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This study investigates the intellectual structure of the front-end of innovation (FEI) research field and elaborates a lifecycle model, which embraces the FEI progress and conceptual development.

This study analysed 355 papers published from 1987 to 2020 in the Scopus and Web of Science databases through bibliometrics, network analysis and content analysis.

This study reveals FEI research themes and topics and explores their evolution through three periods. In addition, following a lifecycle approach, it also sets the research field foundation and discusses the maturity and potential progress of the FEI intellectual structure.

This study complements the existing FEI reviews by capturing the most relevant topics in the area, showing how these topics relate to each other to form the intellectual domain and providing insights concerning the research progress through different lifecycle stages. It also extends the FEI database used in the other literature reviews.

This research aims to answer two major research questions related to the COVID-19 crisis from a longitudinal approach: What is the revenue management (RM) role during the different periods subject to analysis? What are the RM strategies and measures implemented during this crisis in contrast with a non-crisis context? It also aims to propose an RM implementation model that provides a contingency plan to face future crises.

This qualitative study, following a longitudinal approach, analyses three round-table discussions with 11 internationally renowned experts during three key scenarios of the COVID-19 crisis: the lockdown period (from March to June 2020) and the following two summer seasons (the post-lockdown period): Post-lockdown I (the summer campaign, 2020) and Post-lockdown II (the summer campaign, 2021). Based on a deductive approach, thematic analysis is conducted using NVivo.

Further professionalisation of revenue managers, which has enabled the correct application of strategies and measures, highlighting the importance of not lowering prices, the flexibility of booking conditions, the development of other sources of income and the increase in the value of services, amongst others, are key factors in managing this crisis. The longitudinal analysis carried out in three different periods of this crisis shows how these measures have evolved and the contrast with RM application in a non-crisis context. The revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RM systems (RMSs) by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.

This study has various limitations. First, the three round-table discussions were held online due to the health crisis, and the chosen webinar format may have biased the participants' answers due to its public nature. Second, the survey was carried out in Spanish. Despite the strong international profiles of the participants, cultural distortion may appear, suggesting that the research should possibly be extended to other cultural contexts in the future. Third, some of the participants were unable to attend all the round-table discussions due to their professional duties, so people with similar profiles were invited to the rest of the sessions.

The revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RMSs by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.

This work contributes to the literature by providing a model that considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context. The novelty of this research is mainly found in the conducting of a deductive and longitudinal study considering previous research focussed on RM strategies applied during the COVID-19 crisis and supplementing it with new measures by applying qualitative techniques.

The authors’ goal in this paper is to study if there are long-run effects on the wages of those workers who entered the labour market overeducated but who have, over time, been able to overcome this situation by obtaining a job for which they are correctly matched.

This study universe is constituted by workers entering the labour market with a university degree between 2004 and 2012. The age range of these individuals is between 22 and 35 years old. With the data of the 8,359 selected individuals, the authors have constructed a balanced panel covering the period 2013–2017. This methodology is developed in two steps. First, the authors estimate a wage equation with the traditional variables and, second, the authors use the estimated coefficients of these variables to predict the wage paths of a representative individual in each group.

The main result the authors obtain indicates that the wages of those who manage to overcome an initial situation of overeducation do converge but very slowly to the respective wages of those others that entered the labour market correctly matched from the beginning. The authors consider this result to point towards the existence of scarring effects in wages induced by an initial situation of overeducation. The authors also present evidence, beyond education, about the influence that the occupational characteristics that a worker has on wages.

The factors that can influence the catching-up of wages are multiple, and it is not feasible to test all of them empirically. Therefore, the wage convergence process the authors present may also be influenced by other variables for which the authors do not have information.

This paper contributes to different branches of the labour market. First, the authors present new evidence within the literature dealing with the so-called scarring effects on wages related to the conditions entering the labour market. Secondly, this study’s results provide a new argument that complements those developed so far that explain a reduction in the wage skill premium detected among young graduates in Spain. Finally, this paper contributes to advancing research about the effects that overeducation has on wages.

The question the authors are attempting to answer in this paper can be formulated in the following terms: when a worker manages to overcome an initial situation of overeducation, what happens to his/her wage? Will it adjust quickly to the new working situation, or will we observe a slow convergence to the wages of workers with an employment history without overeducation situations? To the best of the authors’ knowledge, this topic has not yet been studied. Researchers have mostly focused their attention on comparing the wages of overeducated workers with the wages of those who are correctly matched. In this case, the authors compare the wages of correctly matched workers, but with the difference that some were initially overeducated and others were not.

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

This research aims at the theory of planned behavior (TPB) and the global entrepreneurship monitor (GEM) framework – How can cognitive traits for entrepreneurship be used by incubators and accelerators?

In this research the authors analyze the factors that catalyze the founding of new technology-based firms. From a practitioner stand-point, the GEM posits that these factors can be classified as contextual, social and individual factors. The present study focuses on the latter, looking into how demographic characteristics, possession of human capital and cognitive traits interrelate. The authors rely on a sample of 141 technological new ventures being incubated in Madrid, Spain, which is analyzed with the multilayer perceptron technique.

The results show that cognitive traits, as defined in the TPB, act as the “last mile” in the entrepreneurial decision process, while demographic and human capital factors appear to antecede them. These results are relevant for incubators and accelerators, which now gain a better, more complete understanding of success factors of their incubatees.

This research deals both with practitioners' view of entrepreneurship and with scientific literature, intertwining both with the purpose of providing valuable information for incubators and accelerators.

This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).

The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs).

The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs.

The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs.

The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.

This study aims to analyze the alignment between sustainable performance and sustainability planning in higher education, proposing a strategic map that integrates planning with the implementation of performance actions and sustainable performance on campuses.

The literature review development addresses sustainability in higher education institutions (HEIs). Data collection took place in two ways, documentary, through the analysis of documents and through an open interview, guided by a script with 13 questions. For data interpretation, the content analysis technique was applied.

To achieve the objective of this work, this study proposed a sustainable performance strategic map for better management of the university’s green strategies, based on three dimensions: internal processes, educational and sustainable performance.

This study’s main contribution was to propose a sustainable performance strategic map as a strategic management system aimed at HEIs to accelerate the promotion of sustainability in these organizations.

The “public interest” of financial institutions was used as an argument to intervene in accounting practices. The Bank of Spain's standard was not compatible with International Accounting Standard (henceforth IAS) 39 and the Spanish banking sector had become one of the most provisioned in Europe. This makes it an interesting case study of the relationship between provisioning and income smoothing. The 2008 financial crisis revealed that provisions were insufficient and a reinforcement regulation process began in 2012. This paper aims to examine whether, since 2012, the Bank of Spain's regulatory effort on impairment accounting standards has induced less income smoothing, correcting its countercyclical effect.

A regression model is applied during the period 2005–2020, to test whether there is a trend change in the correlation between the level of provisions and annual earnings in 2012.

The results show that from 2012 onwards (when the Bank of Spain reinforced the regulation on provisioning), there was a correction in income smoothing behaviour.

This study provides empirical evidence that reinforces the claim that accounting policy can affect decision-making accounting practices, in this particular case, at the Bank of Spain.

The purpose of this study is to identify papers that have produced the most significant impact on research on strategic alliances in the biotechnology industry. The authors attempt to illustrate the thematic evolution of its intellectual structure through 616 papers published between 1992 and 2021.

The present research methodology relies on three distinct techniques, implemented using SciMat software: (1) bibliometric techniques, (2) scientific map analysis and (3) content analysis of research documents from the Web of Science (WoS). In this manner, the authors analyse the intellectual structure of the field of strategic alliances in the biotechnology industry, tracking its evolution over a period of three decades.

The study emphasises the relevance of “innovation” as a key theme and identifies several potential areas for future research, which could serve as a foundation for further investigations.

This study represents a novel contribution to the literature as it is the first to use the SciMat tool to analyse strategic alliances in the biotechnology industry. This research reveals that while strategic alliances have been assessed extensively across various industries, some topics, such as the types and formation of alliances, have not been specifically studied in the biotechnology industry. These areas as well as the barriers and variables influencing the formation of alliances offer promising avenues for future research in this field.

本研究旨在確定對關於生物科技產業內的策略聯盟的探究產生極其顯著影響的學術論文。我們擬透過探討於1992年至2021年期間發表的616篇學術論文，去闡明策略聯盟的知識結構的主題演變。

研究依賴三個不同的技術來進行，並以SciMat 可視化軟件來做具體實施的工作。這三個技術為、(一) 文獻計量技術;(二) 科學製圖分析;和 (三) 就取自 Web of Science 的學術文章而進行的內容分析。我們採用這研究法，對在生物科技產業內的策略聯盟的知識結構進行分析，俾能對有關的知識結構的主題演變進行一個涵蓋三十載的跟蹤調查。

研究強調了創新，並視之為主要的主題的重要相關概念;研究亦確定了一些今後可供研究的潛在領域，這或許會成為進一步研究的基礎。

由於本研究是首個研究、使用SciMat這工具去分析在生物科技產業內的策略聯盟，故就有關的文獻而言，它給予新穎的貢獻。研究結果顯示，雖然策略聯盟已在各個不同的產業裡被廣泛評價，但在生物科技產業裡，一些如聯盟的種類和形成方式等的課題仍未得到適切的研究。這些課題，以及影響著聯盟的形成的障礙和變數，會為這領域內今後的學術研究、提供光明的途徑。