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The authors propose the concept of “Autonomic Cycle for innovation processes,” which defines a set of tasks of data analysis, whose objective is to improve the innovation process in micro-, small and medium-sized enterprises (MSMEs).

The authors design autonomic cycles where each data analysis task interacts with each other and has different roles: some of them must observe the innovation process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the innovation process.

In this article, the authors identify three innovation sub-processes which can be applied to autonomic cycles, which allow interoperating the actors of innovation processes (data, people, things and services). These autonomic cycles define an innovation problem, specify innovation requirements, and finally, evaluate the results of the innovation process, respectively. Finally, the authors instance/apply the autonomic cycle of data analysis tasks to determine the innovation problem in the textile industry.

It is necessary to implement all autonomous cycles of data analysis tasks (ACODATs) in a real scenario to verify their functionalities. Also, it is important to determine the most important knowledge models required in the ACODAT for the definition of the innovation problem. Once determined this, it is necessary to define the relevant everything mining techniques required for their implementations, such as service and process mining tasks.

ACODAT for the definition of the innovation problem is essential in a process innovation because it allows the organization to identify opportunities for improvement.

The main contributions of this work are: For an innovation process is specified its ACODATs in order to manage it. A multidimensional data model for the management of an innovation process is defined, which stores the required information of the organization and of the context. The ACODAT for the definition of the innovation problem is detailed and instanced in the textile industry. The Artificial Intelligence (AI) techniques required for the ACODAT for the innovation problem definition are specified, in order to obtain the knowledge models (prediction and diagnosis) for the management of the innovation process for MSMEs of the textile industry.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

The main purpose of this review is to enhance the understanding of intellectual structure and outlook of management innovation research as an interesting and growing research field.

This systematic literature review examines the question, what is the relationship of management innovation with the performance of companies and with other types of innovation? The work also pursues to summarize theories, contexts, characteristics of the papers and methodologies with the purpose of facilitating further development and opportunities and priorities for future research.

The results suggest that management innovation is an interesting and growing research field; in its relation to different types of innovation and performance, it is a field explored with theoretical approaches, contexts and methodologies that begin to form a consolidated body of knowledge. However, through a critical analysis, this review highlights the gaps in the literature and provides suggestions for future studies to further explore the field. This revision contributes to the literature on management innovation summarizing the findings and contributions of research published in the field and its relationships with innovation and performance. It then identifies three comprehensive research streams, namely, future research on conceptualization, definitions and measurements; research on the level of analysis; and future research on management innovation drivers, antecedents and use as mediator/moderator variables.

Management innovation is an emerging research field that is characterized as a branch of research long ignored by more orthodox lines dedicated to technological innovation and topics in product and service development research.

This paper aims to analyse the relationships between management innovation, marketing innovation, technological innovation and the personnel involved in science, technology and innovation activities.

The work used data from the Technological Development and Innovation Survey – Colombian Industry VII 2013–2014. Six logistic regression models are tested for the analysis with 2,045 manufacturing firms.

The results suggest that the probability to pursue technological innovation diminishes in those firms that introduce management and/or marketing innovations. The same happens in firms seeking non-technological innovations with the introduction of product and process innovations. The human side, administrative and technical staff, working on innovation projects plays a key role in the success of different types of innovations.

At this time, there is a need for research studies with new approaches that look at innovation beyond the technological domain and focus on the human side of innovation and other important aspects such as the managerial contribution to innovation. Theoretically, the work contributes to expanding the scarce literature on the proposed relationship and, as far as is known, it is the only one with empirical data for an emerging economy such as the Colombian one. Empirically, useful information is provided for the design of strategies that seek to improve firms' innovation performance.

This chapter provides an in-depth look at how digital supply chain management (DSCM) can revolutionize supply chains in the post-COVID world. The COVID-19 pandemic exposed the vulnerabilities of traditional supply chains, highlighting the need for resilience and adaptability. The chapter begins by examining these COVID-induced disruptions, setting the foundation for the discussion on DSCM. DSCM, leveraging advanced technologies and data insights, offers a solution to these challenges, promoting agility, transparency, and sustainability in supply chain operations. This represents a significant shift from traditional practices, equipping organizations to cope with the dynamic postpandemic environment. Key capabilities of DSCM, such as resilience, integration, agility, and risk management, are discussed, supported by real-world examples from leading companies. These examples showcase the successful implementation of DSCM and its benefits in navigating the complexities of modern supply chains. However, the adoption of DSCM is not without challenges, including cybersecurity risks and integration difficulties. The chapter suggests strategies to overcome these challenges, emphasizing the importance of technology, collaboration, sustainability, and data-driven decision-making. By embracing these strategies, organizations can effectively manage their supply chains in the evolving global market, leveraging DSCM to withstand future uncertainties.

The aim of this paper is to provide a review of state-of-the-art literature on information sharing in the context of African perishable agri-food supply chains (AFSCs). In doing so, the authors hope to stimulate further research and advance both theory and practice on African perishable AFSCs, which is a relevant, but under-investigated context.

The authors’ systematic literature review covers a period of 21 years (2000–2021). After providing the bibliometric and methodological insights related to this sample of literature, the authors provide a detailed analysis and discussion of the key aspects of information sharing in African perishable AFSCs, based on a review framework grounded in the information sharing literature.

The authors’ review revealed that information sharing in African AFSCs is still in its nascent stage. Findings are based on four themes of (1) why share information (mainly to gain market access), (2) what information is shared (price and market information) (3) how it is shared (still traditional communication, with limited adoption of digital technologies?) and (4) antecedents, drivers and barriers (technology adoption and socio-economic background of Africans).

This paper outlines a research agenda for advancing the theory on information sharing in AFSCs. Furthermore, the review highlights the importance of context, supply chain structure, relationships, product characteristics and culture in studying AFSCs.

A review on information sharing in African perishable AFSCs does not appear to exist in operations and supply chain management (O&SCM) and agribusiness journals.

This study aims to theoretically hypothesize and empirically examine the impact of economic policy uncertainty (EPU) on firms' innovation performance as well as the contingency conditions of this relationship.

This study collects and combines secondary longitudinal data from multiple sources to test for a direct impact of EPU on firms' innovation performance. It further examines the moderating effects of firms' operational and marketing capabilities. A series of robustness checks are performed to ensure the consistency of the findings.

In contrast to the common belief that EPU reduces the innovativeness of firms, the authors find an inverted-U relationship between EPU and innovation performance, indicating that a moderate level of EPU actually promotes innovation. Further analysis suggests that firms' operational and marketing capabilities make the inverted-U relationship steeper, further enhancing firms' innovation performance at a moderate level of EPU.

This study adds to the emerging literature that investigates the operational implications of EPU, which enhances our understanding of the potential bright side of EPU and broadens the scope of operational risk management.

Customers often think that innovations, such as self-service technologies (SSTs), are introduced by service providers to cut costs rather than extend customer service levels. The purpose of this paper is to investigate how customers use such attributions to adjust their perceptions of relational value.

Drawing on attribution and relationship marketing theories, this study proposes a conceptual model that includes benefit and cost attributions, their antecedents, and consequences. Survey data came from customers of a supermarket that recently introduced self-scanning technology.

Attributions mediate the impact of SST performance on relational value. This value is highest for customers with high-benefit and low-cost attributions; customers with low-benefit and low-cost attributions exhibit detrimental effects on the exchange relationship with the firm. Characterized by low self-efficacy, low education, and low spending, these latter customers appear ambivalent and possibly confused about the provider’s motives for introducing SST.

This research has important implications for service managers responsible for communicating technological innovations to customers. A clear reason for the introduction should be provided, to stimulate customers’ attribution and prevent ambivalence among those with low self-efficacy and low education.

Most SST research focusses on adoption, non-adoption, and disadoption. The more subtle responses by customers facing a new SST and the consequences for the customer-provider exchange relationship, as addressed herein, have been left largely unexplored.

The purpose of this study is to empirically investigate the extent to which two types of commercial partnerships (business partner and non-business partner) affect the collaborative innovation of firms in emerging economies. Specifically, the roles of two commercial partnerships are investigated. Additionally, the study explores the moderating effect of external technological uncertainty and internal dynamic capabilities on the relationship between two commercial partnerships and on collaborative innovation.

Using a sample of 370 high-tech firms in China, the authors applied the partial least squares structural equation modeling approach to model these relationships.

The findings reveal opportunities and challenges for companies according to two intensities of commercial partnership for collaborative innovation. The partnership contribution to innovation and competiveness is different within the two routes and ranges. The findings indicate that (1) intense commercial relationships with business partners have a stronger positive significant impact on collaborative innovation than those with non-business partners and (2) non-business partners have a weaker positive impact on collaborative innovation at high external technological uncertainty. It was also found that (3) the positive impact of business partners on collaborative innovation is weakened when a firm has high dynamic capabilities, whereas the positive impact of non-business partners is strengthened.

Insight into the roles of two commercial partnerships in achieving collaborative innovation facilitates the advancement of the theoretical understanding of the circumstances under which cooperative innovation can be more effective under different partnerships.

A key strategic question is whether comprehensiveness enables firms to make better strategic decisions in various environments. In the process of innovation, companies must choose different types and quantities of partners, and they must regulate their partners’ innovative behavior by establishing a corresponding network structure and relationship rules. The current study focuses on analysis of how different intensities of commercial partnerships affect collaborative innovation. This research provides a theoretical framework that creates a new classification of commercial relations with regard to collaborative innovation, and it highlights the difference between the two types of partnerships. This study finds that there are many problems in the selection of innovative partners in China’s high-tech companies. Therefore, companies should strengthen their understanding of cooperative innovation, and they should build and manage highly efficient innovation networks. This study helps companies, high-tech industry associations, academia and government to take enhanced, informed actions.