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The purpose of this paper is to show the effect of Turkey's geopolitical risk on the number of international tourist arrivals to the country. When Turkish economy in 2019 is analyzed, it is seen that the share of tourism in national income is 11%. For this reason, national economy is significantly affected by changing of the number of international tourist arrivals. Security problems are an important variable affecting tourist arrivals.

The paper focused on secondary data for the period 2000–2019 for macroeconomic variables. Accordingly, the number of international tourist arrivals was added as a dependent variable, geopolitical risk as an independent variable, gross domestic product (GDP) and economic freedom index as control variables and inflations as an external variable to the model. The residual augmented least squares–the autoregressive distributive lag (RALS-ADL) cointegration test and the dynamic ordinary least squares (DOLS) coefficient estimator were used. It allows for more robust results to be obtained when the residues do not have a normal distribution.

The RALS-ADL cointegration test result shows that there is a cointegration relationship between variables at a 1% significance level. Moreover, the DOLS coefficient estimator results indicate that an increase in economic freedom and GDP increase the number of international tourists, whereas an increase in the Geopolitical Risk Index and inflation decreases the number of international tourism arrival. It can be said that tourists consider the security and economic stability of the host country when making tourism decisions.

Turkey is one of the most risky developing countries, as well as one of the most popular travel destinations. When the literature is examined, it has been found that studies for Turkey usually determine the relationship between the variables for a short period of time. However, to ensure sustainable growth and environment of confidence, the long-run relationship between variables should be determined so that policymakers can make more impactful decisions. Therefore, the aim of this study is to make a literature contribution, taking into account the long-term effects. In addition, unlike other studies, this study fills the gap in literature using the RALS-ADL cointegration test, which produces robust estimators.

The demand for electric vehicles (EVs) has significantly increased in recent years, though some countries like Sri Lanka have reported the opposite direction compared to the global trend. Hence, this study focused on identifying factors affecting EV purchase intention and barriers to the widespread adoption of EVs in a developing country context. Also, this study presents an overview of the theoretical perspectives utilized for understanding consumer intentions and adoption behavior toward alternative fuel vehicles (AFVs).

The questionnaire method was employed, and 394 individuals who lived in Colombo City, Sri Lanka, with valid driving licenses and a hybrid or conventional vehicle were the study sample. The partial least squares structural equation modeling (PLS-SEM) was used to test the research hypothesis.

The findings confirmed that the three relationships between the unified theory of acceptance and use of technology (UTAUT) variables and EV purchase intention are significant, and there is no significant moderator effect from the consumer’s perceived risk.

These results offer useful information for governments and EV companies to better understand consumer behavior toward purchasing EVs.

Successful project delivery for sustainable building construction (SBC) has been linked to certain features. Previous studies have emphasised the need to improve SBC practice in South Africa. The purpose of this study is to explore the SBC features for project delivery in South Africa.

A structured questionnaire elicited the primary data from 281 built environment professionals, mainly in South Africa’s Gauteng province. Descriptive and inferential statistics were used for the data analysis. This study used the principal component analysis technique to ascertain the principal SBC features.

Three components of SBC features, namely, sustainable resource use and compliance, sustainable waste minimisation and recycling and sustainable designs and materials, were developed from the principal component analysis. The factor loadings of the constituent variables ranged from 0.570 to 0.836. The reliability of each component was evaluated, and the results were 0.966, 0.931 and 0.913.

The revelations from this study will aid the decision-making of the relevant stakeholders towards establishing improvement initiatives and mitigating the reluctance to shift from conventional building methods and poor knowledge sharing of SBC benefits.

This is one of the most recent South African studies that sheds light on the components of a successful SBC deployment. The findings of this study added to knowledge by confirming three fundamental features of SBC. This study recommends adequately considering the principal features for successful SBC project delivery in South Africa.

Entrepreneurial ecosystems (EEs) research has focused on high-growth scale-up entrepreneurship, whereas the role of EEs in nurturing the ventures of marginalised groups like migrant women entrepreneurs (MWEs) has often been elided from extant discussions. This research explores how the EE's structure, policies and programmes advance diversity, equity and inclusion to foster MWEs, and MWEs' contribution to the dynamics and sustainability of the host country's EE based on EE actors' perspectives. We contribute to EEs' diversity, equity and inclusion, which are important but neglected social aspects of sustainable EEs.

The qualitative data was collected through thematic interviews with EE actors, including NGOs and entrepreneurial support-providing organizations based in Finland. The collected data was complemented by interviews with MWEs, archival data and published supplementary materials on ecosystem actors.

EE structure, policies, programmes and individual agency, coupled with MWEs' proactivity in lobbying the necessary actors in the required places for their interests, enhance their businesses' development. There were both impeding and fostering dynamics, which may have idiographic and contextual features. Evidently, by being occupied in various sectors, from science, technology, engineering and mathematics (STEM) to socially beneficial niche service sectors, MWEs contribute to the host country's EE dynamics not only through their productive entrepreneurship but by enriching the ecosystem's resource endowments and institutional arrangements.

We argue that exploring the gender and inclusivity aspects of EEs as the accommodating context is particularly relevant, given that the United Nation's sustainable development goals 5, 8 and 10 aim to improve women's empowerment at all levels, promoting sustained, inclusive and sustainable economic growth, and ensuring equal opportunities and reduced inequalities within the population. Inclusion and embeddedness in EEs positively affect diversity and sustainability in the host country. Theoretically, our contribution is twofold. First, by exploring female migrants' entrepreneurial experiences within the EE based on EE actors' perspectives, we broaden the research on inclusivity in EEs and gender aspects and enrich the research on their societal impact, which has received scant attention from scholars. More specifically, we contribute to EE research with (1) a novel understanding of MWEs and EE elements, their interconnections and dynamism, (2) identifying previously ignored elements shaping MWE and (3) providing EE actor insights into the co-creation of EE for MWE. Second, by analysing the impact of MWEs' businesses on the host country's EE, we contribute to calls for research on MWE contributions to its economic environment.

Adopting adaptive behavior has become a basic measure for farmers because the increasingly severe climate change is affecting agricultural production. Perception is a critical first step in adopting adaptive behaviors. Livelihood resilience represents a farmer's ability to adapt to climate change. Therefore, this article aims to explore the impact of livelihood resilience and climate change perception on the climate change adaptation behavior of farmers in the Qinling Mountains region of China.

In this study, 443 micro-survey data of farmers are obtained through one-on-one interviews with farmers. The Logit model and Poisson regression model are used to empirically examine the impact of farmers' livelihood resilience and climate change perception on their climate change adaptation behaviors.

It was found that 86.68% of farmers adopt adaptive behaviors to reduce the risks of facing climate change. Farmers' perception of extreme weather has a significant positive impact on their adaptive behavior under climate change. The resilience of farmers' livelihoods and their perception of rainfall have a significant positive impact on the intensity of their adaptive behavior under climate change. Climate change adaptation behaviors are also different for farmers with different levels of livelihood resilience.

Based on the results, policy recommendations are proposed to improve farmers' perception of climate change, enhance the sustainability of farmers' adaptive behavior to climate change, strengthen emergency management and infrastructure construction and adjust and upgrade farmers' livelihood models.

Amidst escalating sustainability challenges, product and brand managers face a pressing need to foster responsible consumption and marketing strategies. Guided by the theory of planned behavior, this paper aims to explore consumers’ motivation to purchase second-hand clothing, a type of product that contributes to Sustainable Development Goal (SDG) 12 on Responsible Consumption and Production by democratizing the brand, extending the life-cycle of the product, promoting a circular economy, while reducing economic costs for consumers and environmental costs for companies.

A two-stage study was conducted: 20 consumers were initially interviewed to identify the salient beliefs about second-hand clothing, and following that, a survey was conducted with 449 consumers to statistically analyze consumers’ motivation to purchase second-hand clothing. The data were analyzed using partial least squares-structural equation modeling (PLS-SEM) and necessary condition analysis (NCA).

From a “should-have” perspective (PLS-SEM), the study reveals that behavioral beliefs, injunctive normative beliefs, descriptive normative beliefs and control beliefs positively shape attitudes, injunctive norms, descriptive norms and perceived behavioral control toward second-hand clothing, whereas attitudes, injunctive norms, moral norms and perceived behavioral control positively influence consumers’ purchases of second-hand clothing. From a “must-have” perspective (NCA), the study shows that behavioral beliefs, injunctive normative beliefs and descriptive normative beliefs are necessary conditions to positively shape attitudes, injunctive norms and descriptive norms toward second-hand clothing, whereas attitudes, injunctive norms and perceived behavioral control are necessary conditions to stimulate second-hand clothing purchases.

The study offers a deep dive into consumers’ motivation to purchase second-hand clothing using a multimethod approach that enables not only the elicitation of salient beliefs (through interviews) but also the empirical examination of these beliefs alongside varying subjective norms in motivating consumers to purchase second-hand clothing (via survey). Given that beliefs are deeply rooted, the rigorous unfolding and validation of consumers’ beliefs about second-hand clothing, including the “should-haves” versus the “must-haves,” provide finer-grained insights that product and brand managers can strategically use to encourage consumers to purchase second-hand clothing.

Researchers looking for ways to change the insecure behaviour that results in phishing have considered multiple possible reasons for such behaviour. Therefore, the purpose of this paper is to understand the role of optimism bias (OB – defined as a cognitive bias), which characterises overly optimistic or unrealistic individuals, to ensure secure behaviour. Research that focused on issues such as personality traits, trust, attitude and Security, Education, Training and Awareness (SETA) was considered.

This study built on a recontextualized version of the theory of planned behaviour to evaluate the influence that optimism bias has on phishing susceptibility. To model the data, an analysis was performed on 226 survey responses from a South African financial services organisation using partial least squares (PLS) path modelling.

This study found that overly optimistic employees were inclined to behave insecurely, while factors such as attitude and trust significantly influenced the intention to behave securely.

Our contribution to practice seeks to enhance the effectiveness of SETA by identifying and addressing the optimism bias weakness to deliver a more successful training outcome.

Our study enriches the Information Systems literature by evaluating the effect of a cognitive bias on phishing susceptibility and offers a contextual explanation of the resultant behaviour.

Recent evidence from a linear econometric framework infers that housing search activity, captured from Google Trends data, can predict housing returns for the USA at a national and regional (metropolitan statistical area [MSA]) level. Based on search theory, the authors, however, postulate that search activity can also predict housing returns volatility. This study aims to explore the possibility of using online search activity to predict both housing returns and volatility.

Using a k-th order non-parametric causality-in-quantiles test allows us to test for predictability in a robust manner over the entire conditional distribution of both housing price returns and its volatility (i.e. squared returns) by controlling for nonlinearity and structural breaks that exist in the data.

The analysis over the monthly period of 2004:01 to 2021:01 produces results indicating that while housing search activity continues to predict aggregate US house price returns, barring the extreme ends of the conditional distribution, volatility is relatively strongly predicted over the entire quantile range considered. The results carry over to an alternative (the generalized autoregressive conditional heteroskedasticity-based) metric of volatility, higher (weekly)-frequency data (over January 2018–March 2021) and to over 84% of the 77 MSAs considered.

To the best of the authors’ knowledge, this is the first study regarding predictability of overall and regional US housing price returns and volatility using search activity, based on a non-parametric higher-order causality-in-quantiles framework, which is insightful to investors, policymakers and academics.

We develop a credit-risk model to study the informational role of investment in an economy susceptible to large liquidity shocks. Firms' investment decisions carry information about their asset quality, thereby mitigating informational frictions when firms enter bankruptcy. An increase in aggregate investment can reduce the informational value of investment, depressing firms' recovery values. Therefore, policies boosting investment can decrease debt and firm values by reducing the informational value of investment. The presence of debt overhang may enhance firm value by making firms' investment decisions more informative. We present suggestive empirical evidence consistent with model predictions on the relation between firms' investments and recovery rates.

This article summarizes practitioner observations on three research questions. First, the factors that lead to the emergence and persistence of such teams. Second, the repercussions of siloed teams. And third, practical suggestions and recommendations that practitioners can employ to prevent silo formation or address existing silos. This article thus complements recent academic work that has previously explored the formation of silos.

The authors used the input of current team leads from a focus group along with their consulting experience to explore these three research questions. The team lead input and consulting expertise are integrated with academic research on silos.

The emergence and persistence of silos was mostly attributed to company characteristics (size, growth and stakeholder management) as well as communication inefficiencies (lack of role clarity and ownership within teams), which in turn were impacted by situational variables (pandemic and turnover). The authors noted the effect of team composition effects, team competition and organizational changes (rapid growth and restructuring) as potential contributors to the formation and persistence of silos. The team lead experts and our consulting experiences were congruent with the literature focused on repercussions of silos, from poor information exchanges to inefficiencies, divisions and perceived isolation of teams from the organization. Solutions focused on project organization and documentation as well as the adoption of new decision-making tools and practices, and the creation of more exchange and learning opportunities. The authors added additional options to promote more visibility, appreciation, proactive monitoring within teams and organizational identification initiatives.

The current article adds a pragmatic perspective to silos and how organizations can address these when they become problematic and hinder performance and collaboration.