Introduction: Decentralized autonomous organizations (DAOs) are an emerging organizational form that operates entirely on blockchain infrastructure. Within a DAO, organizational governance rules are hardcoded in transparent and immutable smart contracts. In principle, these rules are intended to facilitate decentralized decision-making among token holders who collectively create, discuss, and vote on proposals that govern the organization. Despite their promise, the extent to which DAOs achieve true decentralization in practice remains unclear. This study addresses an underexplored area in the literature by empirically investigating key aspects of DAO governance, particularly the degree of decentralization and participant composition.Method: Network analysis is used to examine proposal voting coalitions among participants as a proxy for decentralization. Sentiment analysis is employed to assess trust among participants. The analysis draws on data from 54 DAOs, including 774 unique proposals and 13,085 associated token holder comments.Results: The findings indicate that DAOs may not achieve the level of decentralization originally envisioned. Moreover, decentralization and participant composition within governance structures play a critical role in shaping trust, voting participation, and overall financial performance in DAOs PracticalImplications: Although current voting mechanisms aim to reduce the dominance of large token holders (whales), DAOs may still fall short of the level of decentralization originally envisioned. Accordingly, more advanced voting mechanisms may be required to further mitigate coordination and strategic behavior in proposal voting. In addition, DAOs could benefit from adjusting the threshold requirements for the Foundation to improve accessibility for token holders and encourage broader participation. Leveraging artificial intelligence (AI) may also help streamline the voting process and improve the clarity of proposals.

This editorial introduces the special issue "The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience.' We selected twelve papers, encompassing many angles that illuminate the advances and challenges dealing with artificial intelligence tools and approaches in the production systems and operations management resilience domains. This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions. Finally, the conclusions, future pathways, and research directions are presented.

Distributed autonomous organizations (DAOs) are a new organization form that resides entirely on a blockchain. In a DAO, organizational governance rules are hardcoded in an immutable smart contract. This paper examines whether DAOs are able to adapt their governance structures when shocks in the external environment occur. If the DAO is truly decentralized and governance is hardcoded in a smart contract, then effective adaptation may be a challenge. Using case examples, we illustrate approaches to governance adaptation including orderly voting by DAO members, contentious voting with exit by some participants as the DAO evolve, hard forks in the event of negotiation failure, off-chain resolution mechanisms, and the role of a "benevolent dictator". We provide justification for why DAOs may not be as decentralized as conceptualized if they are to be effective. Our research contributes to the theoretical development of DAO management strategies as this new organizational form evolves.

Smart devices connected to the Internet of Things (IoT) platform create new opportunities to provide services to users. Often these smart devices are mobile robots that provide their own power and navigation, or they may be wearables attached to our clothing or person. Examples include autonomous vehicles, robots in factories or homes, and healthcare monitors worn by individuals. As these smart devices move, they can continuously collect data about their changing environment, their own state as they adjust to their environment, or about the user to whom they provide services. Finding ways to collect, store and analyze such big data is a major challenge for service providers. Data from these mobile smart devices also generates a number of privacy challenges that may impact peoples’ intention to adopt such devices and services. As these smart devices become more common, how will people adjust to their presence in their everyday lives? How will service providers make use of this data to provide more personalized services? How will our surrounding environment change because of these increasingly ubiquitous devices? Will users be able to exploit this data to become more fulfilled, productive, and happy? Or will the data be used to exploit individuals in ways they may not even know? This mini-track provides a forum for researchers to address these types of issues related to the proliferation of IoT-enabled smart devices and the resulting big data they produce. The sole paper in this mini-track is entitled “Do Blockchain and IoT Architecture Create Informedness to Support Provenance Tracking in the Product Lifecycle?” by Somnath Mazumdar, Thomas Jensen, Raghava Rao Mukkamala, Robert Kauffman, and Jan Damsgaard. This paper develops and proposes a blockchain-IoT architecture to allay sustainability questions. These days, consumers often lack information about the origin and provenance of the products they buy. They may ask: Is a food product truly organic? Or, what is the origin of the gemstone in the ring I purchased? They also may have sustainability concerns about the footprint of a product at the end of its life. Producers and sellers, meanwhile, wish to know how longitudinal tracking of the provenance of products and their components can boost their sales prices and after-market value, and reveal new business opportunities. The authors of this study focus on how the product lifecycle (PLC) can be leveraged to track information that typically has not been available to support distributed activities. Instead, they have been supported by the manufacturers that create new products. They propose an architecture that utilizes blockchain and the Internet of Things (IoT) to support a range of PLC use case scenarios – from production to marketing and consumption, to maintenance and refurbishment, as well as recycling and disposal. They also offer design thinking about blockchain-IoT architecture to support products such as textiles, furniture and food. A key contribution is an architecture for cross-PLC management support and an explanation of its potential to enhance value through stakeholder informedness. Eventually the authors propose an architecture for information that documents the product lifecycle based on blockchain and IoT devices attached to physical products. The proposed platform is different compared to other state-of-the-art and commercial tools that aim to capture data across the PLC. Their most important overall contribution is to show how it is possible to map important information that needs to be captured, stored and made accessible for use by PLC stakeholders – wherever they are, and whatever the PLC stage in which they are involved. The approach that they advocate is essentially aimed at reconsidering how to enhance the information endowment, so it is possible to more fully track PLC activities and further integrate stakeholders’ legacy systems into a commonly accessible platform. All in all, we believe the Internet of Everything is an emerging area that will define the future ecosystem of information systems, especially combined with location and other data. We believe this is a ripe area for future research. Proceedings of the 54th Hawaii International Conference on System Sciences | 2021

Online peer-to-peer (P2P) lending platforms are gaining popularity for providing financing and loans to small and micro entrepreneurs, particularly in developing parts of the world. This study investigates how individual borrowers in the P2P platform can improve the chance of their loans being funded. Based on theories related to cognitive and affective aspects of information processing, a set of loan description features is identified and evaluated based on their influence on funding success. Sentiment analysis, a text mining technique, is used to analyze and extract emotions from an unstructured P2P loan data set. The results reveal valuable information in the P2P lending context such that, in the absence of market interest rates, borrowers can improve the chance of funding success by improving the textual quality of their loan descriptions in terms of readability and linguistic correctness. In addition, borrowers can make the loan descriptions more attractive to potential lenders by expressing certain emotions in the descriptions.

Virtual reality (VR) provides new opportunities for businesses to gain competitive advantages by enabling them to innovatively engage customers. Based on presence theory, this study aims to test the influences of two major components of presence, social presence and spatial presence, on users’ perceptions of hedonic value, utilitarian value and engagement in the VR environment. An experiment was conducted on two conditions of a VR application (low vs. high social presence) to test the hypotheses proposed in the research model. The results reveal that social presence and spatial presence can improve the hedonic value of VR. However, inconsistent with previous studies, our findings reveal a negative relationship between spatial presence and engagement. Theoretical and practical implications and future research directions are subsequently discussed.

This mini-track addresses issues organizations face as they seek to create and realize business value from incorporating the emerging Internet of Things (IoT) into their organizational infrastructure, their electronic business partner relationships, and the products and services they offer to customers.The IoT is allowing the possibility of tracking and tracing any tagged mobile object as it moves through the value chain thus producing unprecedented end-to-end supply chain visibility.This creates tremendous opportunities for operational and strategic benefits.However, the effective management of this new visibility for improved decision making requires the combination and analysis of data from item-level identification using RFID, sensors, satellites, social media feeds, photos, video and cell phone GPS signals; in short, big data analytics.While the IoT, combined with wireless sensor networks and big data analytics have tremendous potential for transforming various industries, many scholars and practitioners struggle to understand these concepts and capture business value of smart devices being connected through the IoT.In our first paper entitled "Building Dynamic Capabilities with the Internet of Things," Mary Dunaway, Yulia Sullivan, and Samuel Fosso Wamba propose a useful framework where a firm's dynamic capabilities impact the firm's competitive advantage.In this framework, firms can possess IoT capabilities that allow them to sense and shape opportunities and threats in the competitive environment, better seize upon these opportunities, and finally are able to reconfigure assets and resources for the changing competitive landscape.Using an online questionnaire, they measured 184 respondents to validate their model.This study provides useful measures for IoT capabilities that provide theoretical and practical insights.

Virtual reality (VR) provides opportunities for businesses to innovatively engage customers. Based on presence theory, a research model was developed to test the influence of two major components of presence, social presence and spatial presence, on users’ perceptions of hedonic value, utilitarian value, and engagement. An experiment was conducted on two conditions of a VR application (low vs. high social presence) to test the hypotheses in the research model. The results reveal that social presence and spatial presence can improve hedonic value of VR. However, inconsistent with previous studies, our findings reveal a negative relationship between spatial presence and engagement. Theoretical and practical implications, as well as future research directions are subsequently discussed.

•Is an interdisciplinary case for accounting, MIS, and other business students.•Leads practice by incorporating a modern day problem not yet resolved in practice.•Explores data governance in relation to self-service business intelligence (BI).•Focuses on maintaining data integrity and quality given self-service BI tools.

This minitrack addresses issues organizations face as they seek to provide services to end-users through wearable or autonomous mobile devices using the emerging Internet of Things (IoT) platform. The IoT allows connectivity of billions of mobile devices that can perform physical, sensing, and analytical services to users. Many of these devices exist in physical proximity of the user making up part of the user’s personal area network (PAN). Others are wearable devices that sense and track metrics about the quantified self and make up the user’s body area network (BAN). Finally, other mobile devices are semi-autonomous or autonomous and have the ability to move and actuate on their surroundings in an effort to provide services to users who may or may not be in physical proximity of the devices. These scenarios create business opportunities for organizations seeking to provide services to these users.

The Internet has created new opportunities for peer-to-peer (P2P) social lending platforms which have the potential to transform the way microfinance institutions (MFIs) raise and allocate funds used for poverty reduction. Depending upon where decision making rights are allocated, there is the potential for identification bias whereby lenders may be motivated to give to specific projects with which they have an affinity without regard to whether it represents a sound financial investment. We present empirical evidence that identification biases exist at Kiva, a P2P social lending platform, resulting in inefficient loan funding decisions. Even so, prior research indicates that this inefficiency may result in more lending toward poverty reduction than in the absence of identification bias.

Presents an introduction to the minitrack session.

The microfinance industry provides financial services to the world’s poor in hopes of moving individuals and families out of poverty. In 2013 there were 4.7 million active microfinance borrowers in Africa. This represents a smaller percentage of the population compared to other regions of the world, indicating the potential for rapid growth of microfinance in Africa. However, microfinance is maturing in part due to the adoption of information and communication technologies (ICTs). This research examines how ICTs are changing the microfinance industry given recent advancements in mobile banking, Internet usage, and connectivity. By examining the microfinance market structure, we determine that ICTs impact intermediation and market structure among various players in the microfinance industry. We use recent industry risk reports from 2011 through 2014 to inform our predictions of changes to the intermediation structure of the industry. We give particular attention to the impact of ICT on microfinance in Africa.

Intense debate regarding how connected smart devices might create or destroy value is ongoing. Some view connected smart devices as beneficial to consumers, while others argue such devices and related automation may maximize business value, but result in labor substitution and negative long-term economic impacts. This debate extends to the research literature where some scholars take a pessimistic view while others are more optimistic. We seek to address such tensions by working toward a rigorous understanding and description of connected smart devices and the potential effects on consumer value and business value. We develop a foundational, theoretically-based taxonomy of smart connected devices as a basis for understanding where value creation potential lies. We then propose a model to explain how complementarities between humans and smart devices, in terms of the assignment of physical tasks, resource capabilities, information flows, and allocation of decision rights, may affect consumer, business, and societal value.

HICSS-48 marks the beginning of a new mini-track on topics at the intersection of the Internet of Things and Big Data Analytics. The mini-track addresses issues organizations face as they seek to make use of data collected from mobile tracking devices such as RFID and other tracking and sensor technologies. Big data analytics is an increasingly important activity that is driven by the pervasive diffusion and adoption of RFID, mobile devices, social media tools, and the Internet of Things (IoT). The IoT allows for the connection and interaction of smart devices as they move and exist within today’s value chain. This allows for unprecedented process visibility that creates tremendous opportunities for operational and strategic benefits. However, the effective management of this visibility for improved decision making requires the combination and analysis of data from item-level identification using RFID, sensors, social media feeds, and cell phone GPS signals; in short, big data analytics. While the IoT and big data analytics have tremendous potential for transforming various industries, many scholars and practitioners are struggling to capture the business value from combining the IoT and big data analytics. In addition, little research has been conducted to assess the potential of the IoT using big data analytics. In this mini-track, we hope to develop a stream of research where researchers will share new and interesting theoretical and methodological perspectives on this topic. We believe the papers represented in this inaugural mini-track are a good kickoff to what we hope will be more exciting and enlightening each year. We open the mini-track with a paper entitled “Research Directions on the Adoption, Usage and Impact of the Internet of Things through the Use of Big Data Analytics” where Fred Riggins and Samuel Fosso Wamba bring into focus several of the important research questions this mini-track will address. The paper begins by defining current perspectives on the IoT and highlights current research in this area. It then proposes a framework for analyzing the adoption, usage and impact of the IoT enabled through big data analytics. The framework is applied to several research questions that need to be examined if researchers are to understand the non-technical issues related to the emergence of the IoT. Specifically, research questions are posed at four levels of analysis: the individual, organizational, industry, and societal levels. The second paper by Robert Minch is entitled “Location Privacy in the Era of the Internet of Things and Big Data Analytics.” As the IoT emerges there is concern that loss of privacy may occur that could impact individuals’ incentives to belong to online networks, interact using online social media, and engage in activities associated with being digital citizens. These privacy issues involve sensing activities, identification and authentication of identities, storage of personal information, processing of this information, incentives to share information, and the range of activities available to use this information. These six phases of information flow all take place within three different contexts: technical, social, and legal contexts. This paper examines these issues across these six phases of information flow and identifies example privacy measures that are being used, and can be used, for each phase. A literature review of existing research on the technical, social, and legal measures is provided. The third paper, “Dynamic Price Prediction for Amazon Spot Instances” by Vivek Kumar Singh and Kaushik Dutta illustrates the importance of being able to dynamically and efficiently price services in contexts such as the IoT. In the case examined in this paper, cloud vendors, such as Amazon Web Services, provide “spot instances” of cloud-based resources that are dynamically priced through an auction mechanism. This paper develops a novel algorithm for spot price prediction that shows high accuracy of 9.4% Mean Absolute Percent Error (MAPE) for short term forecasting (one day ahead) and less than 20% MAPE for long term forecasting (five days ahead). Such novel pricing algorithms will find a place within the context of the IoT as spot services will need to be negotiated, priced, and provided with a short lead time. 2015 48th Hawaii International Conference on System Sciences

The number of devices connected to the Internet of Things (IoT) by the year 2020 may be as high as 75 billion. Long before that, big data analytics will be needed to make use of the data generated by the Internet of Things. While the technical issues needed to create the Internet of Things are substantial, little attention has been given to the behavioral, organizational and business issues that are necessary for a better understanding of the adoption, usage and impact of the IoT. We propose a framework based on the idea that this technology will evolve from monitored "Things", to "Networks of Things", and ultimately to an "Internet of Things". Each of these instantiations of the technology raises adoption, usage and impact issues that can be scrutinized at four levels of analysis: individual, organization, industry, and society. We apply the framework to propose research questions that need to be addressed by researchers.

The microfinance industry provides financial services to the world's poor in hopes of moving individuals and families out of poverty. This research examines how information and communication technologies (ICTs) are changing the microfinance industry given recent advancements in mobile banking, Internet usage, and connectivity. By examining the microfinance market structure, we determine that ICTs impact intermediation and market structure among various players in the microfinance industry. We use a recent industry risk report to inform predictions of changes to the intermediation structure of the industry.

In this paper, we develop the RFID e-Valuation Framework to help managers identify the value proposition from using RFID. The framework is based on three concepts. First, firms can apply RFID along five dimension of commerce. Specifically, by using various modes of interaction, firms compete over both time and distance in order to provide some product or service through a chain of relationships eventually ending with the end customer. Second, new investments in RFID are typically justified by generating efficiency, effectiveness, and/or strategic benefits. Third, RFID can be applied to four different structural settings across the value chain including inbound logistics, internal operations, outbound logistics, and marketing and sales. By mapping the five dimensions of commerce and the three types of justification, we have developed the RFID e-Valuation Grid which can then be applied to the four structural settings to see where RFID can generate business value. We illustrate usage of the framework by applying it to two examples of asset management in a hospital setting and tracking food produce through the cold chain.