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Digital technologies promise efficiency gains and untapped opportunity. Adoptions of digital technology lead firms to rethink their organizational setup and existing practices. This paper aims to present a management innovation-based framework that describes new processes and practices for digital transformation.

This study uses a structural equation modeling approach to test the framework with survey responses from a sample of 901 Taiwanese organizations – both local firms and multinational subsidiaries – to explore the linkages between adoption of digital technologies and digital maturity.

The results reveal that management innovation mediates the relationship between digital technology adoption and digital maturity. Moreover, fast-paced environments have a greater impact of management innovation toward digital maturity than slow-paced environments.

This study adds to emerging research that considers the role of organizational learning in digital transformation efforts. The extent to which organizations link the lessons from direct experience to digital routines through which management innovation is implemented determines to a large extent whether this strategic initiative is optimized by the firm. More generally, the findings point to the mutual importance of digital maturity and experiential learning efforts, and suggest a specific means by which learning processes are mobilized by innovating organizations. This study contributes to digital transformation research by providing insight into how a firm can restart failed transformation initiatives of this kind.

Currently, most research about the autism spectrum has examined Caucasian autistics. Consequently, African American autistics have not received much attention from scholars. This chapter begins with an overview of statistics from the Centres for Disease Control and Prevention (CDC) about the population of Black and Latino autistics in the United States from 2000 to 2016. Plausible reasons for why there is a lack of research about African American autistics are then presented along with a section about the underrepresentation of ethnic minorities in autism spectrum research. Four strategies that can improve the production of research about African American autistics are then presented. The purpose of presenting these strategies is to help stimulate the production of research about African American autistics.

The original contribution that this chapter makes to the field of autism spectrum research is to inform the reader about the lack of research about African American autistics in comparison to other ethnicities.

This article aims to examine the influence of chief executive officer (CEO) internal political beliefs on labor relations. Prior research has paid little attention to channels through which the internal personal value system of managers enhances or deteriorates firm value. The authors provide evidence consistent with CEOs adopting labor policies impacting incumbent management–labor relationships based upon their political ideologies.

The research design tests the impact of CEO political ideology on labor relation using an individual CEO’s personal information and firm affiliation, employee lawsuit information, financial contributions to candidates and committees, and firm financial information. The authors compiled a sample of 4,354 unique CEOs from 2,558 US firms that are covered by ExecuComp and used 18,404 firm-year observations for the study’s analysis. A Heckman two-stage estimation process is used to address a potential sample selection bias and match the requirements of exclusion and relevance criteria.

Findings indicate that firms led by Republican-leaning CEOs are more likely to be sued by their employees, especially for violating union rights. Moreover, the findings of the study uncovered that Republican-leaning CEOs have fewer cases dismissed or withdrawn compared to Democrat-leaning CEOs and are also less likely to settle court cases prior to trial. Results indicate that Republican-leaning CEOs are associated with more substantial decreases in firm value compared to Democrat-leaning CEOs when facing labor allegations. The authors further show that firm value is lower for all firms facing litigation, with the magnitude of the decrease being more pronounced for firms with Republican CEOs.

Firm affiliations are identified using ExecuComp, employee lawsuit information from the National Labor Relations Board (NLRB), financial contributions to candidates and committees from the Federal Election Committee (FEC) website, and financial information from Compustat. To the extent that these websites are inaccurate, such as financial contributions being underreported, the findings reported here may understate the relationships reported in this article.

The authors capture CEO political ideology using political contributions. There may be other means, such as physical space and personal effort, by which one could also estimate the party and intensity of CEO political ideology. This information is unavailable.

While presidential politics has four-year cycles, managerial finance is a daily activity. While political affiliation is most clearly measurable through monetary contributions, one can see implications of manager political leaning through their relationship with labor throughout the election cycle.

The analyses of this study indicate that labor unions are more likely to sponsor lawsuits and stronger allegations in firms with Republican CEOs and show that withdrawal, settlement or dismissal rates are lower when firms are managed by Republican managers, resulting in higher subsequent legal costs and potentially damaged employee morale. Also, this paper investigates whether lawsuits have a greater negative consequence on firm value when the firm is run by a Republican CEO. The authors find that lawsuits significantly lower Tobin's Q for Republican-led firms compared to companies with Democratic and apolitical CEOs. The authors further show that firm value is lower for all firms facing litigation, with the magnitude of the decrease being more pronounced for firms with Republican CEOs.

The purpose of this paper is to examine the nuanced effects of downstream complexity on supply chain resilience, based on portfolio theory and normal accident theory. Intelligent manufacturing is considered to clarify their boundary conditions.

The ordinary least squares regression was conducted, based on the data collected from 136 high-tech firms in China.

Horizontal downstream complexity has a positive effect on supply chain resilience significantly, while the negative impact of vertical downstream complexity on supply chain resilience is not significant. Contingently, intelligent manufacturing plays a negative moderating role in the relationship between horizontal downstream complexity and supply chain resilience, while it positively moderates the relationship between vertical downstream complexity and supply chain resilience.

This study disentangles the nuanced effects of both horizontal and vertical downstream complexity on supply chain resilience, based on portfolio theory and normal accident theory. It also clarifies their boundary conditions by considering the focal firm's intelligent manufacturing level as the contingent factor.

Exploiting novel measures of climate change exposure and corporate culture generated by a powerful textual analysis of earnings conference calls, this study aims to explore the effect of firm-specific climate change exposure on corporate innovation through the lens of corporate culture.

The authors apply the standard regression analysis as well as a variety of sophisticated techniques, namely, propensity score matching, entropy balancing and an instrumental-variable analysis with multiple alternative instruments.

The authors find that more exposure to climate change risk results in more innovation, as indicated by a significantly stronger culture of innovation. The findings are consistent with the notion that firms more exposed to climate change risk are pressed to be more innovative to adapt to the numerous changes caused by climate change. Finally, the authors also find that the effect of firm-level exposure on innovation is considerably less pronounced during uncertain times.

The authors are among the first studies to take advantage of a novel measure of firm-specific exposure to climate change and investigate how climate change exposure influences an innovative culture. Since climate change is a timely issue, the findings offer important implication to several stakeholders, such as shareholders, executives and investors in general.

The rapid proliferation of Internet of Things (IoT) devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet. Fog computing, which brings computation resources closer to IoT devices, has emerged as a promising solution. An automatic service placement framework is needed to use fog computing resources efficiently.

In this study, first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers, facilitating automatic application deployment. Next, an enhanced version of the equilibrium optimizer (EO) algorithm, inspired by physics, is designed for service placement in fog computing environments.

Simulations reveal that the proposed approach surpasses existing methods, achieving a 99% success rate compared to the closest alternative’s 93%. The algorithm also significantly reduces waiting and planning times for service placement, proving its efficiency and effectiveness in optimizing IoT service deployment in fog computing.

One of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads. Although improved EO offers improvements in placement efficiency, it may not be fully optimized for highly fluctuating environments. Another important limitation is the uncertainty in node resources. Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes. This uncertainty can affect the algorithm’s ability to consistently make optimal decisions for IoT service placement.

From a practical perspective, the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems. Organizations that rely on IoT networks, particularly those with critical real-time requirements, can benefit from reduced service placement times and lower failure rates. This can lead to better resource utilization, reduced operational costs and improved overall performance of IoT systems. The commercial impact is evident in industries such as smart cities, healthcare, where fast data processing is crucial.

Our proposed framework has important implications for real-world IoT applications, particularly in areas requiring low latency processing, such as healthcare, smart cities. By reducing service delays and optimizing resource allocation, the framework can significantly improve the quality and reliability of services. Additionally, improved resource management leads to cost savings and better system efficiency, making the technology accessible to a wider range of applications.

Existing resource placement strategies have shown inadequate performance, highlighting the need for more advanced algorithms. This study introduces a three-layer automatic framework for enhancing the application deployment of a fog system beside a novel improved EO algorithm to offer a robust solution for assigning IoT applications to fog nodes.

The purpose of this paper is to address the Internet of Things (IoT) service discovery problem and investigate the existing solutions to tackle this problem in many aspects.

This paper presents an overview of IoT services aiming at providing a clear understanding about their features because this term is still ambiguous for the IoT service discovery approaches. Besides, a full comparison study of the most representative service discovery approaches in the literature is presented over four perspectives: the IoT information model, the mechanism of IoT service discovery, the adopted architecture and the context awareness. These perspectives allow classifying, comparing and giving a deeper understanding of the existing IoT service discovery solutions.

This paper presents a new definition and a new classification of IoT services and citation of their features comparing with the traditional Web services. This paper discusses the existing solutions, as well as the main challenges, that face the service discovery issue in the IoT domain. Besides, two classifications of the approaches are adopted on the basis of their service description model and their mechanism of discovery, and a set of requirements that need to be considered when defining an IoT service are proposed.

There are few number works that survey the service discovery approaches in the IoT domain, but none of these surveys discuss the service description models in the IoT or the impact of the context awareness aspect in the service discovery solution. There are also few works that give a comprehensive overview of IoT services to understand their nature to facilitate their description and discovery. This paper fills this gap by performing a full comparison study of multi-category and recent approaches for service discovery in the IoT over many aspects and also by performing a comprehensive study of the IoT service features.

Grounded on the value-based adoption model and innovation diffusion theory, this study examined consumer purchase decisions of mobile Global Positioning System (GPS) navigation apps. In addition, this study also investigated the moderating role that perceived availability of free substitutes (PAFS) plays in the relationship between perceived value and purchase intention. The paper aims to discuss these issues.

Data collected from 219 mobile users were analyzed against the research model using the partial least squares approach.

The results showed that compatibility, relative advantage, perceived enjoyment, perceived cost (positively), and complexity (negatively) influenced these users’ value perceptions and purchase decisions. Furthermore, PAFS significantly weakened the positive relationship between perceived value and purchase intentions.

Based on these findings, the authors provide practical suggestions for mobile app developers to increase mobile app sales. This study also helps advance knowledge of mobile internet marketing.

This study is a pioneering effort in explaining consumer purchase intentions in the context of mobile GPS navigation app.