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The purpose of this study is to provide empirical evidence on a possible economic explanation for changes in borrowing costs of US private firms that are going public.

Using an OLS regression with firm fixed effects and the IPO as an information releasing event that alters information asymmetries between borrowers and lenders and relying on several proxies for pre-IPO information asymmetries, I analyze the impact of the IPO on changes in borrowing costs from before to right after an IPO of firms with high pre-IPO information asymmetries.

My findings indicate that small firms, firms with high R&D, firms with negative EBITDA and firms with a single lending relationship benefit more from going public by realizing larger decreases in borrowing costs after an IPO than firms with lower pre-IPO information asymmetries. The results are consistent with changing information asymmetries caused by the IPO event playing a role in changes in borrowing costs after the IPO. Furthermore, I provide empirical evidence that a reduction in the lender’s bargaining power due to the IPO event plays an important role in explaining changes in borrowing costs around that time.

This study uses a hand-collected data set of loans obtained from financial statements issued by US firms at the time of their IPO. As a result, I am able to comprehensively document changes of borrowing costs of US private firms going public and shed light on one of the economic forces behind those changes.

The rapid advancement of artificial intelligence (AI), particularly within the last decade and the application of ‘deep learning’, has simultaneously accelerated human fears of the changes AI provokes in human behaviour. The question is not any more if the new phenomena, like artificially-induced consciousness, empathy or creation, will be widely used, but whether they will be used in ethically acceptable ways and for ethically acceptable purposes.

Departing from a diagnosis of the state humans have brought themselves to by (ab)use of technology, the present chapter investigates the possibility of a systematic study of adaptations human society will have to consider in order to guarantee the obeyance to the fundamental ethical values and thus its spiritual survival. To that end, a new discipline – epharmology (from the Greek epharmozein = to adapt) is proposed, together with its aims and methodology.

This paper aims to provide an overview of the application of blockchain to agri-food supply chains, including key issues and trends. It examines the state of the art and conceptual structure of the field and proposes an agenda to guide future research.

This article performs a bibliometric analysis using VOSviewer software on a sample of 205 articles from the WoS database to identify research trend topics.

The number of publications in this area has increased since 2020, which shows a growing research interest. The research hotspots are related to the integration of blockchain technology in the agri-food supply chain for traceability, coordination between all actors involved, transparency of operations and improvement of food safety. Furthermore, this is linked to sustainability and the achievement of the sustainable development gtoals (SDGs), while addressing key challenges in the implementation of blockchain-based technologies in the agri-food supply chain.

The application of blockchain in the agri-food supply chain may consider four key aspects. Firstly, the implementation of blockchain can improve the traceability of food products. Secondly, this technology supports sustainability issues and could avoid disruptions in the agri-food supply chain. Third, blockchain improves food quality and safety control throughout the supply chain. Fourthly, the findings show that regulation is needed to improve trust between stakeholders.

The paper provides a comprehensive overview of the blockchain phenomenon in the agri-food supply chain by optimising the search criteria. Moreover, it serves to bridge to future research by identifying gaps in the field.

The current paper aims to provide insights into the determinants associated with job satisfaction among police administrative (personnel) officers in Taiwan, especially both internal organizational predictors and unique external predictors related to the Chinese cultural context.

Data were gathered from police administrative officers across major and medium-sized police agencies in Taiwan. Multiple regression models were employed to analyze the relationship between both internal factors to the organization (e.g. workplace fairness, supervisor support, self-efficacy) and external factors (related to traditional Chinese culture and its expectations) and job satisfaction.

The external factors of work-family life balance and financial benefits are strong predictors, emphasizing the cultural significance of family harmony and financial stability in Taiwanese society. This finding challenges the prevailing notion in the literature that the primary source of job satisfaction among police officers is derived from internal organizational factors. Collectively, the findings concluded the multi-faceted determinants of job satisfaction among administrative officers in Taiwan, intertwining both individual and internal organizational factors with broader external cultural influences.

This study investigated the job satisfaction among administrative officers who play a key role in a police department. The findings showed that external factors exert a significant impact on job satisfaction. This offers a new frontier to examine job satisfaction among not only administrative officers but also patrol officers in Taiwan and Asian countries. In addition, training courses can be developed and focus on work-family relations when officers are off duty.

While previous research has extensively explored job satisfaction among police officers in various roles and countries, by integrating internal organizational and external predictors, this study pioneers the focus on “police administrative officers” within Taiwanese police agencies.

Extant research supports the importance of dynamic managerial capabilities in capturing managers’ individual roles in organisations’ adjustments to change. This paper develops a multidimensional scale for measuring dynamic managerial capabilities consisting of sensing, seizing and reconfiguration capacities that mediate between managers’ affective states and their firms’ performance.

The scale is validated in a survey-based study among 204 managers in companies in the United States of America (USA). We applied a multiple regression model (a triple mediation) using each of DMCs’ three dimensions to test the effects of managers’ affective states on their firms’ performance.

The multidimensional construct of DMCs adds about 15 % of variance explained to a firm’s performance, as perceived by its managers. So managers’ affective states do have an impact on DMCs and, later, on their firms’ performance.

We show the impact of negative and positive affect on DMCs. We also show that DMCs’ three dimensions should be treated in a formative manner that advances discussion on DMCs and their role in a firm’s performance.

Understanding managers’ affective states helps incorporate “hot cognition” into firms’ strategising processes. Although both positive and negative emotions can be helpful, depending on the situation, positive affect is generally more valuable than negative affect as it relates to a firm’s performance.

Our work proposes measuring DMCs based on Teece’s (2007) disaggregation of DMCs into sensing, seizing and reconfiguration capacities. We approach each of these dimensions separately and show that managers’ affective states influence each dimension differently.

This study used post-occupancy evaluation (POE) to assess the performance and effectiveness of student housing by examining occupants’ satisfaction with the facility across three performance elements: technical, functional and behavioral. This study aims to address a significant gap in the existing research concerning the POE of university student housing facilities, particularly in understanding how well these facilities fulfill occupant needs across the aforementioned three elements.

This study assesses the existing literature, examines the dormitory’s physical parameters through walkthrough inspections and measures occupant satisfaction through surveys and focus group sessions. The study analyzed questionnaire responses using a four-point Likert scale, using Microsoft Excel software to determine the weighted average response for each performance element.

The analysis indicated that students were satisfied with most housing condition parameters. However, noise management, lighting control, air circulation, washroom facilities, cleanliness, power sockets for equipment and several study rooms and furnishings require improvement to enhance student well-being and performance.

This study provides significant information to aid in making informative decisions on building maintenance, retrofitting and facility upgrading. It also contributes to enhancing the field’s knowledge about POE in student housing facilities. It emphasizes the importance of using physical building attributes and user-centric features to fulfill students’ needs and expectations regarding facilities.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

This study aims to examine the combinations of internal and external knowledge flows between research and development (R&D) incumbents and start-ups in the context of open innovation. While there is a growing body of knowledge that has examined how, in a knowledge economy, a firm’s knowledge and innovation activities are closely linked, there is no systematic review available of the key antecedents, perspectives, phenomenon and outcomes of knowledge spillovers.

The authors have conducted dual-stage research. First, the authors conducted a systematic review of literature (97 research articles) by following the theories–contexts–methods framework and the antecedent-phenomenon-outcomes logic. The authors identified the key theories, contexts, methods, antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. In the second stage, the findings of stage one were leveraged to advance a nomological network that depicts the strength of the relationship between the observable constructs that emerged from the review.

The findings demonstrate how knowledge spillovers can help incumbent organisations and start-ups to achieve improved innovation capabilities, R&D capacity, competitive advantage and the creation of knowledge ecosystems leading to improved firm performance. This study has important implications for practitioners and managers – it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. The emerging network showed that the antecedents of knowledge spillovers have a direct relationship with the creation of a knowledge ecosystem orchestrated by incumbents and that there is a very strong influence of knowledge capacities and knowledge types on the selection of external knowledge partners/sources.

This study has important implications for practitioners and managers. In particular, it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. This will enable managers to take important decisions about what knowledge capacities are required to achieve innovation outcomes. The findings suggest that managers of incumbent firms should be cautious when deciding to invest in knowledge sourcing from external partners. This choice may be driven by the absorptive capacity of the incumbent firm, market competition, protection of intellectual property and public policy supporting innovation and entrepreneurship.

Identification of the key antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. The findings from Stage 1 helped us to advance a nomological network in Stage 2, which identifies the strength and influence of the various observable constructs (identified from the review) on each other. No prior study, to the best of the authors’ knowledge, has advanced a nomological network in the context of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context.

This study draws on Organisation Justice Theory and Social Exchange Theory to examine the effects of the performance appraisal process (PAP) on employee commitment (ECO) via a serial mediation of performance appraisal outcome (PAO) and employee reward (ERE).

Survey data were collected from a sample of 363 academics across UK Higher Education Institutions (HEIs), including post-1992 and pre-1992 universities. We tested our hypotheses using partial least squares structural equation modeling (PLS-SEM) with a bias-corrected bootstrapping method.

The findings show that the PAP positively influences ECO and ERE. PAO and ERE mediate the relationship between the PAP and ECO. However, no significant relationship was found between PAO and ECO.

This study has significant implications for HEIs as it underscores the need for managers to ensure the clarity and accuracy of the PAP and to structure rewards to reflect employees’ efforts, considering they affect ECO.

This study contributes to the current debate on performance appraisal by highlighting the extent to which employees’ commitment to an organisation depends on the PAP, PAO and reward.