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The purpose of this paper is to provide an illustrated step-by-step guideline of the partial least squares factorial structural equation modeling (PLS FAC-SEM) approach. This approach allows researchers to assess whether and how model relationships vary as a function of an underlying factorial design, both in terms of the design factors in isolation (i.e. main effects) as well as their joint impact (i.e. interaction effects).

After an introduction of its building blocks as well as a comparison with related methods (i.e. n-way analysis of variance (ANOVA) and multi-group analysis (MGA)), a step-by-step guideline of the PLS FAC-SEM approach is presented. Each of the steps involved in the PLS FAC-SEM approach is illustrated using data from a customer value study.

On a methodological level, the key result of this research is the presentation of a generally applicable step-by-step guideline of the PLS FAC-SEM approach. On a context-specific level, the findings demonstrate how the predictive ability of several key customer value measurement methods depends on the type of offering (feel-think), the level of customer involvement (low-high), and their interaction (feel-think offerings×low-high involvement).

This is a first attempt to apply the factorial structural equation models (FAC-SEM) approach in a PLS-SEM context. Consistent with the general differences between PLS-SEM and covariance-based structural equation modeling (CB-SEM), the FAC-SEM approach, which was originally developed for CB-SEM, therefore becomes available for a larger amount of and different types of research situations.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

The purpose of this paper is to analyse whether hotels that use a revenue management system (RMS) outperform non-RMS-users in a context of decreasing demand.

A database of chain hotels with a rating of three or more stars was used to estimate MANOVA and ANOVA models to analyse the role of RMSs in hotel performance.

In a context of strong competition in prices and surplus capacity, the findings suggest that RMSs have been more effective in improving occupancy than in achieving higher rates. Also, the use of RMSs did not have a significant impact on hotel labour productivity.

Managers may believe that they have adopted an RMS when, in fact, they have not fully done so. In addition, establishment-level unobserved heterogeneity, such as the quality of management or unobserved quality of service, cannot be fully controlled because of the nature of the data used. The main implication of this paper is that the potential of RMSs as revenue enhancer might be influenced by unstable market and economic conditions. However, the absence of significant effects on RevPAR performance might be also the result of firms’ adopting inadequate RM strategies. Further research could investigate whether the findings are context-specific or whether firms are failing to implement effective RMSs for other reasons.

The approach used in this paper is new to the literature, given that it uses statistical methods to analyse the impact of implementing an RMS on hotel performance under specific economic conditions and using alternative indicators.

Results of a study into the relationships between students’ academic performance and their cognitive and learning styles are presented. A questionnaire containing three instruments assessing learning and cognition was distributed to second‐ and final‐year undergraduates studying on a general and business management degree. The outcomes of this are explored and analysed in relation to the students’ selection of modules and performance in assessments. The research explores whether students’ approaches to learning and cognition influence their selection of and performance in modules. This paper also incorporates consideration of the impact on performance of other factors, notably gender, and mode of study. Results show some impact of style on performance and module selection, but these are not consistent. The implications of this for higher education practice and learning and cognitive style research are discussed.

Celiac disease patients cannot consume gluten-containing diets; thus, gluten-free products should be offered to meet the nutritional needs of these patients. The purpose of this study was to produce gluten-free tarhana for celiac disease patients using corn flour instead of wheat flour and investigate some physicochemical properties of tarhana. Hydrocolloids were used to compensate for technological deficiencies caused by the absence of gluten.

Hydrocolloids including guar gum, xanthan gum and locust bean gum were added at concentrations of 0.5% and 1.0% to the corn flour. The substituted corn flour samples were used to produce tarhana powder.

The pH and acidity measurements were carried out in 0th, 24th and 48th h of fermentation, and for all samples, the pH gradually decreased during fermentation, whereas the acidity increased. According to the color measurements (L, a and b values), it was observed that there was no significant difference (p = 0.588) between the gums in terms of L values in tarhana dough samples. Water retention capacity values of control, guar gum 1%, xanthan gum 1% and locust bean gum 1% were found to be 1.1, 1.1, 0.7 and 1.2 mL/g, respectively. The viscosity measurements were carried out at three different temperatures (30°C, 45°C and 60°C), and the viscosity values were found to decrease significantly (p = 0.000) with the increase in temperature for all the samples studied. The highest viscosity values were obtained by 1.0% xanthan gum (4,333 mPa s) and 0.5% locust bean gum (3,575 mPa s) added tarhana samples for 3 rpm at 30°C. Xanthan gum addition showed the lowest foam capacity values (0.04 mL/mL) among the samples. The samples with guar gum, xanthan gum and locust bean gum are recommended with regard to consistency and foam stability in the production of tarhana.

This study confirms that the use of gums in tarhana, a gluten-free system, is beneficial for the technological aspect. The unfavorable properties that can be seen because of the absence of gluten in corn flour tarhana can be compensated with the use of hydrocolloids, and tarhana can be recommended to celiac disease patients.

This paper investigates the income dynamics of Italian primary sector, during and after the international economic crisis. It focuses on three research questions: what has been the evolution of the main profitability ratios of agricultural enterprises in recent years? After the crisis, have the surviving farms increased their profitability? Has the profitability been different also in relation to the geographic location?

Income dynamics of a sample of companies have been analyzed, obtaining the 10-year evolution of the average value of three income indices (return on equity [ROE], return on assets [ROA] and return on sales [ROS]). Statistical elaborations and the analysis of variance (ANOVA) method have been used.

The years of the international economic crisis are often characterized by higher incomes than the following ones. The descending trend involves all three national macroareas of Italy, although characterized by considerable socioeconomic differences.

The study considers only the society that survived the crisis, so, presumably, the strongest. Moreover, other ratios should be considered in order to have a more complete view.

Public policymakers could use this study for a better intervention in support of agricultural and agro-industrial activities.

The careful economic and financial analysis of the sector favors the relaunching strategies of the Italian primary sector in which many employees work.

The research contributes to the literature by providing a quantitative analysis of the dynamics of the sector, through the comparative information that may be derived from financial statements.

This study adds to the developing literature on how coronavirus disease 2019 (COVID-19) affected policing. Unlike prior research, which focused on police agencies, the authors focus on the perceptions and experiences of police officers. Specifically, about changes in workload or activities during the peak of the pandemic compared to prior to the pandemic. Additionally, officers report on changes in potential second-order effects resulting in changes from the pandemic.

The data come from the survey responses of 167 police officers from seven police agencies of various sizes from around the USA. The authors assessed mean level differences between organizations using a general linear model/ANOVA approach and report a standardized effect size.

There is a considerable heterogeneity in police officers' perceptions of organizational and operational changes made by their police agencies in response to the COVID-19 pandemic. The results show that perceptions of some changes were more strongly by the agency than were others. The study’s results show there are substantive differences in how police officers from different police agencies viewed these operational and organizational changes (i.e. between agency differences). Most of the variance was primarily explained by differences between police officers within the same agency (i.e. within organization differences).

This study moves beyond the monolithic approach to studying how the pandemic affected the police agency and moves to asking officers about their experiences with these changes and the second-order effects of these changes.

Several studies have reported on psychometric and factor‐analytic work related to the team climate inventory (TCI), including its four scales and 13 subscales. This exploratory study reports on the first research to examine team typologies based on team climate scores. The TCI was administered twice to 84 and 63 self‐managed teams of management undergraduates completing graded term projects. Following each TCI administration, a two‐stage clustering procedure (Ward’s and K‐means) was used to determine the number and team composition of clusters. Clusters were then plotted on the multidimensional space (INDSCAL) and a discriminant analysis performed to determine how well cluster membership was predicted using scores from the 13 TCI subscales. At the first TCI administration, three weeks into the term projects, the study obtained three team clusters on a two‐dimensional space. Near the end of the term projects, the study found two team clusters on a two‐dimensional space. Implications are presented for team building, interventions to improve team climate and management training.

Intercultural competence has been found to be increasingly important. The purpose of this paper is to understand how intercultural competence impacts service providers’ ability to recognition faces of both black and white consumers.

Two experiments were administered to understand how intercultural competence impacts recognition of black and white consumer faces.

The authors find that the more intercultural competence that respondents report with blacks, the better they are at distinguishing between black regular customers and black new shoppers in an experiment. The authors find no impact of intercultural competence on the ability of respondents to differentiate between white consumers. These findings hold for respondents in the USA and South Africa.

One limitation of this research is that the studies were conducted in a controlled lab setting. Thus, one could imagine additional noise from a true consumer setting might increase the effects of these results. Another limitation is the focus on only black and white consumer faces. In this paper, the authors focused on these two races, specifically to keep the factorial design as simplified as possible.

The implications of this research are important given that the ability of employees’ recognizing customer faces can affect customers’ day-to-day interactions in the marketplace.