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Smart cities require data governance to articulate data sharing and use among relevant stakeholders. Given the lack of a comprehensive examination of this research topic, this study aims to review data governance publications to detect and categorize endeavors backing up data sharing in smart cities.

A systematic literature review was conducted, and 568 academic and professional sources were identified, but finally, only 10 relevant papers were selected.

Results reveal that data governance must be based on well-defined mechanisms, procedures and roles to achieve accountability and responsibility in a multi-actor environment. Moreover, data governance should be adapted to address power imbalances among all interested parties.

The main limitation is the list of sources considered for the literature review. However, this study provides a holistic overview for researchers and professionals willing to know more about smart city data sharing.

This review identifies the data governance approaches supporting data sharing in smart cities, analyzes their data dimension, enhances the state-of-the-art literature on this topic and suggests possible areas for future research.

The goal of this work is to evaluate how digital transformation disclosure in corporate news and press releases affects stock prices. We examine American and Chinese companies from the energy and industry sectors for two periods: pre-COVID-19 and during the COVID-19 pandemic.

To estimate the effects of disclosure of information related to digital transformation, we applied the bag-of-words (BOW) method. As the benchmark dictionary, we used Kindermann et al. (2021), with the addition of original dictionaries created via Latent Dirichlet allocation (LDA) analysis. We also employed panel regression analysis and random forest.

For USA energy sector, all aspects of digital transformation were insignificant in pre-COVID-19 period, while sustainability topics became significant during the pandemic. As for the Chinese energy sector, digital strategy implementation was significant in pre-pandemic period, while digital technologies adoption and business model innovation became relevant in COVID-19 period. The results show the greater significance of digital transformation aspects for industrials sectors compared to the energy sector. The result of random forest analysis proves the efficiency of the authors’ dictionary which could be applied in practice. The developed methodology can be considered relevant.

The research contributes to the existing literature in theoretical, empirical and methodological ways. It applies signaling and information asymmetry theories to the financial markets, digital transformation being used as an instrument. The methodological contribution of this article can be described in several ways. Firstly, our data collection process differs from that in previous papers, as the data are gathered “from investor’s point of view”, i.e. we use all public information published by the company. Secondly, in addition to the use of existing dictionaries based on Kindermann et al. (2021), with our own modifications, we apply the original methodology based on LDA analysis. The empirical contribution of this research is the following. Unlike past works, we do not focus on particular technologies (Hong et al., 2023) connected with digital transformation, but try to cover all multi-dimensional aspects of the transformational process and aim to discover the most significant one.

Climate change-induced weather changes are severe and frequent, making it difficult to predict apparel sales. The primary goal of this study was to assess consumers' responses to winter apparel searches when external stimuli, such as weather, calendars and promotions arise and to develop a decision-making tool that allows apparel retailers to establish sales strategies according to external stimuli.

The theoretical framework of this study was the effect of external stimuli, such as calendar, promotion and weather, on seasonal apparel search in a consumer's decision-making process. Using weather observation data and Google Trends over the past 12 years, from 2008 to 2020, consumers' responses to external stimuli were analyzed using a classification and regression tree to gain consumer insights into the decision process. The relative importance of the factors in the model was determined, a tree model was developed and the model was tested.

Winter apparel searches increased when the average, maximum and minimum temperatures, windchill, and the previous day's windchill decreased. The month of the year varies depending on weather factors, and promotional sales events do not increase search activities for seasonal apparel. However, sales events during the higher-than-normal temperature season triggered search activity for seasonal apparel.

Consumer responses to external stimuli were analyzed through classification and regression trees to discover consumer insights into the decision-making process to improve stock management because climate change-induced weather changes are unpredictable.

Smart manufacturing (SM) lies at the core of Industry 4.0. Uniform adoption of SM across business partners is crucial to exploit its value creation potential. However, firms' willingness to invest in SM is limited by insufficient or inconclusive evidence on its performance-related benefits. To close this gap, this paper develops and tests a model linking SM adoption to firms' financial performance. Improvements along the four dimensions of operational performance (i.e. cost quality, delivery and flexibility) mediate this relation.

This study follows an empirical research approach. In particular, survey data from 234 automotive component suppliers are analyzed via covariance-based structural equation modeling to explore the link between SM adoption and operational performance. Survey data are then matched with secondary data from balance sheets of 81 firms to investigate the impact of SM on financial performance via partial least square structural equation modeling.

Findings highlight that adoption of SM results in improvements in cost, quality, delivery performance, thus suggesting that SM is a mean to overcome performance trade-offs. Improvements in operational performance enabled by SM do not give rise to superior financial performance, thus implying that SM might support firms in maintaining the competitive position in the market, but could be insufficient to generate higher margin.

Results have implications for SM research and for manufacturing executives engaged in the adoption of SM, as they provide a detailed analysis of the impact of SM on operational performance and clarify the effect that SM adoption has on financial performance.

In the era of digital disruption and customer loyalty loss, it has become even more important to shape the experience journey of a firm’s stakeholders. The benefits of experience data (XD) analysis for a competitive advantage and firm performance are well proven in the business-to-customer context. Therefore, this study aims to explore the limited exploitation of XD in the business-to-business (B2B) context.

The data of 338 B2B firms is generated through computer-assisted telephone interviewing using a structured interview guideline. A Mann–Whitney U test and binary linear regression are applied to test hypotheses derived from literature.

The results suggest that XD non-collectors see XD increase efficiency, whereas XD collectors view XD strategically beyond customer data. Additionally, the successful application of XD in firms can be fostered by connecting XD with operational data through digitalised processes, strategic usage and data collection at certain defined points of time.

This study contributes to the understanding of XD perception between collectors and non-collectors and develops determinants for the successful application of XD management. Based on the results, B2B marketing executives from academics and practice can foster the implementation of XD management to improve all firm’s stakeholders’ experiences. In this way, this study contributes to the understanding of managing not only customers’ but other stakeholders’ experiences.

This public-sourced case describes the latest restructuring efforts by Deutsche Bank (DB) and gives a short history of prior restructuring efforts from the decade before. In July 2019, Christian Sewing, the new CEO of DB, announced a series of measures that included, among others, the elimination of global equity trading, the layoff of 18,000 employees, the creation of a “bad bank” to transfer noncore assets, and the suspension of dividends until 2022. The case describes key decisions a bank CEO makes when a bank needs to change course to return to profitability and growth. The case offers an opportunity to debate these key decisions, as well as discuss some of the prior ones during earlier restructuring efforts, and put the students in the CEO's shoes: What would you do and why? The case also describes key banking performance metrics (e.g., ROE, ROA) and other critical variables such as those reflecting capital health (Tier 1 ratio), as well as gives an overview of the bank business model and factors impacting bank profitability and value.

This study aims to investigate the relationship between corporate governance (CG) and financial performance in the case of publicly listed companies in Vietnam for the period from 2019 to 2021. The topic is crucial in understanding how effective governance practices can influence the financial outcomes of companies. The study sheds light on the link between CG practice and firm financial performance. It also provides insights for policymakers and practitioners to improve CG practices.

Due to the potential dynamic endogeneity in CG research, this study uses the generalized system methods of moments to effectively address the endogeneity problem. Financial performance is measured by Tobin’s Q, return on equity (ROE) and return on assets (ROA). Based on organization for economic cooperation and development (OECD) standards, these indices were calculated to assess the influence of CG practices on corporate financial performance, namely, for accounting information (ROA and ROE) and market performance (Tobin’s Q and service à resglement différé (SRD) – stock price volatility) for the period 2019–2021. In addition, the study examines the relationship between changes in the CG index and changes in financial performance.

The study’s main objective is to determine the relationship between CG performance scores and financial performance. The study found a positive relationship between transparency disclosure and financial performance and a positive correlation between CG and company size. The COVID-19 pandemic caused a decrease in transparency and information index scores in 2021 compared to 2019 and 2020 due to delayed General Meetings of Shareholders. The study failed to find a relationship between shareholder rights index (“cg_rosh”) and board responsibility (“cg_reob”) and financial performance, concerning which the findings of this study differ from those of previous studies. Reasons are put forward for these anomalies.

Policymakers need to develop a set of criteria for assessing CG practices. They also need to promulgate specific regulations for mandatory and voluntary information disclosure and designate a competent authority to certify the transparency of company information. The study also suggests that companies should develop CG regulations and focus on regulations relating to the business culture or ethics, as well as implementing a system to ensure equal treatment among shareholders. The study found that good CG practices can positively contribute to a company’s financial performance, which is crucial for investors to evaluate the quality of CG practices for each listed company so that investment risks can be limited.