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From the buying club’s perspective, the transfer of a player can be interpreted as an investment from which the club expects uncertain future benefits. This paper aims to develop a decision-oriented approach for the valuation of football players that could theoretically help clubs determine the subjective value of investing in a player to assess its potential economic advantage.

We build on a semi-investment-theoretical risk-value model and elaborate an approach that can be applied in imperfect markets under uncertainty. Furthermore, we illustrate the valuation process with a numerical example based on fictitious data. Due to this explicitly intended decision support, our approach differs fundamentally from a large part of the literature, which is empirically based and attempts to explain observable figures through various influencing factors.

We propose a semi-investment-theoretical valuation approach that is based on a two-step model, namely, a first valuation at the club level and a final calculation to determine the decision value for an individual player. In contrast to the previous literature, we do not rely on an econometric framework that attempts to explain observable past variables but rather present a general, forward-looking decision model that can support managers in their investment decisions.

This approach is the first to show managers how to make an economically rational investment decision by determining the maximum payable price. Nevertheless, there is no normative requirement for the decision-maker. The club will obviously have to supplement the calculus with nonfinancial objectives. Overall, our paper can constitute a first step toward decision-oriented player valuation and for theoretical comparison with practical investment decisions in football clubs, which obviously take into account other specific sports team decisions.

This study reexamines fiscal deficit sustainability in South Africa.

The study applies three cointegration testing approaches, namely testing for multiple structural changes in a cointegrated regression model, time-varying cointegration test and asymmetric cointegration test.

The results point to the existence of a level relationship between government revenue and spending. In addition, the long-run equilibrium relationship between government revenue and spending in South Africa is found to be characterized by breaks. As such, assuming a constant cointegrating slope may be misleading. Results from time-varying cointegration and an estimation of a cointegrated two-break model indicate that cointegrating coefficient has been time-varying but has remained less than 1 for the entire study period, indicating that fiscal deficits have been weakly sustainable. This finding is also confirmed by the results from an estimated asymmetric error correction model.

In view of the findings, authorities should put in place policies to improve the fiscal budgetary stance and reinforce the sustainability of the fiscal deficits in South Africa. Among other things, South Africa could undertake reforms to state-owned companies to reduce their reliance on public funds, slow down the pace of the public sector wage growth and devise effective economic measures to boost long-term growth. In addition, tax compliance and other revenue collection measures should be enhanced for additional tax revenue.

The contribution of this study is twofold; first, the study uses a long series of annual data spanning over a century, from 1913 to 2020. Indeed, cointegration is better modeled using long spans of time series data. Second, to examine the existence of a level relationship between spending and revenue, the study uses cointegration tests which allow capturing time-variation in the cointegrating slope coefficient, and accounting for asymmetries in the relationship between government spending and revenue. It is important to allow for time-variation in the cointegrating slope coefficient, especially when it has been hardly treated in the empirical literature on fiscal deficit sustainability. Allowing for time-variation in the cointegrating slope coefficient helps us to analyze fiscal deficit sustainability by periods of time. Indeed, the degree of fiscal sustainability can change from one time period to another.

The rapid, yet low-profit, expansion of the production capacity of state-owned enterprises (SOEs) represents a remarkable phenomenon. However, the motivation behind this key operational decision remains underexplored, especially concerning the prioritization of sociopolitical and financial goals in operations management. Drawing on the multiple-goal model in the behavioral theory of the firm (BTOF), the authors' study aims to examine how SOE capacity expansion is driven by performance feedback regarding the sociopolitical goal of employment provision and how SOEs differently prioritize sociopolitical and financial goals based on negative versus positive feedback on the sociopolitical goal.

The authors' study uses panel data on 826 Chinese SOEs in manufacturing industries from 2011 to 2019. The authors employ the fixed-effects model with Driscoll–Kraay standard errors, which are robust to heteroscedasticity, autocorrelation and cross-sectional dependence.

The authors find that SOEs increase capacity expansion as sociopolitical feedback becomes more negative, but they may not increase capacity expansion in response to positive sociopolitical feedback. Moreover, negative profitability feedback strengthens SOEs' capacity expansion in response to negative sociopolitical feedback. In contrast, negative profitability feedback weakens their response to positive sociopolitical feedback.

The authors' study offers a novel behavioral explanation of SOEs' operational decisions regarding capacity expansion. While the literature has traditionally assumed multiple goals as either hierarchical or compatible, the authors extend the BTOF's multiple-goal model to illuminate when firms pursue sociopolitical and financial goals as compatible (i.e. the activation rule) versus hierarchical (i.e. the sequential rule), thereby reconciling their tension in distinct performance situations. Practically, the authors provide fine-grained insights into how operations managers can prioritize multiple goals when making operational decisions. The authors' study also shows how policymakers can influence SOE operations to pursue sociopolitical goals for public benefit.

The purpose of this paper is to directly link information technology (IT) education with real-world phenomena.

The selected objectives are achieved by modeling line of sight (LOS) and nonline of sight (NLOS) mobile channels using corresponding distributions. Within the described experiments, students verify whether modeled generators generate random variables accordingly to the selected distribution. The results of observations are directly compared with theoretical expectations. The methodology was evaluated by students via questionnaires.

The results show that the proposed methodology can help graduate or undergraduate students better comprehend lectured material from mobile communications or mathematical statistics.

The hands on experience using the EMONA system make the approach original.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

The study aims to investigate the effect of corruption and crime on the investments by firms in emerging economies (EEs).

The study adopts the generalised methods of moments (GMM) estimator and data across 57 EEs.

The study shows that crime management, corruption and external quality assurance drive-up investments. Additionally, investments decline with firm age and crime incidence. Corruption and crime managements increase investments by exporting firms more than non-exporting firms investments. Also, external auditor services benefit investments by large firms more than small-medium firms.

There is a need for EEs to implement policies that will curtail corruption and create a level playing field and sustainable firm growth. EEs firms must be innovative to expand their productive investments and grow over time. Also, EEs firms should seek external quality certification, invest in internal security and monitor goods in transit.

The Public–Private Partnership (PPP) modality plays an important role in the procurement of global infrastructure projects. Regarding PPP's complex transaction structure, pricing of a PPP project is critical to both parties where the government pursues a high value for money (VFM) and the investor strives to maximize its financial gains. Despite the straightforward win–win principle, a formidable compromise is often the case to end up with a fairly acceptable price, subject to many determinants such as the risk profile, expected return, technological innovation and capacities of both parties. Among them, this study chooses to examine the “managing flexibility” (MF) capacity of investors in pricing of a PPP project, in light of the widely recognized importance of a real-option perspective toward the long term, complex and uncertain PPP arrangement. This study addresses two major questions: (1) how is MF in PPP projects to be valued and (2) how are PPP projects to be priced when considering a project's MF value.

A binomial tree model is used to evaluate the MF value in PPP projects. Based on the developed MF pricing model, net present value (NPV) and adjusted VFM value are then calculated. Finally, a multi-objective decision-making method (MODM) was adopted to determine the optimal level of returns based on invested capital (ROIC), return on operation maintenance (ROOM) and concession period.

The applicability and functionality of the proposed model is investigated using a real project case. For a given return, extended NPV and adjusted VFM value were calculated and analyzed using sensitivity analysis. Factor influence is shown by the model to be dependent on factor impact on cash flow. Subsequently, a multi-objective decision-making (MODM) model was adopted to determine the optimal level of returns, where the solution approximates the real-world bidding price. Results confirm that the pricing model provides a reliable and practical PPP proposal pricing tool.

This study proposes an integrated framework for valuing MF in PPP projects and thus more accurately determine optimal pricing of PPP projects than revealed in extant research. The model offers a practical tool to aid in the valuation of PPP projects.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

The purpose of this study is to revisit the measures of internal consistency for multi-item scales in hospitality research and compare the performance of Cronbach’s α, omega total (ω_Total), omega hierarchical (ω_H), Revelle’s omega total (ω_RT), Minimum Rank Factor Analysis (GLB_fa) and GLB algebraic (GLB_a).

A Monte Carlo simulation was conducted to compare the performance of the six reliability estimators under different conditions common in hospitality research. Second, this study analyzed a data set to complement the simulation study.

Overall, ω_Total was the best-performing estimator across all conditions, whereas ω_H performed the poorest. α performed well when factor loadings were high with low variability (high/low) and large sample sizes. Similarly, ω_RT, GLB_fa and GLB_a performed consistently well when loadings were high and less variable as well as the sample size and the number of scale items increased. Of the two GLB estimators, GLB_a consistently outperformed GLB_fa.

This study provides hospitality managers with a better understanding of what reliability is and the various reliability estimators. Using reliable instruments ensures that organizations draw accurate conclusions that help them move closer to realizing their visions.

Though popular in other fields, reliability discussions have not yet received substantial attention in hospitality. This study raises these discussions in the context of hospitality research to promote better practices for assessing the reliability of scales used within the hospitality domain.

Servitization of products is becoming increasingly prevalent among manufacturing enterprises. Existing research has primarily focused on exploring whether the direct impact of servitization on manufacturer performance follows a linear or a curvilinear relationship. However, the understanding of the underlying mechanisms between servitization and manufacturer financial performance remains limited. This paper aims to examine the non-linear relationship between servitization and manufacturer performance as well as the mediating process and boundary condition associated with this relationship.

Drawing on resource-advantage theory, this paper proposes a theoretical model of the U-shaped relationship between servitization and the financial performance of equipment manufacturers. Panel data of 248 listed equipment manufacturers in China during the period of 2010–2020 are used to test each hypothesis through the ordinary least square method.

The empirical results indicate that servitization follows a U-shaped relationship with service business focus and the financial performance of equipment manufacturers. Service business focus mediates this U-shaped relationship between servitization and financial performance, and digital technology application moderates this relationship.

This paper pioneers the unraveling of the potential mechanism that can explain the curvilinear relationship between servitization of manufacturers and financial performance. This mechanism is the focus of the service business, which is theoretically delineated and empirically tested. Furthermore, digital technology application enables manufacturers to achieve service business focus more effectively in the process of servitization. Thus, this study addresses the call for research on digital servitization.