This study examines the optimal location for container yard development in Northeastern Thailand to enhance multimodal transport connectivity and strengthen national competitiveness in regional and global trade. Utilizing the analytic hierarchy process (AHP) and geographic information system (GIS), the study evaluated 18 railway stations based on five key factors: logistics suitability (13.5%), infrastructure readiness (14.6%), government support (22.5%), railway network capabilities (16.9%), and investment economics (32.5%). The findings identify Nata Station in Nong Khai Province as the most strategically advantageous location, with the highest composite weighted Z-score of 0.8775. The station features a road network density of 0.85 km per square kilometer and a railway density of 0.32 km per square kilometer, facilitating efficient cross border freight transportation. The establishment of a container yard in this optimal location is expected to reduce logistics costs, improve supply chain efficiency, and enhance cross border trade, particularly with member states of the Association of South East Asian Nations (ASEAN) trading bloc and China. This development reinforces the role of Thailand as a regional logistics hub, fostering industrial expansion and economic integration. Furthermore, government backed policies, including investment incentives and infrastructure development, contribute to building a resilient and competitive logistics network. The study provides a strategic framework for policymakers and private sector stakeholders to leverage logistics infrastructure in strengthening the long-term competitiveness of Thailand in global trade.
Considering evolving market dynamics, high fixed costs and vulnerability to external shocks, such as the impact of the ongoing global pandemic, it has become increasingly important to assess profitability in the accommodation sector. This study proposes a composite profitability index for accommodation services in Slovakia, which integrates traditional financial indicators such as return on equity (ROE), return on assets (ROA) and return on sales (ROS), as well as broader indicators such as the value-added-to-sales ratio (VAS) and EBITDA margin. Each indicator captures a distinct aspect of financial health, from operational efficiency to capital utilisation and value creation. To ensure methodological robustness, the study employs three expert-based weighting techniques: the exact Saaty method, the approximate Saaty method and the best-worst method. A panel of 14 experts in economics, finance and hospitality management participated in a Delphi-based survey to evaluate the relative importance and optimal values of each indicator. All methods ranked ROA as the most critical metric, followed by ROS and ROE, meanwhile, EBITDA margin and VAS were considered less significant, yet still essential, for a comprehensive assessment. Using sectoral data and applying normalised scoring, the index captures profitability trends from 2020 to 2023. The aim of the study is to develop a composite profitability index for the accommodation services sector in Slovakia, based on the identified limitations of traditional indicators. The results show a marked post-pandemic recovery, with profitability index scores rising steadily across all weighting methods. Notably, the best-worst method yielded consistently higher index values, indicating its sensitivity to prioritisation extremes. This composite index provides accommodation businesses and policymakers with a practical tool for benchmarking, strategic planning and improving long-term financial sustainability in a volatile and competitive environment, offering a multidimensional framework for evaluating financial performance.
Exchange-rate dynamics are non-linear and volatile, which challenges conventional forecasting approaches. This study evaluates a reproducible long short-term memory (LSTM) framework for daily EUR/USD, GBP/USD, USD/TRY, and USD/JPY over 1 January 2010 to 31 December 2021. The contribution is twofold: (i) a fully specified and deployment-oriented LSTM protocol (architecture, preprocessing, and leakage-safe validation) suitable for applied forecasting; and (ii) a time-series-appropriate evaluation that combines rolling-origin (walk-forward) testing with standard baselines (random walk and ARIMA) and diagnostic visualizations. Forecast performance is reported using root mean square error (RMSE), mean absolute error (MAE), Pearson correlation (R), Nash-Sutcliffe efficiency (NSE), and the RMSE-to-SD ratio (RSR), alongside distributional diagnostics (violin plots) and horizon-specific error profiles. The results quantify performance gains relative to baselines under leakage-safe evaluation, while highlighting practical implications for treasury and risk management. Limitations include the exclusion of exogenous drivers and longer-horizon tests, motivating extensions that incorporate macro-financial signals and interpretability modules.
This study addresses a critical gap in the literature by examining how family-based brand identity and entrepreneurial orientation (specifically, proactiveness and risk taking) influence the performance of small family firms operating in emerging markets. Grounded in the resource-based view (RBV), these constructs are conceptualized as strategic intangible resources that underpin competitive advantage. A cross-sectional survey of 460 privately held small family firms in Pakistan was conducted, using validated scales for family-based brand identity, entrepreneurial orientation confirmatory factor analysis and structural equation modeling (SEM) in AMOS 24.0 to assess direct and mediating relationships. The findings reveal that cultivating a family-based brand identity and entrepreneurial orientation significantly contributes to improved firm performance by recognizing the family as the brand and EO as family firm resources via the RBV, by assessing their impact on performance, and by mediating the effect of innovativeness. The study extends RBV theory through the identification of family-based brand identity and EO as firm-specific resources in small family firms and demonstrates innovativeness as a mediator.
As environmental challenges intensify globally, green innovation has become a strategic imperative for firms seeking to balance ecological responsibility with sustained competitiveness. We examine whether green innovation improves firm performance in China's manufacturing sector. Using panel data on 1,033 A-share manufacturers (2014-2023; 10,330 firm-years), we estimate two-way fixed-effects models (firm and year fixed effects (FE)) with cluster-robust standard errors (SE). Green innovation is measured by green patent outputs; firm performance is proxied by average return on equity (ROEAVG), with return on invested capital (ROIC) as a robustness outcome. Results show a positive and statistically significant association on average, with stronger effects in more developed regions. Findings remain under lagged-specification checks and alternative outcome measures. Conceptually, we extend the resource-based view by framing green innovation as a capability bundle whose payoff is conditioned by institutional context and absorptive capacity. Practically, predictable enforcement and disclosure raise both the quantity and influence of green patents and improve market pricing of eco-innovation; targeted, adoption-cost-reducing incentives tied to substantive innovation help curb greenwashing.
Poverty is not only a problem within a region but also a problem that affects multiple regions. The current efforts to alleviate poverty have primarily focused on the household and community level, often neglecting the crucial role that spatial dimensions play in understanding poverty dynamics. The theory of the vicious circle of poverty still needs attention from the whole community and the government, because Indonesia is committed to alleviating poverty. This study examines the significance of spatial considerations in poverty alleviation efforts across 117 districts on Java Island. The research aims to provide recommendations for poverty alleviation policies considering spatial dimensions, including regional conditions and interactions. It was found that almost all macroeconomic variables are spatially dependent, except inflation and health spending. The Moran I measure revealed that the spatial correlation was considerable. It was determined by utilizing spatial econometric techniques. Specifically, with the spatial autoregressive and spatial Durbin model methods, poverty incidence on Java Island highly depends on spatial factors. The study indicated that investing in education in neighboring areas and growing industrial sectors considerably lower poverty within a specific district. The report recommends that policies be executed in a coordinated manner to effectively reduce poverty on Java Island, focusing on industrial and human development through education spending. The spatial network parameters indicate that the impact of these variables is still relatively small, but the effect is specific and accurate. Based on the study’s results, several suggestions for addressing poverty in Java are provided. The government should improve the connections between different areas (provinces, districts, and cities) to increase access among regions.
Innovation outcomes are shaped by both institutional conditions and firms' internal capabilities, yet prior research often examines these dimensions separately and rarely distinguishes between different types of innovation. This limits understanding of how external barriers and organizational resources jointly influence incremental versus market-novel innovation. To address this gap, the aim of this paper is to examine how institutional constraints, specifically perceived corruption and perceived labor regulation, and firm-level capabilities, measured by labor productivity growth, gender diversity in top management, and firm size (as a control), jointly shape firms' innovation performance. Grounded in a unified institutional and resource-based view (RBV) framework, we test how de facto institutional frictions and internal capabilities differentially relate to incremental versus market-novel innovation. Using harmonized World Bank Enterprise Survey data from 2019-2023 covering EU-27 countries (5,534 firms), we estimate two ordinary least squares (OLS) models to explain cross-country differences in: (i) the share of firms introducing new products or services (incremental innovation); and (ii) the share of firms whose innovations are new to the main market (market-novel innovation). All estimates are at the country level (EU-27; N = 27) and are interpreted as between-country associations in the national share of firms innovating. Accordingly, we interpret results as cross-country patterns and avoid firm-level causal claims. The results reveal a clear divergence in determinants across innovation types. Perceived corruption significantly reduces incremental innovation but does not affect market novelty. Labor regulations do not influence incremental innovation yet show a positive association with market novelty, consistent with a compliance-induced innovation mechanism. Productivity growth displays a dual effect: it suppresses incremental innovation but enables market novelty through greater organizational slack. Gender diversity in top management and firm size further strengthen market-novel innovation, while showing no effect on incremental outcomes. The study contributes to institutional theory by demonstrating that governance weaknesses primarily constrain routine innovation and refines RBV by showing that internal capabilities-productivity, organizational scale and leadership diversity are decisive drivers of market-novel innovation. By linking institutional environments with firm-level resources, the study provides an integrated explanation of innovation heterogeneity across EU economies.
City branding strategies have become increasingly prevalent in global competition, yet empirical evidence regarding their actual effects on sustainable tourism development and underlying pathways remains limited. This study examines the impact of National Civilized City designation on sustainable tourism development using panel data from 281 Chinese cities spanning 2002-2022 and employing a staggered difference-in-differences approach. Multiple robustness tests confirm the reliability of the findings. Results demonstrate that obtaining National Civilized City designation significantly enhances urban sustainable tourism development levels. Further analysis reveals that city branding operates through two pathways: enhancing tourism market attractiveness and stimulating tourism entrepreneurial vitality, forming a "dual-wheel drive" mechanism. Specifically, the designation increases tourism arrival rates and the number of newly established tourism enterprises. Moreover, service-oriented cities and highly digitalized cities experience stronger promotional effects. The findings indicate that authentic city brand certifications backed by genuine institutional improvements can advance sustainable tourism development through identifiable pathways, providing empirical insights for urban managers to formulate context-specific branding strategies.
This study examines the impact of corporate environmental, social and governance (ESG) performance on capital market efficiency, namely stock liquidity. Drawing on a dataset of 1,693 publicly traded firms across 23 European stock exchanges between 2002 and 2022, we investigate whether companies with stronger ESG credentials benefit from more liquid equity markets. Our results provide strong evidence that ESG performance has a significant positive effect on stock liquidity. This suggests that a substantial portion of European investors today prefer to invest in companies that act environmentally friendly, value social rights and equality, and engage in good governance. Among the three ESG dimensions, the environmental component exhibits the most significant effect, indicating that investors particularly value firms that engage in environmentally responsible practices. Furthermore, the analysis reveals notable regional differences intheimportance assigned toESGfactors, showing that investors inWestern and Northern European countries show a stronger preference for sustainability-oriented firms compared to their counterparts in Eastern and Southern Europe. These findings highlight the influence of regional economic, cultural, and regulatory contexts on investment behavior. Overall, our study contributes to the growing literature on sustainable finance by underscoring the role of ESG performance not only as a tool for ethical or reputational enhancement but also as a mechanism that can directly improve financial market outcomes. By identifying ESG performance as a determinant of stock liquidity, this research also supports the integration of ESG considerations into investment decision- making and corporate strategy.
The aim of this paper is to determine the optimal advertising strategies for different grids of newly introduced transformed nine-quadrants concept of Foote, Cone and Belding (FCB) model. The author's findings from prior studies on the FCB model, within the context of current conditions in Czechia, suggest that adapting the traditional FCB model is essential. Primary data for this study was gathered through an online questionnaire conducted by the Ipsos research agency, with responses from 1,050 participants from Czechia. The research utilized a modified FCB grid (based on previous studies), positional mapping, and chi-square testing with a post-hoc analysis. The authors propose four major advertisement strategies that are coupled with their corresponding representative product categories. The preference of various media types, emotional and rational appeals, and advertising frequency is discussed for sample identifiers-gender, age, and education. The age group 56-65 years and consumers with university education show the most polarized results in their advertising preferences. Despite the fact that the FCB model is well known, it is rarely applied in scientific research and practical use. Authors' scientific outputs point to this fact and present transformed FCB model.
In the context of growing global emphasis on sustainable business practices, understanding the financial drivers of corporate sustainability has become increasingly important. This study examines key financial and market indicators to understand their influence on corporate sustainability disclosure. Focusing on Romanian companies, it investigates how market share, financial ratings, probability of insolvency, and market value shape sustainability outcomes. The dataset was sourced from Azores Sustainability and CSR Services, a well-known organization in Romania that offers insights into the Romania Corporate Sustainability and Transparency Index, as well as from the Risco database. Over an eight-year period from 2016 to 2023, we compiled a total of 264 observations from 33 companies that provided sustainability information. Correlation and regression analysis were used as inferential statistics to achieve the study's objectives. The findings reveal that market share has a positive and significant impact on corporate sustainability disclosure. Additionally, the study shows that financial ratings significantly and positively influence corporate sustainability. The study's results indicate that probability of insolvency has a negative and significant effect on corporate sustainability disclosure. Moreover, market value was found to have a significant positive impact on corporate sustainability disclosure. The findings of the study underscore the relevance of firm performance in influencing Romanian companies' sustainability disclosure following the provisions of Corporate Sustainability Reporting Directive (CSRD) and 2014/95/EU Directive. Ultimately, this study proves essential in the context of the CSRD, which plays a critical role in enhancing corporate transparency regarding sustainability practices. The key contribution of this study is to address the ambiguity in existing research by exploring the reverse relationship, how firm performance influences corporate sustainability outcomes.
The objective of the article is to examine the impact of soft total quality management (TQM) practices on enhancing workforce quality and business performance in hotels in Vietnam. Additionally, it explores the moderating role of information technology (IT) adoption in these relationships. Quantitative approach was applied to formulate the analytical framework. To test the established hypotheses, structural equation modelling was utilized to analyze data were collected from 122 hotels in Vietnam through a questionnaire survey conducted during 2021-2022. The findings indicate that soft TQM practices, including quality leadership and workforce management, lead to higher workforce quality, which in turn enhance business performance. Furthermore, the adoption of IT strengthens these relationships. However, there was insufficient statistical evidence to confirm the impact of customer orientation on workforce quality and the moderating effect of IT adoption on this relationship. Analytical results suggest hotel business managers to implement TQM practices focusing on quality leadership, customer orientation, and workforce management, which lead to a higher level of workforce quality and business performance improvement. The significant contribution of TQM practices to business performance is also enhanced by implementing IT application. This study supports the resource-based view (RBV) and contingency theories by emphasizing the critical role of employees in achieving superior business performance. Evidently, workforce quality is influenced not only by TQM practices but also by conditional factors, such as the extent to which organizations adopt IT to facilitate communication among employees and with customers. Additionally, the research provides valuable insights for hotel managers in developing countries on how the effective application of TQM can enhance business performance.
This study investigates age management implementation and its impact on employment patterns in Slovak organizations between 2021-2024. The research examines organizational responses to workforce aging challenges in a post-transition economy through a mixed-methods approach. Quantitative workforce analysis of Statistical Office data (n = 2,503) was combined with qualitative organizational assessment through key informant interviews (n = 6) to analyze employment trends across age cohorts and evaluate organizational responses. Findings reveal significant increases in older worker participation, with the 50-64 age group showing a 5.8% increase in employment rates (66.8% to 72.6%). Qualitative analysis identified six critical dimensions of successful age management implementation: technology adaptation, workplace and career continuation support. Organizations implementing comprehensive age management strategies demonstrated improved workforce retention among older employees. The findings provide empirical evidence supporting Slovakia's active aging initiatives while highlighting challenges in gender equity and technology adaptation. This work aligns with the journal's focus on contemporary workforce development challenges in transitional economies and contributes valuable insights for both practitioners and policy makers seeking to address the demographic shift in labor markets.
Well defined and transparent credit risk evaluation continues to be a fundamental issue in financial decision-making, particularly when dealing with intricate borrower profiles in the forms of organized tabular data. While traditional machine learning models often lack interpretability for predictive accuracy, recent deep learning approaches struggle to generalize across such data formats. We propose an innovative hybrid Tabular Deep Learning system that combines feature tokenizer transformer (FT-transformer) and TabNet architectures with an adaptive feature routing (AFR) method. The AFR module dynamically identifies significant aspects for each data instance, facilitating context-aware representation learning and enhancing generalization across other borrower groups. To guarantee explainability and compliance with regulations, our approach integrates a multimodal interpretability package that includes Shapley additive explanations (SHAP)-based attributions, attention mapping, and counterfactual reasoning for actionable what-if analysis. Comprehensive experiments on the benchmark dataset reveal the model's exceptional performance, attaining an AUC of 0.985, an F1-score of 0.972, and a premier ranking according to the Gini coefficient. Visual metrics of AUC vs. Gini coefficient, cost-benefit curves, and the counterfactual dashboards showcase the model's transparency and practical applicability. This study observes the application of explainable AI in credit risk modeling by successfully reconciling the balance between higher predictive accuracy and interpretability, hence facilitating explainable financial decision-making scenarios.
This paperinvestigates the contribution of green product innovation implemented by small and medium-sized enterprises (SMEs) in Europe to sustainable development, and the drivers of SMEs to introduce green product innovation. We selected the following groups of drivers: own financial resources, own green technical expertise, and external support, starting from the results of Flash Eurobarometer report. By applying logit regression to a sample of 12,657 SMEs in 34 European countries (European Union and Western Balkans), we examined how these drivers can influence the increase in the number of SMEs offering green products in the future. The results showed that the greatest impact on increase in SMEs offering green products can have external support and increase in own green technical expertise, as well as an increase in green expertise in combination with an increase in internal financing, while an increase in internal financing alone has no significant impact.
This study explores how the alignment of digital transformation models (DTM) with internal and external environmental factors drives productivity improvement in manufacturing enterprises. Using the fuzzy-set qualitative comparative analysis (fsQCA) methodology, the analysis reveals that no single factor is necessary for successful digital transformation. Instead, the alignment of DTM with environmental factors determines its effectiveness. The study identifies three independent DTM: (1) digitalization of production (DOP); (2) digitalization of organizational management (DOM); and (3) digitalization of business model (DOB). These DTM can be implemented individually or in combination to enhance productivity. Findings indicate that DOB requires strong market acceptance, DOM is effective in unfavorable environments, and DOP is most beneficial when industry digitalization is well-adopted. The research also highlights the importance of contextual adaptation and identifies avenues for further exploration, particularly regarding digital agility in digital transformation.
This study explores the complex relationship between workplace bullying, workplace anxiety, sustainable work performance, and innovative work behavior within the higher education sector of the Sultanate of Oman. Moreover, we also investigate the mediating effect of workplace anxiety between the relationship of workplace bullying and sustainable work performance. Using the research questionnaire from the 320 samples, data were collected from the universities located in the Sultanate of Oman. The data were analyzed using structural equation modeling (SEM) through Smart PLS 3.2.2 to test both direct and mediating effects. The outcomes confirm that innovative work behavior brings sustainability to work performance, highlighting its central role in sustainable work performance. The results also indicate that workplace bullying has a significant negative impact on innovative work behavior as well as on sustainable work performance, emphasizing the detrimental consequences of hostile work environments. Additionally, the results confirmed that workplace anxiety mediates between workplace bullying and sustainable work performance, suggesting that psychological distress is a key mechanism through which bullying affects performance outcomes. However, workplace anxiety does not mediate the relationship between workplace bullying and innovative work behavior. This indicates that employees experiencing anxiety as a result of bullying are not necessarily less creative. One possible explanation is that anxiety can have both positive and negative aspects; Omani employees may interpret it positively, perceiving it as a challenge that encourages proactive behavior and enhances creativity. Therefore, educational institutions in Oman should strive to minimize workplace anxiety, as employees who work in a stress-free environment are likely to be more productive and engaged. Moreover, this research also adds literature on the relationship between workplace bullying, workplace anxiety, sustainable work performance, and innovative work behavior in the context of Oman.
In recent years, the escalating economic uncertainty arising from Sino-US trade frictions has made the accurate forecasting of trade volume a crucial yet challenging task, with wide-ranging implications for the stability of the global supply chain. Precise trade forecasts are essential for supporting strategic decision-making and ensuring resilience across industries that rely on international trade. To address this challenge, this study introduces an innovative predictive model, the principal component analysis-simulated annealing-backpropagation neural network (PCA-SA-BPNN), specifically developed to enhance forecasting accuracy within this volatile economic landscape. The model utilizes principal component analysis (PCA) to reduce the dimensionality of extensive datasets collected from search engines, simplifying the data while retaining critical information. Simultaneously, simulated annealing (SA) is applied to optimize the backpropagation neural network (BPNN), effectively addressing the local optimization challenges often impair traditional backpropagation neural network models, which can hinder prediction accuracy. The effectiveness of the PCA-SA-BPNN model is demonstrated through comprehensive comparative experiments, demonstrating its superior performance compared to other models, including principal component analysis-adaptive differential evolution-backpropagation neural network (PCA-ADE-BPNN) and principal component analysis-backpropagation neural network (PCA-BPNN) models, as well as standalone XGBoost and BPNN models. The PCA-SA-BPNN model achieves notably lower mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values, with an R2 approaching 1, underscoring its superior predictive performance. This research thus offers valuable insights into how combining dimensionality reduction, optimization techniques, and neural network networks can significantly enhance trade volume forecasting amidst economic uncertainties. Furthermore, it provides valuable insights into the interplay between predictive accuracy, model efficiency, and resilient decision-making within global supply chain management, contributing to both theoretical advancements and practical applications in the field.
There is a more or less well-founded fear among the worldwide workforce that in the digital economy, robots will replace people. In this context, a concern arises as to what are the human skills that can help people adapt to the new labor market conditions. The prevailing answer seems to be the soft skills, considering that these are increasingly required for their contribution to enhancing the competitiveness of companies that have already automated their routine tasks. The present article hinges its analysis on these considerations and proposes an in-depth investigation of the importance placed by entrepreneurs on soft skills, disentangling the core layers (difficulties, consequences, actions, and actors of solutions). By combining classical (Kruskal-Wallis H test) with more modern methods (generalized ordered logit model), our study spots targeted solutions for the success of companies and employees in the labor market. Our research reinforces the idea that a combination of soft skills with digital and green ones can increase the competitiveness of both companies and employees. The article also indicates the main actors of the success of recruiting: companies and education, supported by the government, and the opportunities outside the EU. Furthermore, our investigation highlights the critical need for public policies and educational institutions to adapt and collaborate in developing training programs and curricula that align with evolving labor market demands, emphasizing the integration of both technical and soft skills, and leveraging innovative technologies to enhance workforce resilience and adaptability.
The food retail industry has experienced significant transformations during recent crises, including the pandemic, energy crisis, and armed conflicts. These events generated substantial socio-economic challenges for both companies and consumers, directly influencing purchasing behaviour and relational dynamics with brands and products. For instance, mobility restrictions during the pandemic forced consumers to reconsider how they purchased food and non-food items. Concurrently, armed conflicts and the energy crisis severely disrupted supply chains and store operations, requiring retailers to adapt quickly to meet shifting consumer needs and shortages. Many consumers rapidly abandoned previous shopping routines for online purchasing, mandating retailers to rapidly develop strategic responses to these changes. To explore the consequences of recent crises on food retail in Romania, an emerging market, and to show how retailers managed to adapt and implement new strategies based on the lessons learned, in 2025, the authors conducted in-depth interviews with food retail representatives. The aim was to identify transformations in consumer behaviour during the crises. Interview data were processed and analysed, combining text analytics (word frequency, associations, sentiment analysis, clustering) with thematic coding. Respondents consistently emphasised changes in consumer experience, increased adoption of online channels, precautionary behaviours such as stockpiling, and periods of panic buying. The findings provide an integrated perspective on how food retailers perceived and managed changes in consumer behaviour during the crises. Rather than establishing statistical causal effects, the research highlights consistent thematic patterns and emerging themes that link the crises situations to shifts in consumption and food retail strategies. The paper thus contributes to ongoing discussions on consumer behaviour dynamics under uncertainty, extending the methodological use of cluster analysis in food retail contexts, and adding interpretive insights in line with the dynamic capabilities theory.
