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Process redesign refers to the intentional change of business processes. While process redesign methods provide structure to redesign projects, they provide limited support during the actual creation of to-be processes. More specifically, existing approaches hardly develop an ontological perspective on what can be changed from a process design point of view, and they provide limited procedural guidance on how to derive possible process design alternatives. This paper aims to provide structured guidance during the to-be process creation.

Using design space exploration as a theoretical lens, the authors develop a conceptual model of the design space for business processes, which facilitates the systematic exploration of design alternatives along different dimensions. The authors utilized an established method for taxonomy development for constructing the conceptual model. First, the authors derived design dimensions for business processes and underlying characteristics through a literature review. Second, the authors conducted semi-structured interviews with professional process experts. Third, the authors evaluated their artifact through three real-world applications.

The authors identified 19 business process design dimensions that are grouped into different layers and specified by underlying characteristics. Guiding questions and illustrative real-world examples help to deploy these design dimensions in practice. Taken together, the design dimensions form the “Business Process Design Space” (BPD-Space).

Practitioners can use the BPD-Space to explore, question and rethink business processes in various respects.

The BPD-Space complements existing approaches by explicating process design dimensions. It abstracts from specific process flows and representations of processes and supports an unconstrained exploration of various alternative process designs.

Sustainability is regarded as a core value that the coworking movement aspires to. However, most sustainability efforts focus on the providers’ perspective while neglecting the coworking members’ role. Therefore, this paper aims to explore sustainable coworking from the members perspective by focusing on sustainable behaviors.

This study uses a flexible pattern matching approach. Theoretical patterns are identified using literature on coworking space and sustainable behavior while matching them with the empirical data. Data were collected from three different coworking spaces in Sweden through interviews and observations.

Based on the theoretical patterns, three constructs for sustainable coworking were identified, namely, productive behavior, prosocial behavior and responsible space sharing behavior. Through the empirical data, the constructs were further concretized to understand their different aspects. The findings uncovered a new layer of complexity where members can show the same behavior and be perceived differently.

This study offers a more holistic understanding of sustainable coworking by highlighting the members’ role and identifying different member perceptions on sustainable coworking behaviors.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Analysis of log message is very important for the identification of a suspicious system and network activity. This analysis requires the correct extraction of variable entities. The variable entities are extracted by comparing the logs messages against the log patterns. Each of these log patterns can be represented in the form of a log signature. In this paper, we present a hybrid approach for log signature extraction. The approach consists of two modules. The first module identifies log patterns by generating log clusters. The second module uses Named Entity Recognition (NER) to extract signatures by using the extracted log clusters. Experiments were performed on event logs from Windows Operating System, Exchange and Unix and validation of the result was done by comparing the signatures and the variable entities against the standard log documentation. The outcome of the experiments was that extracted signatures were ready to be used with a high degree of accuracy.

This paper explores different approaches to regulating corporate social responsibility (CSR) patterns of adopting codes of conduct, and discusses the approach that courts should embrace.

Case studies from various legal systems will be examined. The paper presents new typology relating to different patterns of the Corporate Social Performance (CSP) model, based on aspects of the CSR pyramid, namely, legislative CSR and ethical CSR. Legislative CSR includes adoption of thin codes which reflect compliance within current legal standards of the criminal code, while ethical CSR includes codes reflecting ethical norms and corporate social citizenship beyond mere compliance. The paper also includes the interplay of different patterns of CSR and three approaches to regulation regarding these patterns.

Both the Israeli negative CSR regulatory approach and the American legislative CSR regulatory approach present difficulties.

The paper introduces a theory for regulating CSR within criminal law, drawing on the pyramid of CSR. It presents an original discussion of distinct approaches to regulation of corporate liability, while further developing the institutional theory of CSR and the interplay of regulation and CSR. The paper suggests a novel solution regarding the regulation and acceptance of CSR: the granting of protection from criminal liability to corporations who adopt CSR.

This study investigates the sustainability models of non-profit and hybrid organizations, which aim to balance economic, social and environmental objectives. The research introduces the Sustainability Model Canvas to analyze these organizations and identify common patterns, unique characteristics and managerial insights to balance the triple bottom line.

The research utilizes the Sustainability Model Canvas to examine the sustainability models of 200 non-profit and hybrid organizations. Data were collected from secondary sources, including articles, reports and websites. The analysis was conducted using the activity system theoretical framework, which helped to identify design elements and themes within the business models of the studied organizations.

The study reveals four primary sustainability model patterns: donated income, earned income, public income and auto-generated income. An additional mixed approach pattern is identified, combining elements from the four primary patterns. The research highlights the parallels between these sustainability models and multi-sided platform business models, offering managerial suggestions for leveraging these patterns to achieve sustainability.

The study is based on secondary data, which may limit the depth of insights compared to primary data collection. At the same time, the chance to consider hybrid organization through multi-sided platform lenses provides relevant contributions to both the literature streams.

The identified sustainability model patterns and managerial suggestions can serve as blueprints for non-profit and hybrid organizations aiming to design or innovate their sustainability models. The Sustainability Model Canvas offers a practical tool for organizations to visualize and balance their triple bottom line objectives.

The research underscores the importance of integrating social and environmental considerations into business models, promoting a holistic approach to sustainability that can lead to broader social and environmental benefits.

This research contributes to the business model literature by extending the focus beyond traditional profit-oriented organizations to include non-profit and hybrid organizations. The introduction of the Sustainability Model Canvas provides a new tool for designing and analyzing sustainability-oriented business models. The study also suggests considering sustainability models as multi-sided platforms, offering new insights for both academic and practical applications.

The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability.

Pattern structures allow one to approach the knowledge extraction problem in case of partially ordered descriptions. They provide a way to apply techniques based on closed descriptions to non-binary data. To provide scalability of the approach, the author introduced a lazy (query-based) classification algorithm.

The experiments support the hypothesis that closure-based classification and regression allow one to both achieve higher accuracy in scoring models as compared to results obtained with classical banking models and retain interpretability of model results, whereas black-box methods grant better accuracy for the cost of losing interpretability.

This is an original research showing the advantage of closure-based classification and regression models in the banking sphere.

The study explores the extent to which business analytics can address business problems using the task-technology fit theory.

The qualitative research approach of pattern matching was adopted for data analysis and 12 semi-structured interviews were conducted. Four propositions derived from the literature on task-technology fit are compared to emerging core themes from the empirical data.

The study establishes the relationships between various forms of fit, arguing that the iterative application of business analytics improves problem understanding and solutions, and contends that both under-fit and over-fit can be acceptable due to the increasing costs of achieving ideal fit and potential unaffected outcomes, respectively. The study demonstrates that managers should appreciate that there may be a distinction between those who create business analytics solutions and those who apply business analytics solutions to solve problems.

Extant studies on business analytics have not focused on how the match between business analytics and tasks affects the level to which problems can be addressed that determines business value. This study enriches the literature on business analytics by linking business analytics and business value through problem resolution demonstrated by task-technology fit. To the authors’ knowledge, this study might be the first to apply pattern matching to study the fit between technology and tasks.

To plan the future university campus, campus executives need decision-making support from theory and practice. Matching the static campus (supply) with the dynamic (demand) - while safeguarding spatial quality and sustainability - requires management information from similar organizations. This study presents an evidence-based briefing approach to support decision-makers of individual universities with management information when making decisions for their future campus.

For the proposed evidence-based briefing approach, the continuous Designing an Accommodation Strategy (DAS) framework is used in a mixed-method research design to evaluate the past to plan for the future. Five campus themes and three campus models (solid, liquid, and gas) are introduced to describe the development and diversification of university campuses and their impact across different university building types. Based on this theoretical framework, first, qualitative interview data are analyzed to understand which standards campus managers expect; second, a quantitative project database is used to demonstrate what is actually realized.

The findings demonstrate that remote working and online education will become more common. Academic workplaces and learning environments are more adaptive to changes than laboratory spaces. The analyses reveal different effective space use strategies to meet the current demand: they include space-efficient mixed-use buildings, and mono-functional generic educational and office spaces. These results show that operationalized evidence-based briefing can help design the future campus.

The study adds knowledge during a critical (post-COVID) period when decision-makers need evidence from others to adapt their campus management strategies to hybrid and sustainable ambitions.

The growing frequency of cybersecurity incidents commonly requires organizations to notify customers of ongoing events. However, the content contained within these notifications varies widely, including differences in the level of detail, apportioning of blame, compensation and corrective action. This study seeks to identify patterns contained within cybersecurity incident notifications by constructing a typology of organizational responses.

Based on a detailed review of 1,073 global cybersecurity incidents occurring during 2020, the authors obtained and qualitatively analyzed 451 customer notifications.

The results reveal three distinct organizational response types associated with the level of detail contained within the notification (full transparency, guarded and opacity), as well as three response types associated with the benefitting party (customer interest, balanced interest and company interest).

This work extends past classifications of cybersecurity incident notifications and provides a template of possible notification approaches that could be adopted by organizations.