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The purpose of this paper is to investigate the role of “soft information” in firms’ debt issue and capital structure choices, and present a new model reconciling the gap between theories of capital structure and empirical findings.

The paper develops an analytical model of debt issues under asymmetric information in a setting where, in addition to observing the amount of debt the firm issues, outside investors obtain “soft information” signals through their own information production or noisy voluntary disclosures made by the firm. This paper analyzes the benefit and cost for the firm's debt issue decisions in this setting, and specifies the effect of soft information on these decisions.

If sufficiently precise soft information is available to outside investors, the firm's debt issue behavior is significantly altered relative to that in existing models. In particular, an inverted‐U shape relationship is found between the intrinsic value of the firm and the amount of debt it issues. Moreover, there is a negative relationship between the amount of debt the firm issues and the precision of soft information. Further, it is found that firms about which outside investors receive more favorable soft information issue less debt.

The model predicts an inverted‐U shape relationship between firms’ debt ratios and operating performance. It also predicts that firms about which outside investors receive more favorable or more precise soft information have lower debt ratios on average. A rationale is provided for the existence of firms’ investor relations departments.

Firms’ capital structure choices remain a topic of significant importance. This paper incorporates the soft informations into firms’ debt issue decisions and proposes a new model of capital structure that generates insights into firms’ financing decisions and disclosure decisions, as well as information production by outside investors.

Business process workflow is a design conceptualization to automate the sequence of activities to achieve a business goal with involved participants and a predefined set of rules. Regarding this, a formal business workflow model is a prime requisite to implement a consistent and rigorous business process. In this context, majority of the existing research works are formalized structural features and have not focused on functional and behavioral design aspects of business processes. To address this problem, this paper aims to propose a formal model of business process workflow called as business process workflow using typed attributed graph (BPWATG) enriched with structural, functional and behavioral characteristics of business processes.

Typed attributed graph (ATG) and first-order logic have been used to formalize proposed BPWATG to provide rigorous syntax and semantics towards business process workflows. This is an effort to execute a business workflow on an automated machine. Further, the proposed BPWATG is illustrated using a case study to show the expressiveness of proposed model. Besides, the proposed graph is initially validated using generic modelling environment (GME) case tool. Moreover, a comparative study is performed with existing formal approaches based on several crucial features to exhibit the effectiveness of proposed BPWATG.

The proposed model is capable of facilitating structural, functional and behavioral aspects of business process workflows using several crucial features such as dependency conceptualization, timer concepts, exception handling and deadlock detection. These features are used to handle real-world problems and ensure the consistency and correctness of business workflows.

BPWATG is proposed to formalize a business workflow that is required to make a model of business process machine-readable. Besides, formalizations of dependency conceptualization, exception handling, deadlock detection and time-out concepts are specified. Moreover, several non-functional properties (reusability, scalability, flexibility, dynamicity, reliability and robustness) are supported by the proposed model.

This paper aims to investigate the relationships between the strategic type of cross‐border mergers and acquisitions (M&As) and enterprise growth, and the moderating role of Chinese companies' organizational factors, including organizational structure, organizational experience, cultural distance and relative size.

The authors, as researchers, have mixed the strategic choice and post‐M&As integration into a comprehensive framework while building the theoretical model of “Strategic Type of Cross‐border M&As‐Organizational Factors‐Enterprise Growth” (ST‐OF‐EG). In this paper, they have empirically examined the model using hierarchical regression by analyzing 76 cross‐borders M&A events of overseas‐listed Chinese companies over the 2000‐2007 period.

The analysis shows that: related cross‐border M&As are better for enterprise growth than unrelated diversification cross‐border M&As; and among the organizational factors, studied organizational structure and organizational experience show a positive significance in terms of the relationship between the strategic type of cross‐border M&As and enterprise growth. The moderating role of cultural distance and relative size is non‐significant.

In this paper, the moderating effect of cultural distance was found to be insignificant. However, further research is encouraged.

Chinese companies should pay attention to strategic choices before cross‐border M&As. They should expand abroad to markets step by step. They should merge the companies that have the higher relevance on a product, industry or market first. On the basis of specialization, Chinese companies should make themselves stronger and then develop from specialization to proper diversification, which is a robust path to achieve enterprise growth. Besides, Chinese companies should accumulate international experience as soon as possible and organizational structure should match the strategic choice.

This paper would be immensely helpful to Chinese companies to plan their cross‐border M&As strategy in a way that would enhance growth and core‐competence.

Proper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.

The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.

The performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.

The proposed methodology can be applied to find the best response plan for all crises.

In this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

Data from sophisticated portable heart‐rate monitors and “work diaries” were used to relate in Mintzberg's “nature of managerial work” to physiological stress in a small sample of working principals. Subjects were categorised by years of experience, Type A and Type B personality, and were “shadowed” for three complete work days in their schools doing regular activities to learn what management functions were stressful. Principals were found to be working under extreme stress (a few at catastrophically high levels), for long hours, and that certain managerial activities were more physiologically stressful than others. Implications for training, deployment and the use of bio‐feedback techniques are discussed.

In this study, the authors unpack the micro-level processes of knowledge accumulation (experiential learning) and knowledge application (problem solving) to examine how task allocation structures influence organizational learning. The authors draw on untapped potential of the classical garbage can model (GCM), and extend it to analyze how restrictions on project participation influence differentiation and integration of organizational members’ knowledge and consequently organizational efficiency in solving the diverse, changing problems from an uncertain task environment. To isolate the effects of problem or knowledge diversity and experiential learning, the authors designed three simulation experiments to identify the most efficient task allocation structure in conditions of (1) knowledge homogeneity, (2) knowledge heterogeneity, and (3) experiential learning. The authors find that free project participation is superior when the members’ knowledge and the problems they solve are homogenous. When problems and knowledge are heterogeneous, the design requirement is on matching specialists to problem types. Finally, the authors found that experiential learning creates a dynamic problem where the double duty of adapting the members’ specialization and matching the specialists to problem types is best solved by a hierarchic structure (if problems are challenging). Underlying the efficiency of the hierarchical structure is an adaptive role of specialized members in organizational learning and problem solving: their narrow but deep knowledge helps the organization to adapt the knowledge of its members while efficiently dealing with the problems at hand. This happens because highly specialized members reduce the necessary scope of knowledge and learning for other members during a certain period of time. And this makes it easier for the generalists and for the organization as a whole, to adapt to unforeseen shifts in knowledge demand because they need to learn less. From this nuanced perspective, differentiation and integration may have a complementary, rather than contradictory, relation under environmental uncertainty and problem diversity.