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In short-term forecasting of day-ahead electricity prices, incorporating intraday dependencies is vital for accurate predictions. However, it quickly leads to dimensionality problems, i.e. ill-defined models with too many parameters, which require an adequate remedy. This study addresses this issue.

In an application for the German/Austrian market, this study derives variable importance scores from a random forest algorithm, feeds the identified variables into a support vector machine and compares the resulting forecasting technique to other approaches (such as dynamic factor models, penalized regressions or Bayesian shrinkage) that are commonly used to resolve dimensionality problems.

This study develops full importance profiles stating which hours of which past days have the highest predictive power for specific hours in the future. Using the profile information in the forecasting setup leads to very promising results compared to the alternatives. Furthermore, the importance profiles provide a possible explanation why some forecasting methods are more accurate for certain hours of the day than others. They also help to explain why simple forecast combination schemes tend to outperform the full battery of models considered in the comprehensive comparative study.

With the information contained in the variable importance scores and the results of the extensive model comparison, this study essentially provides guidelines for variable and model selection in future electricity market research.

This chapter reviews the literature on servitization to understand whether and how mergers and acquisitions (M&As) have been dealt with and what the portrayed consequences are of servitization through M&As. Servitization refers to how manufacturing firms extend and remodel their offerings to focus on value in use rather than product transfer. The rationale of the chapter follows from how business model innovation or business modeling has been predicted as the next M&A wave, while the focus on servitization has been pronounced in research and practice as a means for manufacturing firms to refocus operations during the past decade. The chapter concludes that while the servitization literature is vibrant, the mode of reaching service competence and renewing business is not well explored in the literature. In line with the predicted next M&A wave, servitization through M&As would thereby create an interesting path for future research.

Industry 4.0 has been one of the most topics of interest by researches and practitioners in recent years. Then, researches which bring new insights related to the subjects linked to the Industry 4.0 become relevant to support Industry 4.0's initiatives as well as for the deployment of new research works. Considering “organizational learning” as one of the most crucial subjects in this new context, this article aims to identify dimensions present in the literature regarding the relation between organizational learning and Industry 4.0 seeking to clarify how learning can be understood into the context of the fourth industrial revolution. In addition, future research directions are presented as well.

This study is based on a systematic literature review that covers Industry 4.0 and organizational learning based on publications made from 2012, when the topic of Industry 4.0 was coined in Germany, using data basis Web of Science and Google Scholar. Also, NVivo software was used in order to identify keywords and the respective dimensions and constructs found out on this research.

Nine dimensions were identified between organizational learning and Industry 4.0. These include management, Industry 4.0, general industry, technology, sustainability, application, interaction between industry and the academia, education and training and competency and skills. These dimensions may be viewed in three main constructs which are essentially in order to understand and manage learning in Industry 4.0's programs. They are: learning development, Industry 4.0 structure and technology Adoption.

Even though there are relatively few publications that have studied the relationship between organizational learning and Industry 4.0, this article makes a material contribution to both the theory in relation to Industry 4.0 and the theory of learning - for its unprecedented nature, introducing the dimensions comprising this relation as well as possible future research directions encouraging empirical researches.

This article identifies the thematic dimensions relative to Industry 4.0 and organizational learning. The understanding of this relation has a relevant contribution to professionals acting in the field of organizational learning and Industry 4.0 in the sense of affording an adequate deployment of these elements by organizations.

This article is unique for filling a gap in the academic literature in terms of understanding the relation between organizational learning and Industry 4.0. The article also provides future research directions on learning within the context of Industry 4.0.

This study aims to examine the impact of regulatory, normative and cultural cognitive institutions and firm and individual factors on entrepreneurial behavior.

Using the quantitative research method, the authors collected data from 316 micro, small and medium enterprises (MSMEs) in Kosovo, a transition economy, through a cross-sectional research design. The authors performed exploratory factor analyses, correlation and regression analyses on the data using SPSS 26 and STATA software.

The research findings indicate that, within transition economies, normative and cultural-cognitive institutions have a positive impact on entrepreneurial behaviors. The authors could not determine the effect of regulatory institutions on entrepreneurial behavior. The authors also discovered that young firms are more inclined toward entrepreneurial behavior than older firms, and micro firms display a stronger entrepreneurial behavior than small firms. Furthermore, family businesses showed a greater tendency for entrepreneurial behavior than nonfamily firms. Interestingly, when the rational decision-making interacts with regulatory institutions, the effect on entrepreneurial behavior is negative.

This study employed a cross-sectional approach to investigate the influence of macro, meso, and micro-level factors on entrepreneurial behavior within a transitioning community across three industries. Future studies could replicate these findings within comparable institutional contexts, employing longitudinal studies that include additional variables beyond those considered in our present study.

Considering the importance of MSMEs for a country’s economic and sustainable development, the authors provide some policy implications. The authors recommend managers carefully evaluate the information gathered while they decide and also increase their capabilities concerning digitalization, which is crucial for their firm’s survival, growth and sustainable competitive advantage.

This paper contributes to the literature and shows and analyses entrepreneurial behavior at institutional (macro), firm-level factors (meso) and managers' rational decision-making (micro), providing evidence from a transition community.

This study aims to investigate the effects of firm- and governance-specific characteristics on digital trust (DT) and firm value. Firm-specific factors include return on assets (ROA), market-to-book ratio (M/B ratio), size and leverage, whilst governance-related factors comprise board size, percentage of female board members, board independence and institutional ownership. All listed US firms over the period of 2011–2016 were analysed in this study.

This study provides a novel method to empirically measure DT by combining multiple variables to create a combined DT score. The variables include security and privacy scores, security rankings and data breaches, amongst others. Subsequently, a linear regression was performed to evaluate the effect of firm- and governance-specific characteristics on DT, as well as the effect of DT on firm value.

By using signalling theory, this study finds significant evidence that a firm’s profitability (ROA) decreases whilst its size increases DT. This could be due to the fact that firms with lower DT monetise data more actively, decrease DT and increase short-term profitability. Significant evidence also shows that increasing DT leads to an increase in firm value.

Although numerous studies have been conducted on developing customers’ trust by incorporating corporate social responsibility to improve firm value, the literature remains still on its digital analogue. Therefore, this study extends the knowledge of corporate digital responsibility (CDR) by providing a novel method for calculating DT across industries as an antecedent of CDR. Specifically, it sheds light on how firms can enhance DT by utilising firm- and governance-level factors. This enhanced DT can subsequently increase firm value. The study provides important managerial implications by providing empirical evidence that cybersecurity investments increase firm value. This value increase is related to the rise in shareholder value amongst investors and the increase in the organisation’s consumer perceptions as the latter’s interests are better managed.

To evaluate the comparative effectiveness of perceptions‐based market segmentation strategies: to what extent do consumers' choice rules and the distinctness and variability of consumer preferences determine the success or failure of PBMS strategies?

The computer simulation is run on an artificial consumer market. Its firm and consumer agents enjoy a certain extent of autonomy and a limited capability of learning. Strategies for incorporating the choice information into the firms' segmentation schemes, consumers' brand choice rules, initial preference patterns and their variability over time are factors in the experimental design.

The market factors “brand choice rule” and “distinctness” and “adaptivity” of preferences significantly influence the profit performance of the segmentation and positioning strategies. The distinctness of the initial pattern of consumer preferences turns out to be least influential while the choice rule is most important.

Computer simulation cannot replace analyses of real‐world data. When, however, advanced explanatory models are made to fit to empirical data the results sometimes are disappointing (and then do not get published). Computer simulation on artificial markets assists in exploring the reasons for success or failure.

Boundedly rational consumers; product classes which are technologically homogeneous and subject to communications‐driven differentiation; consumer preferences that are directly inaccessible and must be inferred from actual brand choice; consumers' perceptions and preferences evolving over time are realistic settings.

Controlling for conditions such as the consumers' choice rules and the distribution and variability of preferences in real markets demands a prohibitive research effort. No empirical study so far has tried to systematically relate the profit performance of marketing strategies to choice rules and preference distinctness and variability.

Smart services have gained attention both among academics and practitioners, but manufacturing firms struggle in getting their new smart services extensively adopted by customers, employees and distributors. The purpose of this paper is to identify and analyse the requirements of different actors and the interconnectedness between their requirements in introducing smart services.

An embedded single-case study was conducted with a manufacturing firm and its network, including its sales and service personnel, customers and external salespeople. Data were collected via 30 in-depth interviews.

The paper advances the multi-actor perspective by identifying the requirements of key actors for introducing smart services. These requirements were divided into eight categories: value of smart services, reliability of smart services, competence for smart services, data security and management, attitude towards services, reliance, knowledge of installed base of equipment and services and service reputation. The findings reveal the interconnectedness of different actors’ requirements for introducing new smart services and how discussion and relationships between actors affected their requirements.

The findings represent a comprehensive template of requirements, as well as mapping the interconnectedness of actors’ requirements, serving as a practical guideline for managers.

This study characterises the introduction of smart services as a multi-dimensional, interconnected effort by manufacturing firms and their networks. It shows that service introduction cannot be viewed as manufacturer’s development task or customers’ adoption decision only. Propositions are offered on how multiple actors’ viewpoints can be combined to achieve success in introducing smart services.