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The purpose of this study is to offer a straightforward, cost-effective, and feasible resolution for managers to assess their processes in a live manner using the process mining technique and to identify anomalies in cases that deviate from the standard. Consequently, the findings of this research can be utilized by organizational managers, while process mining vendors can also leverage it as a feature for their solutions.

Our two-step method is designed to initially evaluate the level of standardization within the process, followed by identifying its underlying cause. These two steps are aimed at helping managers effectively evaluate their business processes. The steps are: (1). Start-End Case Diagram: This diagram allows for the evaluation of the lead time trend and identification of cases that deviate from the standard trend line in a service-based process. (2). Happy Path Analysis: Pareto law is suggested to identify the most frequent process variants.

This approach enables organizations to easily identify problematic cases and investigate bottlenecks when deviations from the standards occur.

The novelty of the paper lies in the introduction and utilization of the start-end case diagram, as well as the combination of this diagram with the Pareto law for the identification of happy path and root cause analysis.

Using real-time support systems may help operators in road construction to improve paving and compaction operations. Nowadays, these systems transform from descriptive to prescriptive systems. Prescriptive or operator guidance systems propose operators actionable compaction strategies and guidance, based on the data collected. It is investigated how these systems mediate the perceptions and actions of operators in road pavement practice.

A case study is conducted on the specific application of an operator guidance system in a road pavement project. In this case study, comprehensive information is presented regarding the process of converting input in the form of data from cameras and sensors into useful output. The ways in which the operator guidance systems translate data into actionable guidance for operators are analyzed from the technological mediation perspective.

Operator guidance systems mediate actions of operators physically, cognitively and contextually. These different types of action mediation are related to preconditions for successful implementation and use of these systems. Coercive interventions only succeed if there is widespread agreement among the operators. Persuasive interventions are most effective when collective and individual interests align. Contextual influence relates to designs of the operator guidance systems that determine human-technology interactions when using them.

This is the first study that analyzes the functioning of an operator guidance system using the technological mediation approach. It adds a new perspective on the interaction between this system and its users in road pavement practice.

Applications of artificial intelligence (AI), such as virtual and physical service robots, generative AI, large language models and decision support systems, alter the nature of services. Most service research centers on the division between human and AI resources. Less attention has been paid to analyzing the entangled resource relations and interactions between humans and AI entities. Thus, the purpose of this paper is to extend our metatheoretical understanding of resource integration and value cocreation by analyzing different human–AI resource relations in service ecosystems.

The conceptual paper adapts a novel framework from postphenomenology, specifically cyborg intentionality. This framework is used to analyze what kinds of human–AI resource relations enable resource integration and value cocreation in service ecosystems.

We conceptualize seven different human–AI resource relations, namely background, embodiment, hermeneutic, alterity, cyborg, immersion and composite relation. The sociotechnical entangled perspective on human–AI resource relations challenges and reframes our understanding of interactions between humans and nonhumans in resource integration and value cocreation and the distinction between operant and operand resources in service research.

Our primary contribution to researchers and service providers is dissolving the distinction between operant and operand resources. We present two foundational propositions. 1. Humans and AI become entangled value cocreating resources in inherently sociotechnical service ecosystems; and 2. Human and AI entanglements in value cocreation manifest through seven resource relations in inherently sociotechnical service ecosystems. Understanding the combinatorial potential of different human–AI resource relations enables service providers to make informed choices in service ecosystems.

This paper aims to investigate the impact of corporate dividend policy on the capital structure in the Korean stock market. To distinctly discern the voluntariness of changes in corporate dividend policy, we analyze companies that, following a substantial increase, do not reduce dividends for the subsequent two years or, after a significant decrease, do not raise dividends for the following two years. Our empirical findings indicate that companies that increase dividends experience a significant decrease in both book and market leverage, even after controlling for variables such as target leverage ratios. This result suggests that a large increase in dividends can effectively reduce information asymmetry, leading to a lower cost of equity. On the contrary, after a decrease in dividends, both book leverage and market leverage significantly increase, revealing a symmetric relationship between dividend policy and capital structure. In conclusion, large dividend increases in Korean companies not only reduce information asymmetry but also lower the cost of equity capital, resulting in observable changes in the leverage ratio.