Search results

The purpose of this study is to identify the key data categories and characteristics defined by asset information requirements (AIR) and how this affects the development and maintenance of an asset information model (AIM) for a blockchain-based digital twin (DT).

A mixed-method approach involving qualitative and quantitative analysis was used to gather empirical data through semistructured interviews and a digital questionnaire survey with an emphasis on AIR for blockchain-based DTs from a data-driven predictive analytics perspective.

Based on the analysis of results three key data categories were identified, core data, static operation and maintenance (OM) data, and dynamic OM data, along with the data characteristics required to perform data-driven predictive analytics through artificial intelligence (AI) in a blockchain-based DT platform. The findings also include how the creation and maintenance of an AIM is affected in this context.

The key data categories and characteristics specified through AIR to support predictive data-driven analytics through AI in a blockchain-based DT will contribute to the development and maintenance of an AIM.

The research explores the process of defining, delivering and maintaining the AIM and the potential use of blockchain technology (BCT) as a facilitator for data trust, integrity and security.

This paper aims to explore drivers of the effectiveness of risk assessments in risk workshops.

This study uses an agent-based model to simulate risk assessments in risk workshops. Combining the notions of transactive memory and the ideal speech situation, this study establishes a risk assessment benchmark and then investigates real-world deviations from this benchmark. Specifically, this study models limits to information transfer, incomplete discussions and potentially detrimental group characteristics, as well as interaction patterns.

First, limits to information transfer among workshop participants can prevent a correct consensus. Second, increasing the required number of stable discussion rounds before an assessment improves the correct assessment of high but not low likelihood risks. Third, while theoretically advantageous group characteristics are associated with the highest assessment correctness for all risks, theoretically detrimental group characteristics are associated with the highest assessment correctness for high likelihood risks. Fourth, prioritizing participants who are particularly concerned about the risk leads to the highest level of correctness.

This study shows that by increasing the duration of simulated risk workshops, the assessments change – as a rule – from underestimating to overestimating risks, unraveling a trade-off for risk workshop facilitators. Methodologically, this approach overcomes limitations of prior research, specifically the lack of an assessment and process benchmark, the inability to disentangle multiple effects and the difficulty of capturing individual cognitive processes.

The study covers two different forms of financial support for households, income support for single parents and reimbursements for depression medicines, and explores their relationships with the demand for child protection services.

The data were retrieved from the Sotkanet, the Finnish Indicator Bank, and included 292 Finnish municipalities. It was hypothesised that the effect of income support for single-parent households on the need for child protection is mediated by reimbursements for depression medicines. The hypotheses were tested by using a conditional process analysis program, PROCESS (Model 4).

It was found that income support reduces the proportion of reimbursements for depression medicines in a municipality, which in turn reduces the need for child protection services. At the level of social policy, the study tentatively suggests that the social welfare system may affect the demand for child protection by investing in income support for single-parent households.

The choice of variables does not fully explain the effect of the mechanism. The relationships that are found in this study can have hidden factors which affect them. Further, the data have only 292 cases, which is quite a small sample, and is limited to Finland.

The study suggests that the social welfare system may affect the demand for child protection by investing in income support for single-parent households.

The purpose of this paper is to investigate how digital transformation changes highly traditional business processes and how it impacts value co-creation and co-destruction. More specifically, the aim is to examine, using the resource interaction approach, how the friction between non-digital and digital resources affects the co-creation and co-destruction of value in a network during digital transformation. Based on this, the authors provide managerial implications on how to handle simultaneous digital and traditional business processes to co-create value during digital transformation.

A case study is conducted of a digital platform provider and of three traditional confectioneries. In this analysis, the authors looked at the business processes of highly traditional confectioneries that have introduced online services through a digital platform and are undergoing digital transformation.

In some industries, it is neither possible nor advisable to fully digitalise all business processes, and companies have to partially retain their traditional, analogue character to create value. The process of value co-creation during digital transformation is affected by friction between the digital and non-digital resources and is mitigated by specific lubricants (e.g. mutual reliance, smooth personal communication, willingness to help, attitude towards change). This results in the improvement of processes and capabilities in terms of digital development and traditional production. Friction may also lead to value co-destruction, for example, as the result of transformation from face-to-face to digital interactions.

The authors contribute to research on the digital transformation of highly traditional companies that need to introduce new, digital technologies and resources while continuing their traditional processes. The authors develop the concept of lubricants that mitigate the friction between resources and, therefore, facilitate value co-creation in a business network. Additionally, the authors provide managerial implications for how to handle simultaneous digital and traditional business processes during digital transformation.

This study examines what is the significance of the features of inter-organizational relationships in consecutive phases of the relationship life cycle.

Qu antitative, large-scale surveying was run on 786 software developers operating in Poland. The research hypothesis regarding the systematic increase of relational features (i.e. commitment, communication, (lack of) conflict, cooperation, intensity, investments, longevity, multidimensionality of bonds, trust, and velocity) across the particular relationship life cycle phase le (i.e. initial, development, maintenance, dormant/end, and reactivation) was verified using ANOVA and post-hoc tests.

The results show that the majority of considered features of inter-organizational relationships non-significantly but progressively strengthen from the initial phase, through the development phase, to the maintenance phase, then significantly weaken in the dormant/end phase and strengthen again in the reactivation phase. Interestingly, velocity–as the only examined feature–significantly increases in dormant/end and then decreases if the relationship is reactivated.

Prior studies were focusing on single feature, this one offers a holistic view considering ten relational facets. Moreover, this is one of the few research studies exploring the changes of relational features adopting the life cycle perspective.