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This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Dynamic lot sizing is the problem of determining the quantity and timing of ordering items while satisfying the demand over a finite planning horizon. This paper considers the problem with two practical considerations: minimum order size and lost sales. The minimum order size is the minimum amount of items that should be purchased and lost sales involve situations in which sales are lost because items are not on hand or when it becomes more economical to lose the sale rather than making the sale. The objective is to minimize the costs of ordering, item , inventory holding and lost sale over the planning horizon. To solve the problem, we suggest a heuristic algorithm by considering trade-offs between cost factors. Computational experiments on randomly generated test instances show that the algorithm quickly obtains near-optimal solutions.

The advancement of a profession depends on factors such as firm education, training programmes and vibrant professional associations. The purpose of this study is to provide baseline information on archives and records management (ARM) professionals from Africa, including their views on education and training programmes. Related studies have been limited to investigating the experiences of graduates from a few institutions within a limited jurisdiction. Similar studies have not been conducted by African-based ARM associations.

This study’s survey approach was administered electronically. Two surveys explored themes through 10 research questions in three categories: background information; characteristics of the education; and types of courses and extent covered during the study.

The two surveys had 107 and 179 respondents, respectively, domiciled in 19 African countries. In both studies, about 75% of the respondents had an undergraduate- or master-level qualification. Between 69% and 87% of the respondents from the first and second surveys, respectively, had completed their educational programmes within their home countries. There was varied sentiment on the types of courses and level of coverage within their studies. Legal and technology aspects were less covered in the curriculum than core subjects.

To the best of the author’s knowledge, this study is the first with findings from ARM respondents across several African countries. It provides insight into the demographics and educational programmes. This study’s unique contribution provides baseline data for more detailed and nuanced studies that will explore data and investigate patterns within and among individual countries on the continent.

The purpose of this paper is to test an implication of the pecking order theory to explain capital structure decisions among Chinese listed companies during the 2005-2007 NTS Reform transition period.

The authors utilize direct proxies for information asymmetry based on microstructure models including Probability of the arrival of informed trades (PIN), Adverse selection component of the bid-ask spread (λ), Illiquidity ratio (ILLIQ) and liquidity ratio, and Information asymmetry index (InfoAsy) to examine their relation with firms’ debt financing.

Consistent with the prediction of Pecking Order Theory, the authors find that companies for which stock investors are challenged with more severe informational disadvantages are associated with higher degree of leverage use.

The study provides a more direct test on the positive relation between information asymmetry and financial leverage of Chinese firms. In contrast to previous findings by Chen (2004), the results suggest that capital structure choices among Chinese firms progressively conform to conventional finance theories (e.g. Pecking Order Theory) with the decline of non-tradable shares.

The purpose of this paper is to examine the association between consumers’ emotions towards emerging e-banking technology, perceived risk and subsequent intention to adopt emerging e-banking technology.

An online questionnaire was used to collect data, which were analysed in a quantitative study. The final sample of 224 educated young consumers, familiar with emerging e-banking technology, allowed testing of the research hypotheses by applying confirmatory factor analysis and structural equation modelling (SEM).

The empirical results indicate that deterrence emotions and hedonic motivation are associated with consumers’ perceived risk and, subsequently, their intention to adopt emerging e-banking technology. Additionally, analysing the moderating role of hedonic motivation in the association between consumers’ deterrence emotions towards emerging e-banking technology and their perceived risk highlights the significant association of deterrence emotions with perceived risk, regardless of the presence of hedonic motivation.

This study demonstrates the association between consumers’ emotions, perceived risk and subsequent intention to adopt emerging e-banking technology whilst underscoring the importance of distinguishing between different types of emotions and their corresponding appraisals.

Drawing on the literature on professional ignorance, here defined in affirmative terms as the capacity to act regardless of the incompleteness of available information in organizations and professional communities, the article reports empirical material from an urban development project wherein policy makers' instructions are vague and, in certain domains, inconsistent with market conditions.

Urban development projects regularly include uncertainty and risk taking, and policy makers' stated objectives regarding project goals may be incomplete or merely signal a political ambition. In such situations, first-line project participants need to make decisions as if uncertainties regarding policy objectives are manageable and preferably minimal. The purpose of the article is to substantiate the proposition that professional ignorance is a key mechanism in incomplete or imperfect governance systems.

Project participants actively questioned policy but acted on the instructions just the same, which is indicative of how professional ignorance is supportive of governance system that relies on first-line market actors and agencies to implement also incomplete or vaguely stated policy objectives. Incomplete policies derive from challenges in political deliberation and bargaining processes, uncertainty regarding the future and shifting preferences among policy makers and constituencies more widely. In practice, incomplete policies regularly include issues for first-level actors (e.g. on the urban development project level) to handle and to reconcile in their day-to-day work.

On basis of an empirical study of a major urban development project, the study contributes to a growing literature that recognizes the value of professional ignorance in governance systems and in project management practice. The study invites further scholarly research that takes an affirmative of professional ignorance but without overlooking its risks and potential malfunctions.

Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers.

The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes.

Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given.

Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations.

The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.

Prior research has reported the indirect implications of firm's dynamic capabilities on their competitive firm performance. Our attention now turns to open innovation since it has been confirmed to be an influential factor contributing to the superior performance of technological firms. So far there has been little research on assessing the relationship between a firm's dynamic capabilities as an antecedent of the competitive performance of the firm or investigations into the mediating role of open innovation in this relationship.

Drawing on the theory of dynamic capabilities, we developed a framework as a way to better understand the role of open innovation, which could then help to better explain the relationship between firms' dynamics capabilities and their competitive firm performance. Based on the empirical data of 465 firms operating in innovative and non-innovative industries, we employed structural equation modelling (SEM) to examine the research hypotheses and the path relationships in the proposed model.

The SEM analysis revealed that a firm's dynamic capabilities significantly impact its open innovation performance and that open innovation, consequently, impacts the competitive performance of the firm. Moreover, the results show that the path between dynamic capabilities and competitive firm performance is partially mediated through open innovation.

The findings provide practical implications and draw managerial attention to the importance of: (1) investing in innovation, (2) engaging customers in the innovation process and (3) maintaining innovation management excellence as significant antecedent factors in increasing competitive firm performance.

Considering the lack of empirical research in the literature on the links between dynamic capabilities and open innovation, this paper contributes to the dynamic capabilities and open innovation literature by confirming that open innovation not only mediates the relationship between these two aspects but also strengthens the effect the dynamic capabilities have on competitive firm performance. Besides, due to the significant impact of dynamic capabilities on open innovation, dynamic capabilities might be regarded as an antecedent of open innovation.

This paper aims to explore how tensions and alignments between different actors’ needs in a transformative services network affect balanced centricity, which is an indicator of well-being. Balanced centricity describes a situation in which all network actors’ interests and needs are fulfilled simultaneously. In such cases, all actors are better off, which increases both individual actors’ and overall actor-network well-being.

The empirical study takes place in nursing homes in which in-bed baths represent co-created service encounters that affect the well-being of focal actors (i.e. patients), frontline service employees (i.e. nurses) and transformative service mediators (i.e. family members), who have potentially competing needs. Using a qualitative, phenomenological approach, the study inductively explores and deductively categorizes actors’ personal experiences to gain deep, holistic insights into the service network and its complex web of actor interdependencies.

The resulting conceptual model of balanced centricity identifies actors’ lower-order needs as different manifestations of the psychological needs for autonomy, competence and relatedness. If actors’ needs are aligned, their psychological needs can be satisfied, which facilitates balanced centricity. If actors exhibit competing needs though, balanced centricity is impeded.

This study establishes actors’ psychological needs as the origin of tensions/alignments in multi-actor networks that impede/contribute to balanced centricity. Transformative service providers should try to address all actors’ psychological needs when co-creating services to achieve network well-being.

This study adopts a novel, multi-actor perspective and thereby presents a conceptual model that contributes to the understanding of balanced centricity. Future research could test this model in other transformative service settings.

This paper covers the development of a multidimensional contract administration performance model (CAPM) for construction projects. The proposed CAPM is intended to be used by the industry stakeholders to measure the construction contract administration (CCA) performance and identify the strengths and weaknesses of the CCA system for running or completed projects.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. In the first phase, contract administration indicators were collected from relevant literature. In the second phase, an online questionnaire was prepared, and data were collected and analyzed using the crisp value of fuzzy membership function, and structural equation modeling (SEM). The fuzzy set was chosen for this study due to the presence of uncertainty and fuzziness associated with the importance of several key indicators affecting the CCA performance. Finally, SEM was used to test and analyze interrelationships among constructs of CCA performance.

The data collected from 336 construction professionals worldwide through an online survey was utilized to develop the fuzzy structural equation model. The goodness-of-fit and reliability tests validated the model. The study concluded a significant correlation between CCA performance, CCA operational indicators, and the process groups.

The contribution of this paper to the existing knowledge is the development of a fuzzy structural equation model that serves as a measurement tool for the contract administration performance. This is the first quantitative structural equation model to capture contract administration performance. The model consists of 93 Construction Contract Administration(CCA) performance indicators categorized into 11 project management process groups namely: project governance and start-up; team management; communication and relationship management; quality and acceptance management; performance monitoring and reporting management; document and record management; financial management; changes and control management; claims and dispute resolution management; contract risk management and contract closeout management.