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During the COVID-19 pandemic, lockdowns were implemented to achieve two goals: (1) to reduce the number of COVID-19 cases and (2) to reduce the number of COVID-19 deaths. In this paper, the authors aim to look at empirical evidence on how effectively lockdowns achieved these goals among small island developing states (SIDS) and for one specific SIDS economy, Guam.

The authors reviewed existing studies to form two hypotheses: that lockdowns reduced cases, and that lockdowns reduced deaths. Defining a lockdown as a positive value for Oxford COVID-19 government response tracker, OxCGRT's stringency index, the authors tested the above hypotheses on 185 countries, 27 SIDS economies and Guam using correlation and regression analyses, and using different measures of the strictness, duration and timing of the lockdown.

The authors found no evidence to support the hypothesis that lockdowns reduced the number of cases based on data for all 185 countries and 27 SIDS economies. While the authors found evidence to support the hypothesis in the case of Guam, the result required an unrealistically and implausibly long time lag of 365 days. As to the second hypothesis that lockdowns reduced the number of deaths, the authors found no evidence to support it for 185 countries, 27 SIDS economies as well as Guam.

From the review of the existing literature, the authors are the first to conduct this type of study among SIDS economies as a group and on Guam.

This study aims to propose a new conforming tax measure based on the work of Badertscher et al. (2019).

This study divides total tax avoidance/management (TM) into nonconforming and conforming portions through a regression. The residual of the regression is treated as the conforming tax measure. In addition, the new conforming tax measure is validated via three approaches. Then, this study examines the moderating effect of nonconforming earnings management (EM) on the relationship between conforming TM and firm performance.

The empirical results show that the model has stronger explanatory power than the model proposed by Badertscher et al. (2019). Additionally, the validation results show that the mean value of the conforming tax measure is lower in quasi-private corporations (financially constrained companies) than in matched public corporations (nonfinancially constrained companies), and firms under high market capital pressure are less motivated to engage in conforming tax practices. Furthermore, nonconforming EM positively moderates the conforming tax–ROA association, implying that nonconforming EM can reduce financial reporting costs resulting from conforming tax practices.

This study contributes to conforming tax research in the following ways. First, this study proposes a new conforming tax measure by substituting the cash book tax difference (BTD) for the BTD in the model of Badertscher et al. (2019) (“BKRW”). Second, this study demonstrates theoretically why the cash BTD should outperform the BTD in computing the BKRW conforming tax measure and confirm this empirically. Third, this study presents a three-way conceptual schema that divides corporations into two groups along each of three tax-relevant dimensions. The group of firms that use both conforming and nonconforming tax strategies have different characteristics compared to the other group. This study also validates the conforming tax measure across the two-group dichotomies.

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.

The aim of this study is to investigate the role of Corporate Social Responsibility (CSR) in assisting firms in their response to public emergency crises under the integrated view of government emergency response.

Using event study and survival analysis method, the authors examine whether CSR can act as a stock price stabilizer for companies from China by splitting the stock price fluctuations into two phases – CSR price insurance, which decrease the shock on stock prices during the emergency crisis, and CSR price recovery, which helps stock prices rebound faster during the postcrisis phase.

The authors’ empirical results confirm the stabilizer role of CSR during crisis and that effective government response can strengthen such effect. Furthermore, the authors examine the different aspects of the government’s response and the impact of multiple waves of public emergency.

This study provides empirical evidence on the topic of CSR and the government’s response to public emergency under the emerging context.

This study aims to investigate the existence of bubbles and their contagion effect in crude oil and stock markets of oil-exporting countries Gulf Cooperation Council (GCC) from 2016 to 2021.

The authors use Generalized Sup augmented Dickey–Fuller (GSADF) and Backward Sup augmented Dickey–Fuller (BSADF) to significantly identify multiple bubbles stock and oil markets with precise dates. Furthermore, the authors check the contagion effect of bubbles between crude oil and GCC stock markets based on the time-varying Granger causality test.

First, the authors find empirical evidence of downwards bubbles in crude oil prices and in all GCC stock indexes (except the Saudi stock index) during the corona virus disease 2019 (COVID-19) outbreak. Second, the authors do not detect empirical evidence of bubble transmission between crude oil markets and GCC stock markets (except with the Dubai Financial Market index).

The findings of this study would illuminate policymakers not to limit the factors of systematic financial crises in oil-exporting countries to crude oil and to consider factors such as monetary policy and economic diversification measures. This study has also crucial implications for investors. In fact, investors should not ignore the responses of the stock markets to oil price shocks that are heterogeneous across countries when looking for investment opportunities in the GCC region.

The study justifies the changing nature of the bubble contagion effect through the novel implementation of the time-varying Granger causality test to detect whether bubble contagion exists between oil and GCC stock markets and if that does, in which direction.

The study aims to find the synchronization between foreign agriculture investment (FAI) and Sustainable Development Goals (SDGs) related to agriculture as classified by the Food and Agriculture Organization (FAO). The study tries to find such an association in India over 2 decades from 2001.

The Toda-Yamamoto Granger using the M-Wald test for the non-causality procedure is applied to find the synchronization. Stationarity is tested using the Augmented Dickey-Fuller, Phillips-Perron and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) tests. The Johanson methodology with MacKinnon-Haug-Michelis P-value is employed for the Cointegration test.

The empirical results indicate that the FAI Granger cause SDG2 “Zero hunger” and “Overall sustainability”, but SDG13 “Climate Change”, SDG6 “Clean water and sanitation”, SDG12 “Responsible production and consumption” and SDG15 “Life on Land” granger cause global investments. Notwithstanding, SDG5 “Gender equality” and SDG14 “Life below water” found no-way causality with FAI.

Host governments should prioritize sector-level sustainable development, notably agricultural SDGs, to attract global investments. Foreign agriculture investment is influenced differently by various SDGs; thus, policymakers should concentrate on specific agricultural SDGs to enhance the flow of capital into the agriculture sector. Global investors should take sustainability into account while framing foreign investment plans, and the supra-national organization may consider global agricultural investments while addressing the problems related to global food security.

The distinguishing feature of the study is that SDGs classified by the FAO from a global investment perspective have not been studied so far.

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.

The use of brand slogans that represent brand concepts on app launch pages can improve user brand impressions. The purpose of this paper is to investigate the impact of using animated or static spokes-characters with brand slogans on app launch pages.

Using the theory of attention selection, the authors conducted two experiments to study the boundary and mediation path of the influence of the motion attributes of spokes-characters (static vs animated) on brand memory based on app launch time (3 s vs 5 s), user engagement with spokes-characters and the level of attention to brand slogans.

Study 1 explores the effect of the interaction between launch time and the motion attributes of spokes-characters on brand memory. The results show that when the launch time of the app is 3 s, the advertisement memory effect of using a static spokes-character is better than that of using an animated spokes-character; when the launch time of the app is 5 s, the advertisement memory effect of using an animated spokes-character is better than that of using a static spokes-character. Study 2 shows that user engagement with spokes-characters and the level of attention given to brand slogans play a continuous mediating role in the effect of the interaction between launch time and the motion attributes of spokes-characters on brand memory.

This paper contributes to the marketing literature by expanding the knowledge of spokes-characters and animated visual images, providing new insights for future research.

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.