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Contemporary service and marketing research on value co-creation and value co-destruction assume a one-dimensional view on value, ranging from positive value co-creation, alignment and high value to negative value co-destruction, misalignment and low value. This limitation has recently led researchers to conceptually develop more dynamic spatial-temporal models of how value is formed during the interaction, e.g. in terms of different relationships between practice elements (procedures, understandings and engagements) both within and between actors in “value formation spaces”. However, much of this research awaits validation and is in need of more details. This study aims to address this limitation with the purpose of detailing how and why the mechanisms in such spaces are formed.

Two different and interlinked typologies were analytically derived from previous research and applied on ethnographically-inspired multi-perspective empirical data from a service combining health care and transport service ecosystems, using a combination of interviews, observations and service design methodologies. The design in combination with a practice theory perspective was used to articulate crucial aspects related to understanding the dynamics of value co-formation for elaborative and illustrative purposes.

The study contributes to service theory by conceptualizing as follows: a typology consisting of nine different configurations of practice elements (within and between such elements) and eight possible directions that value formation can take, suggesting a theory that explains value co-creation, value co-destruction and mixed cases.

Although the findings have been developed in a specific empirical context, they articulate a conceptualization applicable to many other service and marketing value co-formation settings.

The typologies are conceptual tools to be used in identifying and measuring the alignment/misalignment of practice elements in complex organizations. The empirical findings uncover service problems faced by disabled customers.

The suggested typologies can guide research and practitioners in understanding and analysing value co-formation mechanisms in complex service settings.

As corporate sustainability is systemic, it cannot be achieved without effective involvement of suppliers. This study aims to examine the drivers of supplier companies’ resource allocation to a sustainability issue that affects customer companies and society at large.

Supplier companies’ resource allocation for a sustainability issue is explained from variables at the levels of the institutional, supply chain and internal environments of a supplier company. The framework is tested with a moderated regression model on 102 supplier companies in animal-based supply chains, focussing on their resource allocation for farm animal welfare.

The findings show that supply chain factors have the strongest influence on suppliers’ resource allocation, including a strong effect of investment specificity and a U-shaped effect of chain integration. Also, significant effects from institutional variables, namely, the pressure on consumer companies, and an inverted U-shaped effect of sustainability competition are found. The innovativeness, referring to the internal environment of supplier companies, appears as another important factor for the allocation of resources to animal welfare, as a sustainability issue.

The results have implications for consumer market companies to deal with sustainability issues that require involvement of their suppliers, for supplier companies to increase their competitive positions and strengthen their relationships within the supply chain, and for policymakers seeking solutions for sustainability issues in the market domain.

While existing literature focusses mostly on the corporate sustainability of highly visible and large consumer companies, to the best of the authors’ knowledge, this study is the first to examine the drivers of supplier companies’ resource allocation for a sustainability issue, namely, animal welfare. It provides insights on what drives supplier companies, usually operating outside the spotlight, to become part of a sustainability transition.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

With the ongoing catastrophe of the COVID-19 pandemic, the world seems to have come to a standstill. Daily living routines, work, and schools predominantly launched into a state of confusion, and people across the globe excessively find ways to cope with their experiences of this traumatic disaster. Concerning schools, the pandemic has dramatically challenged the education system with teaching and learning processes managed remotely, utilising online platforms. This paper explores university students’ perception of online learning, specifically during the outbreak of the COVID-19 pandemic, to uncover how they respond to this ‘new normal’ digital mode of teaching and learning and how they seek to regain control over the sudden shift in their lives. Through interviews with students from Universiti Darussalam (UBD) and Universiti Islam Sultan Sharif Ali UNISSA, this original article reveals students’ transformative behaviours during online learning, and institutional supports that contribute to the shaping of students' online learning experiences in Brunei.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

The purpose of this study is to analyze and forecast the financial sustainability and resilience of commercial banks of Bangladesh in response to the negative effects of COVID-19 pandemic.

Eighteen publicly listed commercial banks of Dhaka Stock Exchange (DSE) have been taken as a sample for this study. To measure the riskiness of banks' credit portfolio, nine industries of DSE have been considered to determine probable loss of revenue arising from the COVID-19 pandemic shock. Moreover, two commonly used multiple-criteria-decision-making (MCDM) tools namely TOPSIS method and HELLWIG method have been used for analyzing the data.

Based on the performance scores under TOPSIS and HELLWIG method, banks are categorized into three groups (six banks each) namely top resilient, moderate resilient and low resilient. It is found that EBL and DBBL are the most resilient banks, and ONEBANK is the worst resilient bank in Bangladesh in managing the COVID-19 pandemic shock.

This study concludes that banks with low capital adequacy, low liquidity ratio, low performance and higher NPLs are more vulnerable to the shocks caused by the COVID-19 pandemic. The management of commercial banks should emphasize on maintaining higher capital base and reducing default loans.

Resilience of the Bangladeshi banking sector under any adverse economic event has been examined by only using stress testing approach. This study is empirical evidence where both TOPSIS and HELLWIG MCDM methods have been used to make the result conclusive.

The evolution of technologies has unleashed a wealth of challenges by generating massive amount of data. Recently, biological data has increased exponentially, which has introduced several computational challenges. DNA short read alignment is an important problem in bioinformatics. The exponential growth in the number of short reads has increased the need for an ideal platform to accelerate the alignment process. Apache Spark is a cluster-computing framework that involves data parallelism and fault tolerance. In this article, we proposed a Spark-based algorithm to accelerate DNA short reads alignment problem, and it is called Spark-DNAligning. Spark-DNAligning exploits Apache Spark ’s performance optimizations such as broadcast variable, join after partitioning, caching, and in-memory computations. Spark-DNAligning is evaluated in term of performance by comparing it with SparkBWA tool and a MapReduce based algorithm called CloudBurst. All the experiments are conducted on Amazon Web Services (AWS). Results demonstrate that Spark-DNAligning outperforms both tools by providing a speedup in the range of 101–702 in aligning gigabytes of short reads to the human genome. Empirical evaluation reveals that Apache Spark offers promising solutions to DNA short reads alignment problem.

Manufacturing small and medium-sized enterprises (SMEs) have already noticed the tangible benefits offered by artificial intelligence (AI). Several approaches have been proposed with a view to support them in the processes entailed in this innovation path. These include multisided platforms created to enable the connection between SMEs and AI developers, making it easier for them to network each other. While such platforms are complex, they facilitate simultaneous interaction with several stakeholders and reaching out to new potential users (both SMEs and AI developers), through a collaboration with supporting ecosystems such as digital innovation hubs (DIHs).

Mixed methods were used. The literature review was performed to identify the existing approaches within and outside the manufacturing domain. Computer-assisted telephonic (in-depth) interviewing , was conducted to include perspectives of AI platform stakeholders and collect primary data from various European countries.

Several challenges and barriers for AI platform stakeholders were identified alongside the corresponding best practices and guidelines on how to address them.

An effective approach was proposed to provide support to the industrial platform managers in this field, by developing guidelines and best practices on how a platform should build its services to support the ecosystem.

The aims of the paper were to highlight the dearth of applied practitioner research concerning the expression of neurodiversity at work and develop an epistemological framework for a future research agenda.

A systematic empty review protocol was employed, with three a priori research questions, inquiring as to the extent of neurodiversity research within mainstream work psychology, psychology in general and lastly within cross-disciplinary academic research. The results of the final search were quality checked and categorized to illustrate where studies relevant to practice are currently located.

The academic literature was found to be lacking in contextualized, practical advice for employers or employees. The location and foci of extracted studies highlighted a growing science-practitioner gap.

The research focused on common neurominority conditions such as autism and dyslexia; it is acknowledged that the neurodiversity definition itself is broader and more anthropological in nature. A need for a comprehensive research agenda is articulated, and research questions and frameworks are proposed.

Guidance is given on applying disability accommodation to both individual and organizational targets.

The disability employment gap is unchanged since legislation was introduced. The neurodiversity concept is no longer new, and it is time for multi-disciplinary collaborations across science and practice to address the questions raised in this paper.

This paper offers an original analysis of the neurodiversity paradox, combining systematic inquiry with a narrative synthesis of the extant literature. The conceptual clarification offers clear directions for researchers and practitioners.