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This chapter presents the current research status of family constitutions from an economics perspective. It locates the family constitution as part of the family and business governance structure of a family firm and the owner family. The typical structure and content of a family constitution are introduced. The chapter focuses on the status of research about family constitutions and provides a structured map for future research. With regard to extant research, it must be stated that the stock of literature is small. The contributions to literature are categorized in surveys; conceptual contributions; survey data; small sample, qualitative, empirical studies; and big sample, quantitative, empirical studies. The latter group includes three studies with a separate family constitution variable. This small number symbolizes that the family constitution still is an under-researched area. Therefore, family constitution research is far away from being able to answer central questions of advice-seeking owner families like, for example, whether a family constitution affects family performance, firm performance, or both; or whether the development process of a family constitutions disposes of an effect on family or firm performance separately from the hypothesized effect of the family constitution document.

The main purpose of this research is to (1) prioritise key determinants of Industry 4.0 (I4.0) readiness assessment and (2) evaluate causal relationships among those determinants and associated sub-criteria based on inputs from industry experts.

The methodology involved two phases: (1) an MCDM approach for determining causal relationships among determinants and (2) empirical validation of findings from the first phase using industry experts' inputs.

It was found that while the choice of I4.0 technologies is important, organisational factors are also critical, as evidenced by the ranking of the “Strategy and Organisation” determinant as the highest rank prominent determinant. Also, the ranking of the sub-criteria within each determinant shows the importance of several organisational influencing and resulting sub-criteria.

This research extends the existing literature on I4.0 by demonstrating the prioritisation of determinants and delineating causal relationships among them and associated sub-criteria as a basis for developing I4.0 adoption guidelines. This research is limited to the specific scope of determinants selected/considered and experts' inputs from the Sri Lankan manufacturing sector. Future studies could consider extending this research into a broader global manufacturing context.

Prioritisation and causal relationships of I4.0 readiness assessment determinants, supported with inputs from functional managers and industry experts, could be used to guide practitioners in developing guidelines for I4.0 adoption in a phased manner.

This research provides a re-evaluation and validation of a selected I4.0 readiness assessment framework from the perspectives of interdependencies and casual relationships among its determinants and sub-criteria, based on inputs from industry experts as a basis for developing guidelines for I4.0 adoption.

The advent of Industry 4.0 (I4.0) and the requirements imposed on companies still need to be clarified. Companies still strive to understand I4.0 requirements and technological, organizational, operational and management challenges. Current literature on I4.0 underlies the importance of a roadmap with structured steps to achieve the benefits of I4.0, mainly focused on augmenting operational performance. Therefore, this paper proposes a roadmap to implement I4.0 focused on operational management concepts, mainly aiming to augment operational performance and bridge the gap between theory and practice regarding roadmaps focused on the operational management dimension.

This paper follows a research approach divided into the following stages: a literature review to analyze the I4.0 roadmaps and identify the main components of I4.0; development of the proposed I4.0 roadmap presented; field research to test the roadmap by collecting data from a manufacturing company in the automotive industry; validation of the roadmap through modeling and simulation.

The authors presented a production line design with real-time control, fast response, shop floor coordination and predictive capacity. The results prove that the proposed I4.0 roadmap augments operation performance in the investigated automotive company. The main results were work in process reduction, lead time reduction, output increase, real-time control, shop floor coordination and fast response.

The main novelty of the proposed roadmap is to move toward I4.0 implementation with a focus on the operational management dimension. The roadmap has an innovative combination of the two approaches – lean manufacturing and factory physics – a straightforward roadmap with only three steps: (1) requirements, (2) real-time control and (3) predictive capacity, a structured definition of the approaches and operational management concepts fundamental in each step.

The purpose of this study is to identify the prominent research constituents in the domain of integration of Lean manufacturing and Industry 4.0 techniques and analyze the intellectual structure among them.

A bibliometric analysis of articles based on Donthu et al. (2021a) has been adopted to conduct a systematic review of the integration of Lean manufacturing and Industry 4.0 using the Scopus database.

The co-citation analysis and bibliographic coupling depicted three clusters and themes around which the research related to the integration of Lean manufacturing and Industry 4.0. Publications related to the topic have majorly focused on the development of conceptual models and frameworks for integrating Lean manufacturing and Industry 4.0, analyzing the compatibility between the two techniques for better implementation of one another and the techniques' combined impact on operational performance.

Most of the review studies related to the domain of integration of Lean manufacturing and Industry 4.0 have adopted a systematic literature review methodology. The present study has tried to infer the intellectual framework of the research being conducted in the said domain using the bibliometric analysis to identify the prominent research constituents in the field and examine the intellectual relationship between them.

Significant developments in the service sector have been brought about by Industry 4.0. Automated digital technologies make it possible to upgrade existing services and develop modern industrial services. This study prioritizes critical factors for adopting Industry 4.0 in the Indian service industries.

The author identified four criteria and fifteen significant factors from the relevant literature that have been corroborated by industry experts. Models are then developed by the analytical hierarchy process (AHP) and analytical network process (ANP) approach to ascertain the significant factors for adopting Industry 4.0 in service industries. Further, sensitivity analysis has been conducted to determine the sensitivities of the rank of criteria and sub-factors to corroborate the results.

The outcome reveals the top significant criteria as organizational criteria (0.5019) and innovation criteria (0.3081). This study prioritizes six significant factors information technology (IT) specialization, digital decentralization of all departments, organizational size, smart services through customer data, top management support and Industry 4.0 infrastructure in the transition toward Industry 4.0 in the service industries.

The potential factors identified in this study will assist managers in determining strategies to effectively manage the Industry 4.0 transition by concentrating on top priorities when leveraging Industry 4.0. The significance of organizational and innovation criteria given more weight will lay the groundwork for future Industry 4.0 implementation guidelines in service industries.

Our research is novel since, to our knowledge, no previous study has investigated the potential critical factors from organizational, environmental, innovation and cost dimensions. Thus, the potential critical factors identified are the contributions of this study.

There is increasing interest in the connection between Industry 4.0 (I4.0) and operational excellence approaches; however, studies on the integration between Six Sigma (SS) and I4.0 have been absent from the literature. Integration with I4.0 technologies can maximize the positive effects of SS. The purpose of this study is to understand what types of relationships exist between SS and I4.0 and with I4.0's technologies, as well as the benefits derived from this integration and future directions for this field of study.

A Systematic Literature Review (SLR) was carried out to analyze studies about connections between I4.0 technologies and SS. SLR analyzed 59 articles from 2013 to 2021 extracted from the Web of Science and Scopus databases, including documents from journals and conferences.

The SLR identified relationships between SS and several I4.0 technologies, the most cited and with the greatest possibilities of relationships being Big Data/Big Data Analytics (BDA) and Internet of Things (IoT). Three main types of relationships were identified: (1) support of I4.0 technologies to SS; (2) assistance from the SS to the introduction of I4.0 technologies, and, to a lesser extent; (3) incompatibilities between SS and I4.0 technologies. The benefits are mainly related to availability of large data sets and real-time information, enabling better decision-making in less time.

In addition, the study can help managers to understand the integration relationships, which may encourage companies to adopt SS/Lean Six Sigma (LSS) in conjunction with I4.0 technologies. The results also drew attention to the incompatibilities between SS and I4.0 to anticipate potential barriers to implementation.

The study focuses on three previously unexplored subjects: the connection between SS and I4.0, the existing relationships with different technologies and the benefits resulting from the relationships. In addition, the study compiled and structured different types of relationships for SS and I4.0 and I4.0's technologies, identifying patterns and presenting evidence on how these relationships occur. Finally, exposes current trends and possible research directions.

Artificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an important concept remains underexplored in AI marketing research and practices. This study aims to introduce the concept of AI-enabled personalization (AIP), understand the applications of AIP throughout the customer journey and draw up a future research agenda for AIP.

Drawing upon Lemon and Verhoef's customer journey, the authors explore relevant literature and industry observations on AIP applications in interactive marketing. The authors identify the dilemmas of AIP practices in different stages of customer journeys and make important managerial recommendations in response to such dilemmas.

AIP manifests itself as personalized profiling, navigation, nudges and retention in the five stages of the customer journey. In response to the dilemmas throughout the customer journey, the authors developed a series of managerial recommendations. The paper is concluded by highlighting the future research directions of AIP, from the perspectives of conceptualization, contextualization, application, implication and consumer interactions.

New conceptual ideas are presented in respect of how to harness AIP in the interactive marketing field. This study highlights the tensions in personalization research in the digital age and sets future research agenda.

This paper reveals the dilemmas in the practices of personalization marketing and proposes managerial implications to address such dilemmas from both the managerial and technological perspectives.

This is one of the first research papers dedicated to the application of AI in interactive marketing through the lenses of personalization. This paper pushes the boundaries of AI research in the marketing field. Drawing upon AIP research and managerial issues, the authors specify the AI–customer interactions along the touch points in the customer journey in order to inform and inspire future AIP research and practices.

Anecdotal evidence suggests that casting actors with similar facial features in a movie can pose challenges in foreign markets, hindering the audience's ability to recognize and remember characters. Extending developments in the literature on the cross-race effect, we hypothesize that facial similarity – the extent to which the actors starring in a movie share similar facial features – will reduce the country-level box-office performance of US movies in East and South-East Asia (ESEA) countries.

We assembled data from various secondary data sources on US non-animation movies (2012–2021) and their releases in ESEA countries. Combining the data resulted in a cross-section of 2,616 movie-country observations.

Actors' facial similarity in a US movie's cast reduces its box-office performance in ESEA countries. This effect is weakened as immigration in the country, internet penetration in the country and star power increase and strengthened as cast size increases.

This first study on the effects of cast's facial similarity on box-office performance represents a novel extension to the growing literature on the antecedents of movies' box-office performance by being at the intersection of the two literature streams on (1) the box-office effects of cast characteristics and (2) the antecedents, in general, of box-office performance in the ESEA region.

This study aims to examine the role of the dimensions of entrepreneurial orientation (EO) under turbulent market conditions and reveal the role of an entrepreneur's perception of a crisis in shaping the impact of EO on firm performance.

This study uses partial least squares structural equation modeling (PLS-SEM), multiple linear regression (MLR) and fuzzy-set qualitative comparative analysis (fsQCA). The study sample was comprised of 117 one- and two-star hotels that were operating in Poland.

The results showed that proactiveness and risk-taking significantly affected firm performance. Furthermore, the results revealed that an entrepreneur's perception of a crisis moderated the impact of risk-taking and proactiveness on firm performance. In particular, the findings suggested that, in firms where the crisis strongly influenced their operations, performance was affected by proactiveness, while in those firms where the crisis influenced their operations to a low or moderate degree, performance was affected by risk-taking. Furthermore, fsQCA unveiled the role of innovativeness, which (along with risk-taking) is a sufficient condition that leads to firm performance.

Two characteristics make this study original: first, it investigates EO under turbulent market conditions, and second, it analyzes the role of an entrepreneur's perception of crisis consequences for business operations. The study contributes to the literature on entrepreneurship and crisis management with findings on the different roles of EO dimensions under crisis conditions and an observation about the moderating role of an entrepreneur's perception of the impact of a crisis on operational management and how this perception differentiates the impact of risk-taking and proactiveness on firm performance.