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The purpose of this study is to investigate a framework for knowledge retention to support business continuity in cross-border mergers of the telecommunications industry in Lesotho.

This study applied a qualitative case study, with data collected through interviews from a purposively selected sample of staff members who held managerial positions. Information in this study was partially extracted from the PhD thesis of Dr Tseole ET supervised by Prof Ngulube P at the University of South Africa completed in 2021.

The study discovered that a considerable amount of knowledge may have been lost because employees who either left the organisation or those who were apparently forced to resign during the process had left without any proper knowledge retention arrangements.

The framework proposed in this study may be used in framing future studies as a theoretical framework. The study also provides new literature for review and discussion of background in future related studies.

The framework provided in the study may be used as a benchmark in the knowledge management industry and/or organisations for policy development or improvements and implementation of knowledge retention strategies.

Through recommendations and framework provided by this study, organisations will be able to improve their services to their sphere of influence in the surrounding communities. So, community will be receiving an improved and good service at all the times.

A framework for facilitating knowledge retention in the cross-border mergers of the telecommunications industry is therefore proposed and the researchers believe it will be helpful to the organisation for improving knowledge retention going forward, particularly in the merger process.

This study aims to explore the interplay between community dynamics and knowledge production using the quantum computing research field as a case study. Quantum computing holds the promise of dramatically increasing computation speed and solving problems that are currently unsolvable in a short space of time. In this highly dynamic area of innovation, computer companies, research laboratories and governments are racing to develop the field.

After constructing temporal co-authorship networks, the authors identify seven different events affecting communities of researchers, which they label: forming, growing, splitting, shrinking, continuing, merging, dissolving. The authors then extract keywords from the titles and abstracts of their contributions to characterize the dynamics of knowledge production and examine the relationship between community events and knowledge production over time.

The findings show that forming and splitting are associated with retaining in memory what is currently known, merging and growing with the creation of new knowledge and splitting, shrinking and dissolving with the curation of knowledge.

Although the link between communities and knowledge has long been established, much less is known about the relationship between the dynamics of communities and their link with collective cognitive processes. To the best of the authors’ knowledge, the present contribution is one of the first to shed light on this dynamic aspect of community knowledge production.

The ongoing anthropological transformation urges the rethinking of education, underlining the inadequacy of our schools and universities in dealing with hypercomplexity, that is, with the global extension of all political, social, and cultural processes and with their indeterminacy, interdependence, and interconnection. The idea that educational processes are questions of a purely technical/technological nature, solely a problem of skills and know-how, is the “great mistake” of the hypertechnological society, based on the illusion of being able to measure and quantify everything, to eliminate error and unpredictability, and to achieve total control and rationality. It is necessary to rethink education radically because the extraordinary scientific discoveries and the dynamics of the new technologies have completely overturned the complex interaction between biological and cultural evolution, doing away with the borders between the natural and the artificial. Emergence and emergency themselves are structural features of complex systems (living, social, and human systems), rendered hypercomplex through today’s acceleration and virality, regarding not only education and socialization but also the representations and perceptions of all systemic processes. The merging of fields of knowledge and an epistemology of error become essential for the analysis and interpretation of this hypercomplexity and the unpredictability that distinguishes it.

Adaptive reuse entails the physical modification of abandoned architectural structures, with the activation of processes and practices leading to the re-incorporation of heritage into the contemporary life of communities. This transformation entails an affective adaptation, a re-modulation of how citizens attune to a built environment that has been returned to urban, shared forms of use. By observing the emotional ties that are established between subjects and the spaces they inhabit, affecting forms of dwelling, attachments and corporeal responses, the author can clarify how adaptation purports this affective modification, where the original ambiance is not necessarily altogether overwritten, but may rather merge with the supervening situation to give life to unique assemblages of spatialized feelings.

Drawing from contemporary phenomenological theories, with their specific focus on the affective and embodied dimension of lived experience, this paper describes and discusses two instances of adaptive reuse, one in Brussels, the second in Rome, highlighting their different processes and spatial outcomes.

The paper implements recent literature on spatial experience to bring to light conditions found in cases of adaptive reuse. By describing the generators of shared emotions – objects, movements, expressions, materialities, textures – it highlights how the layering of the physical world can lead to both the domestication of affects and to discrepancies and discontinuities in the fabric of experienced space.

There is only a limited literature dedicated to the description of adaptive reuse processes from the contemporary phenomenological perspective. This kind of description can clarify the dynamics unfolding between citizens and experienced space in cases of heritage reuse.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

Digital transformation (DT) harnessing the potential of emerging technology creates opportunities and challenges for organizations worldwide. Senior executives view DT as a key initiative for future competitiveness, a view shared by academic researchers. What may challenge the organization is that the vision may be present while preparedness may be lacking. Organizational preparedness depends on managers and employees charged with implementing DT and their perceptions on preparedness are often not aligned with senior executives.

In this research, the authors explore the perceptions of managers and employees on DT preparedness in an organization by gathering data from 579 participants. This study uses an innovative approach to qualitative data analysis using interactive topic modeling.

Findings in this qualitative study provide valuable insights on the perceptions of these individuals and helps understand (a) how they view DT preparedness and (b) may behave in this context. In general DT is well understood, however managers are not keen to change work processes to take advantage of the new digital tools and there appears that generational gap is a barrier to successful DT.

Senior executives play a central role communicating the DT vision necessary to inspire managers and employees. As organizations continue to invest large sums of money to explore value creation for customers and stakeholders by leveraging digital technologies, the information systems (IS) discipline can take the lead by asking the question, what can be done to improve the understanding of DT implementation in an organization?

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

The purpose of this study is to explore how companies can adopt digital humanism (DH), defined as the integration of humanistic skills in the development process of digital technologies, to create value and consequently develop effective business models. Specifically, the aim is to answer the following research question: what are the main DH mechanisms through which companies create value?

Given the limited understanding of the phenomenon under investigation, a qualitative approach was adopted based on a multiple-case study to explore how companies are embracing DH. The study will focus on three exemplar cases in the use of DH, namely, IBM, Microsoft and SAP, well recognized as three of the market leaders in the IT industry. In addition, the selected companies are recognized as some of the most innovative in their specific industries, hence offering a rich set of information on how to specifically embrace DH.

This study unveils the main mechanisms through which companies can create value by implementing DH’s approaches into their business models.

The originality of this research lies in its focus on how companies integrate DH into their business models. Indeed, the study aims at uncovering the main mechanisms that companies use to integrate DH into their overall business practices. Overall, this research provides valuable insights into how companies can effectively integrate DH into their business models, which could have important implications for creating responsible, sustainable and inclusive solutions that prioritize human needs and values.

David Maines established himself as one of the premier symbolic interactionists who did not attach himself to any particular interactionist school of thought. In creating an extensive and intensive legacy via his publications, Maines looked into issues that other interactionists, bound to particular schools of thought, either took for granted or neglected to examine. In so doing, Maines resembled Simmel's Stranger not only as one who fits into a community but also as one who remains distant from the community. One of the key areas of investigation that defined Maines' work and that separated him from other interactionists pertained to his interest in social structure and specifically, how people become structurally situated as social-structural interactors. This chapter examines, in detail, Maines' interest in getting structurally situated and uses a television series, Dopesick, to discuss the relationships between institutional actors as a way to substantiate Maines' theoretical interest in social structure and the activities involved in the process of getting structurally situated.

Engaging with customers and addressing unmet value have become increasingly challenging within multi-stakeholder environments of service innovation. Therefore, this paper aims to address this challenge by studying how strategic design abilities address unmet value in service engagement strategies.

The authors conducted a qualitative inductive study at a multinational corporation and interviewed marketing and design professionals on their innovation practices in service engagement strategies.

From the inductive analysis, this study identified three strategic design abilities that effectively contribute to addressing unmet value throughout the co-evolving process of service engagement: envisioning value, modelling value and engaging value. Based on this, this study proposes the emerging co-evolving loop framework of service engagement strategies.

The limitation of this emerging theory is a lack of broad generalizability with mutual exclusivity or collective exhaustiveness across industries. A theoretical implication of the framework is the integration of strategic design and services marketing towards co-created engagement strategies.

The service engagement loop framework can be of great value to service innovation processes, for which an integrated, cross-functional approach is often missing.

The findings further suggest that next to a methodological skillset, strategic design abilities consist of a distinct mindset.

This paper introduces strategic design abilities to address unmet value and proposes a novel co-evolving loop framework of service engagement strategies.