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The objective of this work is to demonstrate the relationships between the two main processes of research and development (R&D) activities: the knowledge generation phase (KPP) and the knowledge commercialization, or transfer, phase (KCP), in a sector that is intensive in this type of activity, such as the pharmaceutical sector. In addition, within the framework of the general objective of this work, the authors propose two other objectives: (1) make advances in network efficiency measurement models, and (2) determine the factors associated with efficiency in the KPP and in the KCP in companies of the pharmaceutical sector in Spain.

A Network Data Envelopment Analysis (NDEA) model (Färe and Grosskopf, 2000) with categorical variables (Lee et al., 2020; Yeh and Chang, 2020) has been applied, and a sensitivity analysis of the obtained results has been performed through a DEA model of categorical variables, in accordance with the work of Banker and Morey (1986), to corroborate the results of the proposed model. The sample is made up of 77 companies in the pharmaceutical sector in Spain.

The results obtained point to a greater efficiency of pharmaceutical companies in the KPP, rather than in the KCP. Furthermore, the study finds that 1) alliances between companies have been the accelerating factors of efficiency in the KCP (but patents have slowed this down the most); 2) the quality of R&D and the number of R&D personnel are the factors that most affect efficiency in the KPP; and 3) the quality of R&D again, the benefits obtained and the position in the market are the factors that most affect efficiency in the KCP.

The authors have not found studies that show whether the efficiency obtained by R&D-intensive companies in the KPP phase is related to better results in terms of efficiency in the KCP phase. No papers have been found that analyse the role of alliances between R&D-intensive companies and patents, as agents that facilitate efficiency in the KCP phase, covering the gap in the research on both problems. Notwithstanding, this work opens up a research path which is related to the improvement of network efficiency models (since it includes categorical variables) and the assessment of the opinions of those who are responsible for R&D departments; it can be applied to decision-making on the aspects to improve efficiency in R&D-intensive companies.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

The purpose this paper is to review some of the statistical methods used in the field of social sciences.

A review of some of the statistical methodologies used in areas like survey methodology, official statistics, sociology, psychology, political science, criminology, public policy, marketing research, demography, education and economics.

Several areas are presented such as parametric modeling, nonparametric modeling and multivariate methods. Focus is also given to time series modeling, analysis of categorical data and sampling issues and other useful techniques for the analysis of data in the social sciences. Indicative references are given for all the above methods along with some insights for the application of these techniques.

This paper reviews some statistical methods that are used in social sciences and the authors draw the attention of researchers on less popular methods. The purpose is not to give technical details and also not to refer to all the existing techniques or to all the possible areas of statistics. The focus is mainly on the applied aspect of the techniques and the authors give insights about techniques that can be used to answer problems in the abovementioned areas of research.

The purpose of this paper is to provide an in-depth content analysis of phishing messages and to enhance understanding of them from a persuasive communication perspective.

This study analysed phishing message content in a persuasion mechanism framework including message presentation and content (rational appeal, emotional appeal, reasoning type). It also used semantic network analysis to identify meaning structure.

The results indicate that phishing messages used logical appeals, reasoning from cause, motivational appeals, appealing to safety needs, and emotional appeals to gain compliance. Semantic network analysis showed that two word clusters represent security and privacy.

This study applied modern persuasion and deceptive communication theories to interpret phishing e-mails. The findings enhance relevant theories by including phishing e-mail cases.

The results of this study can be utilised for developing phishing prevention techniques and phishing detection software.

Past phishing detection studies only used a technological approach, whereas the current study provides a more comprehensive content-oriented and persuasion theory-based understanding of phishing messages.

LifeMatters is a cognitive behavioural coaching programme that provides tools and techniques for developing and applying five areas of life skill competency: taking care of the body, feeling positive, thinking wisely, acting wisely, and taking care of the spirit. The purpose of paper is to evaluate the viability of the LifeMatters programme with a cohort of secondary school students (12-15 years) in Ireland.

Open-ended feedback obtained from 196 participants who completed an open-ended questionnaire. These responses were subjected to a thematic analysis. Subsequent quantitative analysis of the resulting categorical data were carried out using correspondence analysis.

Categorical analysis produced statistically significant sex and age differences showing that males and females differed in their experience of the programme. Girls benefited more from a focus upon stress and self-confidence whereas boys benefited more from a focus on relationship building.

This study demonstrates the viability of the LifeMatters programme for secondary school students as an aid to develop life skills. It highlights the different needs of boys and girls in this area.

The purpose of this paper is to create an automatic interpretation of the results of the method of multiple correspondence analysis (MCA) for categorical variables, so that the nonexpert user can immediately and safely interpret the results, which concern, as the authors know, the categories of variables that strongly interact and determine the trends of the subject under investigation.

This study is a novel theoretical approach to interpreting the results of the MCA method. The classical interpretation of MCA results is based on three indicators: the projection (F) of the category points of the variables in factorial axes, the point contribution to axis creation (CTR) and the correlation (COR) of a point with an axis. The synthetic use of the aforementioned indicators is arduous, particularly for nonexpert users, and frequently results in misinterpretations. The current study has achieved a synthesis of the aforementioned indicators, so that the interpretation of the results is based on a new indicator, as correspondingly on an index, the well-known method principal component analysis (PCA) for continuous variables is based.

Two (2) concepts were proposed in the new theoretical approach. The interpretative axis corresponding to the classical factorial axis and the interpretative plane corresponding to the factorial plane that as it will be seen offer clear and safe interpretative results in MCA.

It is obvious that in the development of the proposed automatic interpretation of the MCA results, the authors do not have in the interpretative axes the actual projections of the points as is the case in the original factorial axes, but this is not of interest to the simple user who is only interested in being able to distinguish the categories of variables that determine the interpretation of the most pronounced trends of the phenomenon being examined.

The results of this research can have positive implications for the dissemination of MCA as a method and its use as an integrated exploratory data analysis approach.

Interpreting the MCA results presents difficulties for the nonexpert user and sometimes lead to misinterpretations. The interpretative difficulty persists in the MCA's other interpretative proposals. The proposed method of interpreting the MCA results clearly and accurately allows for the interpretation of its results and thus contributes to the dissemination of the MCA as an integrated method of categorical data analysis and exploration.

Employees’ satisfaction and productivity is one of the main interests of employers. Psychological comfort can cause dissatisfaction with their work. Thus, it is important to understand what factors contribute to employees’ satisfaction in workplaces. The purpose of this paper is to identify the weight of contribution of each design parameter on increasing psychological satisfaction.

The study included 579 employees in five offices in The Netherlands through an online survey. The typologies of offices vary in terms of office layouts, orientations and façade. Additionally, a parameter of desk location was included as this factor may be associated with user satisfaction. Kruskal–Wallis H test, categorical regression, and logistic regression analyses were performed to examine the impact of these design parameters on psychological user satisfaction.

The results revealed the impact of design parameters on the psychological satisfaction. The parameters of office layouts and desk locations were the significant predictor factors for the probability of satisfaction variables (e.g. privacy, concentration, communication, social contact and territoriality). The parameters for optimal satisfaction were found in cellular office, north-west oriented workstation and 4 m away from a window.

Psychological comfort is an inevitable aspect in user satisfaction studies. This paper, therefore, measures and predicts the relationship between design factors and employees’ satisfaction through case studies in The Netherlands. The findings help designers, architects, planners and facility managers to develop user-focussed office design principles supporting employees’ work performance.

This study aims to examine the factors of growth‐oriented atmosphere in a Finnish polytechnic institution of higher education with categorical exploratory factor analysis, multidimensional scaling and Bayesian unsupervised model‐based visualization.

This study was designed to examine employee perceptions of how their managers create conditions that support professional growth and learning, and how the employees perceive their growth motivation and commitment to the organization. Data were gathered from 447 employees with the Growth‐oriented Atmosphere Questionnaire in a Finnish polytechnic institution of higher education.

Results showed that the theoretical four‐group classification of the growth‐oriented atmosphere factors was supported by the empirical evidence. Results further showed that managers and teachers had higher growth motivation and level of commitment to work than other personnel, including job titles such as cleaner, caretaker, accountant and computer support. Employees across all job titles in the organization, who have temporary or part‐time contracts, had higher self‐reported growth motivation and commitment to work and organization than their established colleagues.

Leaders in various organizations may benefit from learning what is the current professional growth status of diverse employee groups, and in understanding the potential differences in employee growth motivation.

This study contributes to an understanding of organizational growth and learning as a non‐linear process. The statistical non‐linear modeling approach is novel providing research and practical example of how to use these techniques in practice.