The Impact of Green Disclosure Nudging in Online Reuse Markets: Evidence from a Natural Experiment

2023 | International Conference on Information Systems | Citations: 0

Authors: Liao, Yunqi; Liu, Xiaoxiao; Gong, Xiang; Xiao, Zhenxin

Abstract: As the environment continues to deteriorate, for managers, the means of shiftin ... Expand

Abstract: As the environment continues to deteriorate, for managers, the means of shifting consumer behavior to "green" is urgently needed. Information disclosure as a tactic to promote green consumption has been widely studied, which typically stimulates demand for new green products through cost disclosure. In contrast, the impact of environmental benefit information disclosure on green consumption, especially on online reuse platforms, remains to be ascertained. In this study, we examine the economic effects of a green disclosure nudge through a natural experiment. Drawing on daily sales data, we find that the green disclosure nudge can stimulate consumer demand and generate economic benefits. We provide suggestive evidence that this positive effect stems from an increase in consumers' perceptions of the functional and symbolic value of used products, respectively. In addition, exploratory analysis shows that the nudge may have potential social benefits. The findings provide practical and theoretical implications for promoting green consumption. Collapse

Topics: data breach electronic commerce digital media marketing management mobile platform

Methods: natural experiment experimental group regression analysis method Poisson regression causality analysis

Under Pressure? - The Effect of Conversational Agents on Task Pressure and Social Relatedness in Digital Labor

2023 | International Conference on Information Systems | Citations: 0

Authors: Lichtenberg, Sascha; Hildebrandt, Fabian; Mirbabaie, Milad

Abstract: Digital labor platforms (and many other digital workplaces) can be anonymous, i ... Expand

Abstract: Digital labor platforms (and many other digital workplaces) can be anonymous, isolated, and lacking social interaction. In this context, implementing conversational agents (CAs) to provide social presence and relatedness could be a remedy. However, based on research in the context of human-to-human interaction, two counteracting effects of CAs' social presence can be derived. First, social presence and social relatedness induce enjoyment. Second, social presence can lead to a perception of task pressure, which reduces enjoyment. We conducted a three-condition online experiment with 269 participants from a commercial digital labor platform to investigate these effects. Our results show that social presence directly leads to social relatedness and enjoyment, and indirectly to task pressure. However, this perceived task pressure does not reduce enjoyment and only positively effects performance. Thus, it appears that introducing CAs as part of digital labor platforms is a win-win situation for users and work. Collapse

Topics: social presence virtual workplace electronic mail IT supported collaboration remote work

Methods: experiment experimental group chatbot survey parametric test

Theories: social response theory

“My Precious!”: A Values-Affordances Perspective on the Adoption of Bitcoin

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Lichti, Constantin; Tumasjan, Andranik

Abstract: Given the growing pervasiveness of information systems (IS) in everyday life, r ... Expand

Abstract: Given the growing pervasiveness of information systems (IS) in everyday life, recent research has acknowledged that IS technologies are often not value free but are instead infused with fundamental personal values. However, little is known about how such values explain why people assimilate these technologies and their affordances. In the intriguing case of Bitcoin, personal values-especially libertarian political values-played an essential role in clarifying the ideological underpinnings of Bitcoin and its early adoption. Consequently, we draw on research on personal values and affordance theory to develop and test a model explicating how these personal values guide individuals toward using IS applications with salient affordances that address their values. Specifically, we hypothesize and test how individuals' personal values (i.e., libertarian political values) influence their attitudes toward Bitcoin affordances and their Bitcoin use behavior using data from a multiple administration survey of 236 users and nonusers of Bitcoin. Our results indicate that libertarian political values affect individuals' attitudes toward Bitcoin affordances, which in turn mediate the effects of these values on actual Bitcoin use. Our findings advance the field by demonstrating the importance of integrating values into the conceptualization of IS technology affordances. Collapse

Topics: Bitcoin affordance blockchain information system use user experience

Methods: theory development survey design artifact data transformation post-hoc analysis

Theories: theory of affordance sociomaterialism theory socio technical theory

Walking the line: Mindfulness with IT in hospital medication routines

2023 | Information and Organization | Citations: 0

Authors: Lichtner, Valentina; Karanasios, Stan; Iannacci, Federico

Abstract: This paper addresses the dilemma that organizations face when they introduce inf ... Expand

Abstract: This paper addresses the dilemma that organizations face when they introduce information technology (IT) to standardize and guide operations and improve performance, while also sup­ porting staff mindfulness in using IT and questioning it, to safeguard against errors. People are warned to be mindful in using the information provided by IT, yet IT may contribute to their mindlessness. Organizational operations involve routine work, where work is distributed across roles, in space and time. To fully understand mindfulness or mindlessness with IT at work it is necessary to consider the routines in which they are embedded. We sought to investigate what factors might influence mindfulness or mindlessness with IT in the context of organizational routines. We carried out an in-depth study of clinicians using technology during medications routines in a UK hospital. The IT in this context aimed to guide and standardize clinical work to improve medication safety. The study uncovered several factors influencing mindfulness and mindlessness with IT: not only the IT design but also task design, individual experience and history of IT use, distribution of work, and the situation at hand. These are interacting influences on mindfulness and mindlessness with IT, each embodying a tension, as each may influence both mindfulness and mindlessness. The distribution of work and the dynamics of the routine over time mean that individuals in the routine may (mindlessly) entrust mindfulness in using IT to others, or to other moments in time. The study highlights the complexity of achieving mindfulness with IT in organizations, and a nuanced relation between mindfulness and IT in a routine work context. Collapse

Topics: mindfulness systems design virtual teamwork personality organizational context

Methods: qualitative interview case study qualitative coding theoretical contribution design science

Who needs XAI in the Energy Sector? A Framework to Upgrade Black Box Explainability

2023 | International Conference on Information Systems | Citations: 0

Authors: Lier, Sarah; Gerlach, Jana; Breitner, Michael H.

Abstract: Artificial Intelligence (AI)-based methods in the energy sector challenge compa ... Expand

Abstract: Artificial Intelligence (AI)-based methods in the energy sector challenge companies, organizations, and societies. Organizational issues include traceability, certifiability, explainability, responsibility, and efficiency. Societal challenges include ethical norms, bias, discrimination, privacy, and information security. Explainable Artificial Intelligence (XAI) can address these issues in various application areas of the energy sector, e.g., power generation forecasting, load management, and network security operations. We derive Key Topics (KTs) and Design Requirements (DRs) and develop Design Principles (DPs) for efficient XAI applications through Design Science Research (DSR). We analyze 179 scientific articles to identify our 8 KTs for XAI implementation through text mining and topic modeling. Based on the KTs, we derive 15 DRs and develop 18 DPs. After that, we discuss and evaluate our results and findings through expert surveys. We develop a Three-Forces Model as a framework for implementing efficient XAI solutions. We provide recommendations and a further research agenda.increase the value creation of such information and mitigate the energy transition-associated complexity (Kruse et al. 2021a;Kuzlu et al. 2020;Machlev et al. 2022a). AI is used for load forecasting, power generation and management, demand side management (DSM), and electrical grid operation and control. AI has high relevance regarding decentralized smart energy solutions, e.g., smart grids (SGs) and the modernization of power grids, although AI is not always considered efficient, as traditional methods are less expensive and more sufficient (Kuzlu et al. 2020). The safe integration of renewable energies is enhanced by hardware like smart meters, energy storage devices, SGs, and software like AI and Blockchain. The modernization of power grids enables the collection of information on power quality and energy consumption data, analysis of data for such purposes as forecasting energy demand, optimizing supply and demand, reducing power generation costs and greenhouse gas emissions, improving grid stability and reliability, optimizing energy efficiency, and responding to unforeseen changes (e.g., Alova et al. 2021;Hatzakis et al. 2019;Kruse et al. 2021b;Machlev et al. 2022b). AI-based energy methods contribute to the transformed energy system by optimizing and scheduling flexibilities to increase power efficiency and consumption (Ponnusamy et al. 2021). Implementing AI solutions makes business processes in the energy sector about 28% more efficient (Benbya et al. 2020). Efficient energy distribution improves consumer welfare and energy organizations' services (Antonopoulos et al. 2020). Due to the non-transparent nature of AI methods, explainability is becoming relevant for users, developers, energy providers, decision-makers, and other stakeholders because of the limited explanations and dynamics in decision-making processes. XAI, also known as white or grey box, enables a better understanding of models and systems through interpretability and traceability (Rawal et al. 2021). Explainability is the degree to which humans understand the decisions made by AI systems. XAI in the energy sector refers to the application of AI technologies, algorithms, and models. They make accurate predictions or decisions and provide transparent and interpretable insights into how those predictions or decisions are reached. XAI aims to bridge the gap between the inherent complexity of advanced AI techniques and the need for understanding and trust in decision-making (Angelov et al. 2021; Arrieta et al. 2019;Rawal et al. 2021). XAI techniques in the energy sector enable stakeholders to comprehend and validate the rationale behind AI-driven recommendations, predictions, or optimizations related to energy production, distribution, consumption, and management. By offering comprehensible explanations, XAI enhances transparency, accountability, and stakeholder confidence in AI-powered solutions, allowing for informed and effective decision-making while minimizing the risks of biased or unexpected outcomes (Angelov et al. 2021; Arrieta et al. 2019;Machlev et al. 2022a). Current XAI literature includes programming XAI algorithms (e.g., Ren et al. 2022;Sairam et al. 2020), monoculture guidelines for XAI applications (e.g., Ignatiev 2020; Kuhn et al. 2020), or XAI challenges and requirements (e.g., Angelov et al. 2019;Rawal et al. 2021). There are general principles, guidelines, and frameworks for XAI. For example, Phillips et al. (2021) derived four XAI principles: explanation, meaning, accuracy, and knowledge limits. Some other big organizations like Google, Amazon, and Tesla also derived general principles, requirements, and guidelines for using and implementing XAI. However, these algorithms, applications, principles, requirements, and guidelines cannot be transmitted completely to using and implementing XAI in the energy sector. The energy sector represents a critical infrastructure with complex impacts. AI decisions impact the economy, environment, and society. Sustainability, environmental protection, and societal acceptance and participation are reasons for customized DPs of XAI in the energy sector (e.g., Alova et al. 2021;Kruse et al. 2021b;Kuzlu et al. 2020;Machlev et al. 2022a). To combine the fragmented scientific knowledge base, we analyze relevant literature and expert interviews to develop DPs for XAI applications in the energy sector. We address the research questions (RQs): RQ1:What XAI design requirements in the energy sector are known, today? Collapse

Topics: explainable artificial intelligence artificial intelligence knowledge base database system cloud computing

Methods: qualitative interview natural language processing topic model mixed method literature study

How to build supply chain resilience: The role of fit mechanisms between digitally-driven business capability and supply chain governance

2023 | Information & Management | Citations: 0

Authors: Lin, Jiabao; Lin, Shunzhi; Benitez, Jose; Luo, Xin (Robert); Ajamieh, Aseel

Abstract: Drawing upon resource orchestration theory, we argue that deploying digitally-dr ... Expand

Abstract: Drawing upon resource orchestration theory, we argue that deploying digitally-driven business capability aligned with supply chain governance may improve supply chain resilience. Using a sample of Chinese agri­ culture firms, the empirical analysis verified three fit mechanisms (complementing fit, balancing fit, and configuring fit) between digitally-driven business capability and supply chain governance and their effects on supply chain resilience. This research offers novel insights into the specific actualization mechanisms by which digitally-driven business capability and supply chain governance jointly improve supply chain resilience. Im­ plications for management and future Information Systems (IS) research are provided. Collapse

Topics: supply chain management innovation management electronic market business process innovation business process management

Methods: survey partial least squares path modeling descriptive statistic regression analysis method cluster analysis

Theories: dynamic capabilities theory

Improved learning performance for small datasets in high dimensions by new dual-net model for non-linear interpolation virtual sample generation

2023 | Decision Support Systems | Citations: 0

Authors: Lin, Liang-Sian; Lin, Yao-San; Li, Der-Chiang; Liu, Yun-Hsuan

Abstract: The number of reliable samples obtained in early decision-making activity is usu ... Expand

Abstract: The number of reliable samples obtained in early decision-making activity is usually relatively small. Due to variable distribution and incomplete structure of tiny datasets, it is challenging to create reliable and robust predictive modeling using classic statistical and machine learning models in small sample settings. The virtual sample generation (VSG) technique improves model learning accuracies for minimal datasets across diverse applications. Virtual samples on independent variables were generated using established VSG methods predi­ cated on the assumption of a probability distribution or a membership function to fill data gaps. However, in the actual world, non-linear function interactions between variables are common. To address this issue, this paper developed a novel VSG method called Dual-VSG, which generates non-linear interpolation virtual samples using a self-supervised learning (SSL) framework to improve learning performance on small datasets. We generated non-linear interpolation virtual samples without labels by estimating non-linear functions and transforming them into a high-dimensional space using the proposed dual-net model. The weights of the dual-net model are transferred to a downstream task to generate virtual sample labels. To demonstrate the effectiveness of the suggested strategy, this research employed five datasets. On the Backpropagation Neural Networks (BPNN) predictive model, we compared the suggested method’s prediction performance to two state-of-the-art VSG approaches. To assess prediction performance on a regression dataset, the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) are used. Furthermore, the classification accuracy (ACC) and the Fl measure are used to assess classification capability on classification datasets. In addition, the paired t-test was utilized to see if the suggested Dual-VSG approach differed significantly from the other VSG methods in terms of RMSE, MAPE, accuracy (ACC), or F1 score. For short datasets, the suggested Dual-VSG method out­ performs those VSG methods, according to our experimental results. Collapse

Topics: Python programming language

Methods: data transformation t-distributed stochastic neighbor embedding experiment machine learning parametric test

Experts vs. Nonexperts in Online Crowdfunding Markets

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Lin, Mingfeng; Sias, Richard; Wei, Zaiyan

Abstract: The growth of crowdfunding markets that include both expert and nonexpert inves ... Expand

Abstract: The growth of crowdfunding markets that include both expert and nonexpert investors will soon accelerate due to recent changes in Securities Exchange Commission (SEC) regulations. Prior work has suggested that nonexperts (1) may benefit from experts' participation via mimicking their trades, but (2) will also face a cost, as experts crowding nonexperts out of the best opportunities will ensure that nonexperts will suffer lower returns than experts. Traditional economic theory holds that the crowding effect means that the relative importance of nonexperts in the market will decline over time until they become unimportant. Exploiting a unique period in one crowdfunding market (Prosper.com) that allowed us to directly estimate the net cost of competing with better-informed experts, we found that the net negative effects of expert participation on nonexperts are small. We used simulations to both better understand (1) the market characteristics and crowdfunding platform choices that influence experts' and nonexperts' returns, their return gap, and the extent to which nonexperts are better or worse off relative to a market without expert participation, and (2) the factors that may contribute to the small expert/nonexpert Prosper return gap. Collapse

Topics: crowdsourcing Prosper crowdfunding extensible markup language accounting

Methods: simulation descriptive statistic logistic regression experiment computational algorithm

Theories: economic theory

A two-stage prediction model based on behavior mining in livestream e-commerce

2023 | Decision Support Systems | Citations: 0

Authors: Lin, Qinping; Jia, Ning; Chen, Liao; Zhong, Shiquan; Yang, Yuance; Gao, Tong

Abstract: Livestream e-commerce has been developing at a tremendous pace in recent years. ... Expand

Abstract: Livestream e-commerce has been developing at a tremendous pace in recent years. On livestream platforms, such as Douyin, a retailer attracts viewers into the live room through short video advertising, and then streamers promote and sell products in real time. In such a scenario, an accurate prediction of traffic and sales plays an essential role in operation management, including live content strategy and inventory control. However, complex behaviors (follow, share, comment, etc.) of users and long conversion paths (from seeing the advertisement to entering the live room, and to purchasing the goods) lead to poor performance of traditional prediction methods. Additionally, few studies focus on advertising information in evaluating live room performance. Therefore, we propose a two-stage learning model for traffic and sales prediction based on behavior mining, which combines marketing models and deep learning methods. In the first stage, we integrate user behaviors before getting into the live room with short video advertising data for traffic prediction. In the second stage, based on the traditional marketing model, AIDA (Attention-Interest-Desire-Action), we design a funnel convolutional neural network (FCNN) to learn sophisticated behaviors in the live room in both time and behavior orientations, and take the predicted traffic volume as the auxiliary information for sales prediction. Extensive experiments on real-world datasets from Douyin illustrate the efficacy of our proposed method, which shows the value of fusing marketing models with deep learning techniques. Furthermore, the in-depth analysis provides practical insights into user behaviors for livestream e-commerce. Collapse

Topics: electronic commerce advertising management user behavior marketing management personality

Methods: experiment deep learning time series analysis longitudinal research convolutional neural network

Theories: management theory

Navel Gazing or the Long View?: The Influence of Firm Positioning with Respect to OSS Communities on a Developer’s Firm Embeddedness

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Lin, Xuan; Daniel, Sherae; Chen, Liwei

Abstract: Platforms increasingly rely on AI algorithms to execute automated task allocati ... Expand

Abstract: Platforms increasingly rely on AI algorithms to execute automated task allocation, monitoring, and performance evaluation of workers in the gig economy. Platforms also expect AI algorithms can prompt gig workers to provide proactive services to customers for a better reputation. However, it remains unclear how algorithmic management impacts the proactive service behavior of gig workers. Based on self-determination theory, this emergent research forum paper proposes a double-edged sword effect of algorithmic management due to conflicting work motives: algorithmic management can increase gig workers' proactive service behavior via autonomy motivation, whereas it can also inhibit proactive service behavior via controlled motivation. A three-wave longitudinal survey will be conducted to test the posited double-edged sword effect. This study has the potential to contribute to the algorithmic management literature in IS field by uncovering the "black box" between algorithm management and proactive service behavior of gig workers from the perspective of work motivation. Collapse

Topics: algorithmic management platform worker

Methods: computational algorithm longitudinal research longitudinal survey

Theories: self determination theory