Blockchain technology perception regarding supporting the digital transformation of Supply Chain Management

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Kuciapski, Michał; Nazet, Franz

Abstract: The globalization of the world economy increases the role of value chains, wher ... Expand

Abstract: The globalization of the world economy increases the role of value chains, where disruptions in the realization of the logistics processes occur repeatedly. Climate changes, pandemics, and military conflicts further increase the difficulties in matching supply and demand in a vast majority of networks. As a result, the digital transformation of Supply Chain Management (SCM) often referred to as Digital Supply Chain (DSC) is considered an important solution for several SCM concerns. Extensive subject matter literature research pointed out that several technologies have been studied and implemented as DSC solutions contrary to blockchain technology. Therefore, the research aims of this article is to examine the challenges of DSC with particular emphasis on blockchain technology perception in this context. Research has been conducted via structured in-depth interviews with SCM experts. Study results highlighted that blockchain is perceived by SCM experts as a technology with significant capability to support the creation of DSC, but unfortunately used at all. They also pointed out that blockchain should not be the technology of the first choice when initiating the digitization of SCM processes, but potentially beneficial for mature DSC. Collapse

Topics: blockchain digital transformation supply chain management internet of things mobile system

Methods: survey qualitative interview personal interview survey design theoretical contribution

Tackling the Accuracy-Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal

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

Authors: Kucklick, Jan-Peter; Müller, Oliver

Abstract: Deep learning models fuel many modern decision support systems, because they typ ... Expand

Abstract: Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows extending the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this article, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning, which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density. Collapse

Topics: Bing explainable artificial intelligence shapley additive explanation video conference price management

Methods: principal component analysis data transformation experiment artificial neural network regression analysis method

Theories: game theory

Trust transfer effects and associated risks in telemedicine adoption

2023 | Electronic Markets | Citations: 0

Authors: Kuen, Leonie; Schürmann, Fiona; Westmattelmann, Daniel; Hartwig, Sophie; Tzafrir, Shay; Schewe, Gerhard

Abstract: Telemedicine has the potential to address considerable challenges in the efficie ... Expand

Abstract: Telemedicine has the potential to address considerable challenges in the efficient provision of health care services. However, this will not be realized until a high acceptance rate among patients is achieved. We address the research gap that arises from the need to explore the interplay of different trust referents (physician, technology, treatment) and perceived risk dimensions (performance, privacy, time, psychological) in patients’ telemedicine adoption considering two different symptom types (physical vs. mental). We conducted a scenario-based online survey and performed t-tests, scenario-specific structural equation modeling, and multi-group analysis. T-tests and multi-group analysis do not indicate differences in perceptions and path coefficients between the symptoms. Furthermore, scenario-specific structural equation modeling reveals that for both scenarios, trust in physician is less important for trust transfer effects and intention to use than trust in technology and trust in treatment. Trust in treatment has similar effects for all risk dimensions, while only performance risk relates to use intention. Moreover, the results indicate a considerable intention-behavior gap. We advance IS research by emphasizing the relevance of considering multiple trust referents, trust transfer effects, and a multidimensional perspective on perceived risk. Collapse

Topics: electronic health usage intention privacy technology adoption health information system

Methods: survey structural equation modeling partial least squares path modeling Student's t-test factor analysis

Theories: unified theory of acceptance and use of technology

Toward Government as a Platform: An Analysis Method for Public Sector Infrastructure

2023 | International Conference on Business Informatics | Citations: 0

Authors: Kuhn, Peter; Zavolokina, Liudmila; Balta, Dian; Matthes, Florian

Abstract: ... Expand

Abstract: Collapse

Topics: computer supported cooperative work government system participatory design

Methods: design science qualitative interview design methodology case study literature study

What triggers digital employee advocacy behavior?

2023 | International Conference on Information Systems | Citations: 0

Authors: Kumar, Anaya; Shirish, Anuragini

Abstract: As companies increasingly move towards remote/hybrid workplace settings for bot ... Expand

Abstract: As companies increasingly move towards remote/hybrid workplace settings for both sustenance and growth motives, they face people-centric problems such as quiet quitting and organizational (dis)identification. As a way to mitigate these situations, empower employees, and improve well-being, they heavily invest in people-centric digital workplace platforms such as the employee advocacy (EA) platforms that promise both instrumental and humanistic outcomes, including encouraging digital employee advocacy behavior (DEA). However, no prior empirical research has conceptualized DEA nor have they shown the efficacy of the EA platform in encouraging DEA. Using a bricolage approach, we explore digital trace data (user reviews) to firstly conceptualize and then validate a grounded well-being led digital employee advocacy model. We contribute to EA and digital workplace literature by conceptualizing and providing measures to study DEA as well as identifying platform enabled social and psychological well-being as the key triggers to DEA. Practical implications are also discussed. Collapse

Topics: IT workforce social media user experience user behavior online review

Methods: qualitative content analysis regression analysis method qualitative coding digital trace data multiple regression

Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster

2023 | Information Systems Frontiers | Citations: 0

Authors: Kumar, Abhinav; Singh, Jyoti Prakash; Rana, Nripendra P.; Dwivedi, Yogesh K.

Abstract: During a disaster, a large number of disaster-related social media posts are wid ... Expand

Abstract: During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by the eyewitness is preferred over the other source of information as it is more effective at helping organize rescue and relief operations and potentially saving lives. In this work, we propose a multi-channel convolutional neural network (MCNN) that uses three different word-embedding vectors together to classify disaster-related tweets into eyewitness, non-eyewitness, and don’t know classes. We compared the performance of the proposed multi-channel convolutional neural network with several attention-based deep-learning models and conventional machine learning-models such as recurrent neural network, gated recurrent unit, Long-Short-Term-Memory, convolutional neural network, logistic regression, support vector machine, and gradient boosting. The proposed multi-channel convolutional neural network achieved an F1-score of 0.84, 0.88, 0.84, and 0.86 with four disaster-related datasets of floods, earthquakes, hurricanes, and wildfires, respectively. The experimental results show that the training MCNN model with different word embedding together performs better than the conventional machine-learning models and several other deep-learning models. Collapse

Topics: Twitter earthquake hurricane crowdsourcing social media

Methods: machine learning deep learning convolutional neural network experiment GloVe

M&A Negotiations: Role of Negotiation Process, Ownership and Advisors on Deal Completion

2023 | Group Decision and Negotiation | Citations: 0

Authors: Kumar, Deepak; Sengupta, Keya; Bhattacharya, Mousumi

Abstract: Mergers and acquisition deals are uncertain, with completion likelihood changing ... Expand

Abstract: Mergers and acquisition deals are uncertain, with completion likelihood changing throughout the negotiation process. During negotiations, the firms process new information to modify their choices. This study aims to examine the dynamic nature of the M&A negotiation process. The study uses a sample of 983 worldwide unsolicited deals from 1999 to 2019 and classifies M&A deal outcomes using logistic regression, decision tree, and random forest classifiers. Ownership considerations and advisors are essential elements of the negotiation. Ownership sought has the potential to impact the deal's complexity and exercisable control. Prior ownership may either help gain information or recover due diligence costs. The advisors may assist the firms in the available information's financial and legal due diligence. Prior research lacks the negotiation period’s dynamism and gives mixed results for the impact of ownership and the role of advisors. We uniquely identify a non-linear inverted U-shaped relationship between the negotiation period and completion likelihood. The results prove that prior ownership, over the negotiation period, serves the dual objective of assisting deal completion and acting as a substitute for the termination fee. The acquirer and target advisors help with the completion of the deal. The varying effects for ownership and advisors over the negotiation process help explain the conflicting results in previous studies. The evolving negotiating circumstances play an important role in affecting the outcome. Our work shows the importance of temporal characteristics and the dynamic nature of decision-making during the M&A negotiation process and encourages further studies exploring processual theories. Collapse

Topics: logistics management decision making database system mergers and acquisition missing data

Methods: logistic regression random forest classification decision tree classification statistical hypothesis test machine learning

Theories: lemon market theory

Orchestrating ‘Institutional Network’ for the Sustainability of IS Program: Evidence from Indian Public Healthcare

2023 | International Conference on Information Systems | Citations: 0

Authors: Kumar, Mayank; Sahay, Sundeep; Mukherjee, Arunima S.; Chandwani, Rajesh

Abstract: Sustaining an IS program in healthcare encounters the complexities of the dynam ... Expand

Abstract: Sustaining an IS program in healthcare encounters the complexities of the dynamic and loosely connected network of institutionally powerful actors. We apply 'orchestration' framework to examine the orchestration of public healthcare by a low power stakeholder for sustaining an IS program. We study the case of a hospital information systems running across 20+ hospitals in Himachal Pradesh, India, for more than a decade. Analysis informs that institutional network orchestration goes through multiple interconnected stages, each presenting unique dilemmas. The orchestrator takes different roles to perform various relation and material institutional work across these stages and often leverages on unanticipated events, just by being physically and temporally present. We identify four orchestration stages-promoting member engagement, building distributed ownership, managing partnership, and managing coherence. Initial stage institutional work helps orchestrator accumulate 'symbolic power', which proves critical in later stage orchestration. Findings contribute to orchestration framework and inform IS in healthcare literature. Collapse

Topics: IT workforce open source health information system green IS digital ecosystem

Methods: case study archival research personal interview longitudinal research

Responsible Artificial Intelligence (AI) for Value Formation and Market Performance in Healthcare: the Mediating Role of Patient’s Cognitive Engagement

2023 | Information Systems Frontiers | Citations: 0

Authors: Kumar, Pradeep; Dwivedi, Yogesh K.; Anand, Ambuj

Abstract: The Healthcare sector has been at the forefront of the adoption of artificial in ... Expand

Abstract: The Healthcare sector has been at the forefront of the adoption of artificial intelligence (AI) technologies. Owing to the nature of the services and the vulnerability of a large section of end-users, the topic of responsible AI has become the subject of widespread study and discussion. We conduct a mixed-method study to identify the constituents of responsible AI in the healthcare sector and investigate its role in value formation and market performance. The study context is India, where AI technologies are in the developing phase. The results from 12 in-depth interviews enrich the more nuanced understanding of how different facets of responsible AI guide healthcare firms in evidence-based medicine and improved patient centered care. PLS-SEM analysis of 290 survey responses validates the theoretical framework and establishes responsible AI as a third-order factor. The 174 dyadic data findings also confirm the mediation mechanism of the patient’s cognitive engagement with responsible AI-solutions and perceived value, which leads to market performance. Collapse

Topics: responsible artificial intelligence artificial intelligence IT skill privacy health information system

Methods: survey qualitative interview mixed method partial least squares path modeling personal interview

Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation

2023 | International Journal of Information Management | Citations: 0

Authors: Kumar, Pradeep; Sharma, Sujeet Kumar; Dutot, Vincent

Abstract: Although AI-enabled customer relationship management (CRM) systems have gained m ... Expand

Abstract: Although AI-enabled customer relationship management (CRM) systems have gained momentum in healthcare to enhance performance, there is a striking dearth of knowledge on how such capabilities are formed and affect service innovation. The study adopted a mixed-method approach to investigate the underlying phenomena. This research infused resource-based theory, dynamic capability theory, and theory of productivity paradox to investigate how healthcare in India acquires AI-enabled CRM capabilities and enhances service innovation. We identified the facets of AI-enabled CRM capabilities using a case study and developed a framework for AI-enabled CRM capability and service innovation. This study noticed that customer service flexibility (CSF) is a missing link in this relationship. The findings of the quantitative study employing PLS-SEM reveal the linear relationships between AI-enabled CRM capability, CSF, and service innovation. This study explains the formation of AIenabled CRM capabilities to fill the research gap and direct innovative performance in healthcare, which is an immediate need to sustain in a volatile environment. This study provides theoretical implications to enhance the research stream and practical implications for decision-makers. Collapse

Topics: artificial intelligence innovation management business process innovation customer service productivity

Methods: partial least squares regression survey qualitative interview survey design mixed method

Theories: dynamic capabilities theory resource based view of the firm