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This study addresses the challenge of integrating entrepreneurial competences development into the traditionally structured engineering curriculum, recognizing its potential contribution to job creation. Specifically, this study proposes a course design that intersects project management and entrepreneurial disciplines, adopting a challenge-based learning approach.

Considering a list of common features that entrepreneurs and engineers—in the role of project managers—should excel at, and building upon the principles of experiential learning, this study proposes and describes a course design that is expected to help engineering students develop entrepreneurial competences. Through a series of assessment instruments and descriptive statistics, the study evaluates its implementation in a pilot test applied in a Project Management course at a Spanish university.

The results demonstrate a significant improvement in students' entrepreneurial competences after completing the course. Noteworthy variations in receptiveness to skill development among different personality profiles are observed. Gender differences are minimal, with the exception of women exhibiting heightened self-perception in the autonomy dimension.

This study explores the common features shared by two often-disconnected disciplines, namely engineering and entrepreneurship. It suggests that integrating both perspectives through a challenge-based course design can enhance entrepreneurial competences among engineering students without compromising the specific knowledge gained from engineering programs. Engaging students in such pedagogical experiences not only fosters entrepreneurial competences but also contributes to their professional and personal growth.

This study aims to assess the effectiveness of active teaching methodologies, namely, problem-oriented learning and the case method, to develop sustainability competencies. It also analyses the advantages and challenges for teachers when implementing the sustainable development goals (SDGs) in eight undergraduate and postgraduate degrees within the framework of a cross-departmental collaboration.

A mixed research methodology was used: a quantitative study to assess the levels of acquisition of sustainability and research competencies and the potential correlation between them, as well as a mixed study of the advantages and challenges for the teachers participating in the cross-departmental initiative. Curriculum content linked to the SDGs was worked on. Active teaching methodologies and a competency assessment rubric were used as curriculum implementation strategies in the eight courses involved.

Active teaching methodologies are suitable to implement the SDGs in university teaching and to develop both sustainability and research competencies. A synergic effect is observed between them. Coordinated work between teachers of different subjects in several degrees contributes to developing a culture of sustainability at the university.

Although the collaboration between teachers from different disciplines was successful, this study did not promote interdisciplinary projects among students from different degrees. This promises to be highly valuable for future research.

Students can become present and future leaders in achieving the SDGs. This approach can be replicated in other educational institutions.

This study bridges the gap between theoretical recommendations and the practical implementation of the SDGs in undergraduate and postgraduate degrees.

Coordinated work between teachers of different subjects in different degrees contributes to the development of a culture of sustainability at the university.

The purpose of this paper is to present findings derived from research work carried out by a team of six university lecturers who are members of a teaching quality improvement network. The aim is to increase the motivation of the lecturers involved, so that better performance can be achieved, and the teaching‐learning process enriched.

Three complementary performance measures are used: students' active participation; lecturers' personal reflections; and lecturer peer‐to‐peer assessment.

These tools have provided the teaching staff with information about their strengths and weaknesses, as a consequence of which they have learned to deploy their skills inside the classroom and prepare their subjects more effectively. In this way, it is shown that the public teaching system can assess the potential of its human resources, and increase their motivation.

From an institutional point of view, performance quality assessment practices can also help to design training and development programmes that adapt the individual needs identified to the educational goals of today's universities.

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

In the last 15 years, online sales of second-hand products have grown substantially due to changes in the economy, increasing interest in sustainability and the new opportunities offered by the alternative markets. However, little is known about the antecedents of customer retention in this particular online market. To this effect, the aim of this research is to bring new insights about what retains shoppers and creates loyal behaviour in this market by presenting a loyalty model based on expectancy-value theory and building on Olsen's satisfaction-loyalty model.

To achieve the above-mentioned purpose, a survey comprised of 507 respondents who had purchased second-hand products via an online platform during 2020 was used to test the model.

The results validate both the satisfaction-loyalty model and the mediator role of the fulfilment of expectations between perceived quality and satisfaction. The results also indicate that customer fulfilment of expectations depends only on the perception of product quality. Perceived product quality was also the dimension that most influenced satisfaction, followed by perceived vendor quality. Last, the dimensions that contributed most to loyal behaviour were satisfaction with the vendor and with the website.

The originality of this research lies in the validation of the unique satisfaction-loyalty model for online second-hand purchases. The value of this research is that it adds knowledge about effective strategies for platform owners and sellers to enhance customer satisfaction and loyalty in online second-hand markets.

Firms innovate and sophisticate their offerings to remain competitive. This sophistication often finds opportunities in servitization. Given that many customers expect the service offering from manufacturing companies, it is crucial to research what capabilities drive service offerings and their impact on performance. The purpose of this paper is to test the capabilities–service–performance chain.

A conceptual model is proposed and the research hypothesis is tested using structural equation modelling (SEM) performed on a data set corresponding to 205 Spanish and Croatian manufacturing firms.

Using SEM, this research analyses the causal model between manufacturing, organizational and digital capabilities on base, intermediate and advanced services, and their impact on both service and financial performance.

This study has some limitations: the advanced service construct was mostly developed on case-based research. Some constructs have low convergent validity and reliability. The relative smallness of the data set used and its two-country provenance could raise issues about the international nature and generalizability of the findings.

Digital capabilities are important for the provision of all three groups of services in terms of using digital devices in data acquisition, helping to make the manufacturing company more agile.

The present study also contributes to the conceptual framework of servitization by providing a new and more up-to-date definition of capabilities and services, also considering digital capabilities, which are less explored. It also contributes to being the first to explore the entire manufacturing sector [nomenclature statistique des activités and économiques dans la Communauté éuropéenne (NACE) 10–31].

Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key economic indicators. Econometric methods, known as weak σ-convergence tests, have recently been developed (Kong, Phillips, & Sul, 2019) to evaluate such trends in dispersion in panel data using simple linear trend regressions. To achieve generality in applications, these tests rely on heteroskedastic and autocorrelation consistent (HAC) variance estimates. The present chapter examines the behavior of these convergence tests when heteroskedastic and autocorrelation robust (HAR) variance estimates using fixed-b methods are employed instead of HAC estimates. Asymptotic theory for both HAC and HAR convergence tests is derived and numerical simulations are used to assess performance in null (no convergence) and alternative (convergence) cases. While the use of HAR statistics tends to reduce size distortion, as has been found in earlier analytic and numerical research, use of HAR estimates in nonparametric standardization leads to significant power differences asymptotically, which are reflected in finite sample performance in numerical exercises. The explanation is that weak σ-convergence tests rely on intentionally misspecified linear trend regression formulations of unknown trend decay functions that model convergence behavior rather than regressions with correctly specified trend decay functions. Some new results on the use of HAR inference with trending regressors are derived and an empirical application to assess diminishing variation in US State unemployment rates is included.

This chapter develops robust panel estimation in the form of trimmed mean group estimation for potentially heterogenous panel regression models. It trims outlying individuals of which the sample variances of regressors are either extremely small or large. The limiting distribution of the trimmed estimator can be obtained in a similar way to the standard mean group (MG) estimator, provided the random coefficients are conditionally homoskedastic. The authors consider two trimming methods. The first one is based on the order statistic of the sample variance of each regressor. The second one is based on the Mahalanobis depth of the sample variances of regressors. The authors apply them to the MG estimation of the two-way fixed effects model with potentially heterogeneous slope parameters and to the common correlated effects regression, and the authors derive limiting distribution of each estimator. As an empirical illustration, the authors consider the effect of police on property crime rates using the US state-level panel data.

The purpose of the chapter is to study peculiarities of formation of “green economy” in Russia and to substantiate the perspectives of its development by technological parks. The “green economy” is just appearing in Russia, as its volume is 4% of GDP as of now, with a downward trend for the recent 10 years. Favorable conditions for the formation of “green economy” are created in technological parks due to the attraction of the necessary volume of investments and increased state regulation. Technological park ‘Sinarsky’, West-Siberian innovational center (Tyumen technological park), and technological park of high-tech of Khanty-Mansi Autonomous Okrug show “green” initiatives. However, their share in the total structure of technological parks of Russia is only 2%. The volume of “green” investments in technological parks of Russia constitutes RUB 0.58 billion, and the volume of “green” production – RUB 9.153 billion, with 3,980 “green” jobs. The developed authors’ concept and the offered practical recommendations allow for the deeper study of the potential of technological parks in the context of the “green economy” in Russia. According to the forecast, their implementation will allow ensuring achievement of the share of “green” technological parks in their structure at the level of 30%, thus increasing the volume of “green” investments in Russia’s technological parks to RUB 7.888 billion: the volume of “green” production to RUB 124.48 billion, and the number of “green” jobs to 54,128. “Green” development of technological parks will allow increasing the volume of “green” economy in Russia by 3.2% until 2025.

We generalise the spectral EM algorithm for dynamic factor models in Fiorentini, Galesi, and Sentana (2014) to bifactor models with pervasive global factors complemented by regional ones. We exploit the sparsity of the loading matrices so that researchers can estimate those models by maximum likelihood with many series from multiple regions. We also derive convenient expressions for the spectral scores and information matrix, which allows us to switch to the scoring algorithm near the optimum. We explore the ability of a model with a global factor and three regional ones to capture inflation dynamics across 25 European countries over 1999–2014.