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It has become increasingly apparent that one's perception of issues depends largely on his or her personal history. Human beings, professionals, and stakeholders vary in their perspectives, strategies, and solutions. Rather than arbitrarily selecting issues or seeking consensus among interdisciplinary but disparate groups, it is critical to examine the broader array of values and issues that impact learners with exceptionalities and the future of special education. In addition, it is important to conceptualize effective techniques to reverse traditional problems or difficulties while considering the “cycles” of change in general and special education. These cycles are based on the view that educational perspectives have their time since they come and go. When they are innovative, they become the status quo and become finally obsolete as the next group of methods, beliefs, and educational initiatives takes hold. In special education, we have fundamental values that must be respected and followed to deliver services to learners with exceptionalities. These values are sometimes not valued by ill prepared and unprepared professionals, especially since good professionals believe in change. This chapter focuses on how these values can be respected to protect and advance special education and education as a whole.

We explore the relationship between urbanicity and police officers’ perceptions of changes in their reactive and proactive work during the COVID-19 pandemic.

Using the 2021 survey of 1,262 Croatian police offices (436 police officers from a large urban community, 471 police officers from small towns and 155 from rural communities), we examine the perceived changes in their reactive activities (e.g. responses to the calls for service, arrests for minor crimes) and proactive activities (e.g. community policing activities, directed patrols) during the peak month of the pandemic compared to before the pandemic.

The majority of police officers in the study, regardless of the size of the community where they lived, reported no changes before and during the pandemic in reactive and proactive activities. Police officers from urban communities and small towns were more likely to note an increase in domestic violence calls for service. Police officers from urban communities were also more likely than the respondents from small towns and rural communities to report an increase in the responses to the disturbances of public order. Finally, police officers from small communities were most likely to observe a change in the frequency of traffic stops during the pandemic.

This study is the first one to explore the differences in perceptions of COVID-19-related changes in reactive and proactive police activities in a centralized police system.

The world is increasingly threatened by climate change. As the dimensions of this danger grow, it becomes essential to develop the most effective policies to mitigate its impacts and adapt to these new conditions. Technology is one of the most crucial components of this process, and this study focuses on examining climate change adaptation technologies. The aim of the study is to investigate the entire spectrum of technology actors and to concentrate on the technology citation network established from the past to the present, aiming to identify the core actors within this structure and provide a more comprehensive outlook.

The study explores patent citation relationships using social network analysis. It utilizes patent data published between 2000 and 2023 and registered by the US Patent and Trademark Office.

Study findings reveal that technologies related to greenhouse technologies in agriculture, technologies for combatting vector-borne diseases in the health sector, rainwater harvesting technologies for water management, and urban green infrastructure technologies for infrastructure systems emerge as the most suitable technologies for adaptation. For instance, greenhouse technologies hold significant potential for sustainable agricultural production and coping with the adverse effects of climate change. Additionally, ICTs establish intensive connections with nearly all other technologies, thus supporting our efforts in climate change adaptation. These technologies facilitate data collection, analysis, and management, contributing to a better understanding of the impacts of climate change.

Existing patent analysis methods often fall short in detailing the unique contributions of each technology within a technological network. This study addresses this deficiency by comprehensively examining and evaluating each technology within the network, thereby enabling us to better understand how these technologies interact with each other and contribute to the overall technological landscape.

This study aims to explore the impact of women empowerment and gender-stereotypical advertising exposure on consumers’ attitudes and purchase intents.

The quantitative research approach was used to assess the women-empowerment and gender-stereotypical advertising on consumers’ attitudes and purchase intentions, explicating the most effective approach in marketing communication in the Arab context, specifically Egypt. Administrated questionnaires were distributed online cross-gender through the use of convenience sampling. The targeted average sample size sought was minimum of 370. Once the data was acquired, partial least square path modelling of structural equation modelling was conducted to test the hypotheses.

Results show that male consumers and female consumers react to a certain extent differently to women-empowerment and gender-stereotypical advertisings. For instance, gender-stereotypical advertisings effect attitude for both genders; it effects male consumers’ purchase intentions, but not female consumers. Women empowerment advertising is insignificant on brand attitude and purchase intention among male consumers; among female consumers, it has significant impact on brand attitude, but not purchase intention. Nevertheless, both categories of advertising contribute to male consumers’ and female consumers’ perception and attitude towards gender role portrayal.

This study provides noteworthy insights and perspectives on women-empowerment (Femvertising) and gender-stereotypical advertising effects on cross-gender consumers in the Arab region (a region of strong masculine culture values). The emerged conclusions aid as a reference and guide for scholars and practitioners to develop effective advertising that create prospect depictions and build inspired female customer base.

Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

Research on the relationship between customer bargaining power and supplier performance in supplier–customer relationships has flourished in recent decades. This study aims to empirically investigate whether product market overlap (PMO) in a supply chain moderates the effect of customer bargaining power on supplier profitability.

This study uses large-scale secondary data from multiple databases. Econometric panel data techniques are used to test the hypotheses.

The results show that PMO in a supplier–customer relationship and PMO in supplier–supplier relationships both exacerbate the negative effect of the bargaining power of customers on supplier profitability.

This study contributes to the field of supply chain management. This study brings new insights into the ongoing debate surrounding the relationship between customer bargaining power and supplier profitability. The study also contributes to the literature on supply chain networks by showing the impact of indirect supply chain relationships.

What kinds of support do interstate rivals provide to domestic actors in ongoing civil wars? And how do domestic actors utilize the support they receive? This chapter answers these questions by comparing Iranian and Saudi military and non-military (mediation, foreign aid and religious soft-power promotion) support to the Houthis and to the Government of Yemen (GoY) during the Saada wars (2004–2010) and the internationalized civil war (2015–2018). It also focuses on the processes through which the GoY and the Houthis have utilized this support for their own strategic purposes. This chapter applies a structured, focused comparison methodology and relies on data from a review of both primary and secondary sources complemented by 14 interviews. This chapter finds that there were less external interventions in the conflict in Saada than in the internationalized civil war. During the latter, a broader set of intervention strategies enabled further instrumentalization by domestic actors, which in turn contributed to the protracted nature of the conflict. This chapter contributes to the literature on interstate rivalry and third-party intervention. The framework of analysis is applicable to civil wars that experience intervention by rivals, such as Syria or Libya.