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The purpose of this paper is to focus on challenges faced by longitudinal quantitative analyses of school improvement processes and offers a systematic literature review of current papers that use longitudinal analyses. In this context, the authors assessed designs and methods that are used to analyze the relation between school improvement processes and student outcomes. Based on this the authors point out to what extent the papers consider different aspects of the complex nature of school improvement (e.g. multilevel structure, indirect and nonlinear effects, reciprocity). The choice of study designs and methods of analysis substantially determines which aspects of this complexity are taken into account.

The authors searched in four international high-impact journals and in ERIC for articles reporting longitudinal school improvement studies. The database of the review consisted of a total of 428 journal articles. In total, 13 of the 428 papers met the selection criteria and were analyzed in detail.

The analyzed papers use a wide range of designs and methodological approaches. They support the assumption that sophisticated quantitative longitudinal designs and methods can be applied effectively in school improvement research. However, considering the complexity of school improvement is accompanied by high demands on designs and methods. Due to this none of the papers met the standards applied in this review completely.

In particular, further research is needed to consider a long period of observation, reciprocal indirect and nonlinear processes in a multilevel structure. Moreover, research is required for a better and unambiguous theoretical foundation and empirical validation of the number of and intervals between measurement points.

If more consideration is given to the complex nature of school improvement in future studies, the broader knowledge base will allow a better understanding of the dynamic relation of school improvement and student learning. It would thus be possible to make more appropriate recommendations for the support of school improvement practice.

The original contribution of the paper is to show which aspects of the complexity of school improvement processes – and to what extent – are currently addressed in designs and methods of analysis applied in quantitative longitudinal studies that investigate the relation between schools’ capacity to managing change and student outcomes. Additionally the authors aim at deriving need for further research and giving guidelines how designs and methods in further studies can reflect the complexity appropriately. It is highly important to consider all aspects of this complexity to describe and understand the dynamic relation of school improvement processes and student outcomes.

Longitudinal investigations are often suggested but rarely used in operations and supply chain management (OSCM), mainly due to the difficulty of obtaining data. There is a silver lining in the form of existing large-scale and planned repeated cross-sectional (RCS) data sets, an approach commonly used in sociology and political sciences. This study aims to review all relevant RCS surveys with a focus on OSCM, as well as data and methods to motivate longitudinal research and to study trends at the plant, industry and geographic levels.

A comparison of RCS, panel and hybrid surveys is presented. Existing RCS data sets in the OSCM discipline and their features are discussed. In total, 30 years of Global Manufacturing Research Group data are used to explore the applicability of analytical methods at the plant and aggregate level and in the form of multilevel modeling.

RCS analysis is a viable alternative to overcome the confines associated with panel data. The structure of the existing data sets restricts quantitative analysis due to survey and sampling issues. Opportunities surrounding RCS analysis are illustrated, and survey design recommendations are provided.

The longitudinal aspect of RCS surveys can answer new and untested research questions through repeated random sampling in focused topic areas. Planned RCS surveys can benefit from the provided recommendations.

RCS research designs are generally overlooked in OSCM. This study provides an analysis of RCS data sets and future survey recommendations.

The purpose of this paper is to provide a systematic overview with guidelines how to use partial least squares (PLS) path modeling in longitudinal studies. Practical examples from a study of the acceptance of battery electric vehicles (BEVs) in corporate fleets are used for demonstration purposes.

In this study, data at three points in time were collected: before the initial use of a BEV, after three and after six months of extensive usage of BEVs.

Three different models are identified depending on the research objective and on the data basis. Multigroup analyses are suggested to test the difference between the path coefficients of latent variables at different points in time. Limitations for the use of repeated cross-sectional data have to be observed.

Academics and practitioners will benefit from this paper by receiving an overview of the different PLS path models in longitudinal studies. A decision-tree enables them to make a choice regarding the most appropriate model and suggests a sequence of complementary analyses. So far, there is a lack of a tutorial type paper delivering such guidance.

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.

This paper aims to propose a method for the longitudinal analysis of the environment considering both firms' and environmental variables.

The study is based on a sample of firms in Canary Islands (Spain) for 2000 and 2003. Managerial perceptions are considered, based on the cognitive perspective. The measurements used are the result of applying the Rasch model and the rack and stack analyses. This approach provides information about how dynamic the firms perceive the environment and also about how the items are perceived.

The results show that most firms perceive that dynamism increased between 2000 and 2003. From the perspective of the environmental variables, the most dynamic are perceived to be competition, demand, consumer motivation and technological resources.

This paper proposes a longitudinal method for environmental scanning that include both firms' and environmental variables. It considers managerial perceptions, that is the information entering the decision making process. It is one of the first papers to study environmental scanning with Rasch model and one of the few about longitudinal environmental analyses. It opens a field of research and applications of the Rasch model in the management literature.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

To report on longitudinal change in corporate real estate (CRE) practice in the last two decades, in particular, monitoring significant changes in CRE policy, function and activities through the statistical analysis of annual survey data.

A literature review revealed the major themes that have influenced CRE practice in the last 20 years. Applying principal components analysis the annual survey of CRE practice dataset was analysed to identify significant tends in the CRE practices reported by organizations.

A significant overlap was observed between the activities used in practice and the CRE literature, and new working practices were identified as a significant theme. Based on these analyses comment is made on the impact of changes in CRE practice over the timescale of the survey and it was predicted that new working practices will continue to influence CRE practices in the future.

This paper fulfils a need for evidence‐based monitoring of CRE practices to give insight into the relationship between past, present and indicative directions for future CRE practice.

The purpose of this work is to explore what determines a country’s entrepreneurial environment attractiveness, by understanding how countries compare regarding their business environment and entrepreneurial opportunities and whether such aspects have changed over time.

Through a longitudinal country-level cluster analysis of business environments (years 2001 and 2016), this study captures changes in classification of both emerging and developed market economies throughout an attractiveness spectrum, from least to most attractive environments.

Interesting findings involve the difference in trajectories of emerging economies, such as India compared to the stagnation of Brazil, Argentina and South Korea in the 15-year period. The paper seeks to contribute to the debate on the attractiveness of the entrepreneurial environment beyond the simple notion of most and least economically developed countries by providing a framework for dynamic cross-country analysis of entrepreneurial environmental attractiveness that can be further explored, tested and expanded.

Main limitations relate to the non-exhaustive sample of countries and variables. Contributions are both academic and managerial: helping to fill important research gaps in international entrepreneurship, namely, environmental conditions, cross-country comparisons (Coombs et al., 2009) and the understanding of elements of the investment climate (Stern, 2002); and assisting managers, entrepreneurs and policymakers understand what defines a country’s entrepreneurial environment attractiveness to better evaluate potential locations for investment.

The originality of the paper lies in using cluster analysis in a longitudinal study of country attractiveness, as well as in advancing the debate of country attractiveness, by adding a temporal dimension (from factors that are less structural to more conjunctural) and a comparative dimension in a new cross-country comparison framework of analysis.

The purpose of this paper is to present an analysis of media representation of business ethics within 62 international newspapers to explore the longitudinal and contextual evolution of business ethics and associated terminology. Levels of coverage and contextual analysis of the content of the articles are used as surrogate measures of the penetration of business ethics concepts into society.

This paper uses a text mining application based on two samples of data: analysis of 62 national newspapers in 21 countries from 1990 to 2008; analysis of the content of two samples of articles containing the term business ethics (comprised of 100 newspaper articles spread over an 18‐year period from a sample of US and UK newspapers).

The paper demonstrates increased coverage of sustainability topics within the media over the last 18 years associated with events such as the Rio Summit. Whilst some peaks are associated with business ethics scandals, the overall coverage remains steady. There is little apparent use in the media of concepts such as corporate citizenship. The academic community and company ethical codes appear to adopt a wider definition of business ethics more akin to that associated with sustainability, in comparison with the focus taken by the media, especially in the USA. Coverage demonstrates clear regional bias and contextual analysis of the articles in the UK and USA also shows interesting parallels and divergences in the media representation of business ethics.

A promising avenue to explore how the evolution of sustainability issues including business ethics can be tracked within a societal context.

A linearized analytic model of general longitudinal movement of airplane is discussed. Linearized deviation equations of motion, derived in stability axes system, are applied to the parametric analysis of airplane's longitudinal dynamic stability under arbitrary symmetric flight conditions. The analytic model is generated in the form of interactive software package PODYST_ALFA. This is used to show the influence of some constructional and mass parameters on the longitudinal airplane motions after encountering disturbance for a chosen steady level flight.