social media. We report on predictive accuracies accomplished by applying a combination of technologies, including a custom-designed web-crawler, The Dark Crawler, the Posit toolkit, and four different machine-learning models based on Naïve Bayes, Support Vector Machines, LibLinear and LibShortText. Overall, we found that Posit and LibShortText models showed higher levels of correlation to the pre-determined (manual and machine-driven) data classifications than the other machine-learning algorithms tested. We further argue that the harms associated with COVID-19 misinformatione.g., the social and economic damage, and the deaths and severe illnessesoutweigh the right to personal privacy and freedom of speech considerations. Collapse

Topics: misinformation Twitter Reddit social media pandemic

Methods: machine learning computational algorithm support vector machine decision tree classification random forest classification

The Core Industry Manufacturing Process of Electronics Assembly Based on Smart Manufacturing

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chen, Rongli; Chen, Xiaozhong; Wang, Lei; Li, Jianxin

Abstract: This research takes a case study approach to show the development of a diverse a ... Expand

Abstract: This research takes a case study approach to show the development of a diverse adoption and product strategy distinct from the core manufacturing industry process. It explains the development status in all aspects of smart manufacturing, via the example of ceramic circuit board manufacturing and electronic assembly, and outlines future smart manufacturing plans and processes. The research proposed two experiments using artificial intelligence and deep learning to demonstrate the problems and solutions regarding methods in manufacturing and factory facilities, respectively. In the first experiment, a Bayesian network inference is used to find the cause of the problem of metal residues between electronic circuits through key process and quality correlations. In the second experiment, a convolutional neural network is used to identify false defects that were overinspected during automatic optical inspection. This improves the manufacturing process by enhancing the yield rate and reducing cost. The contributions of the study built in circuit board production. Smart manufacturing, with the application of a Bayesian network to an Internet of Things setup, has addressed the problem of residue and redundant conductors on the edge of the ceramic circuit board pattern, and has improved and prevented leakage and high-frequency interference. The convolutional neural network and deep learning were used to improve the accuracy of the automatic optical inspection system, reduce the current manual review ratio, save labor costs, and provide defect classification as a reference for preprocess improvement. Collapse

Topics: artificial intelligence smart manufacturing production management Python internet of things

Methods: experiment deep learning case study convolutional neural network Bayesian network