International Journal of Business and Development Studies

Objective: This study investigates the asymmetric effects of Economic Policy Uncertainty (EPU) and Oil Price Uncertainty (OPU) on inflation in Iran, using the Quantile-on-Quantile (QQ) regression method. Given Iran's oil-dependent economy, the research aims to understand how global and domestic uncertainties impact inflation dynamics, particularly during economic turbulence. Traditional models often neglect the non-linear and heterogeneous effects of uncertainty on inflation, prompting the use of the QQ approach to capture the varying impacts across different quantiles. Methods: The study employs the QQ regression method to analyze the asymmetric effects of EPU and OPU on inflation, using monthly data from 2008 to 2023. The QQ approach is chosen to address the heterogeneity and non-linearity in the relationship between uncertainty and inflation, particularly in the context of Iran's oil-driven economy. Results: The findings reveal significant heterogeneity in inflation responses to uncertainty shocks. At lower quantiles, both EPU and OPU have minimal effects on inflation, indicating that minor changes in uncertainty do not significantly alter inflation rates. However, at higher quantiles—especially during periods of heightened uncertainty—both factors show a more pronounced and positive effect on inflation. Additionally, the QQ analysis indicates that EPU has a more consistent impact on inflation compared to OPU, suggesting that policy-induced uncertainty exerts greater pressure on price levels than oil price volatility. Conclusions: The study highlights the importance of accounting for asymmetric and quantile-dependent uncertainty effects when formulating inflation control policies, as this can help address inflationary challenges in the face of fluctuating global and domestic conditions.

This study aims to estimate the demand function for recycled paper in the Iranian economy during the seasonal period of 2000 to 2021. The Johansen-Juselius cointegration method was used to identify the factors influencing demand in both the long and short term. Quarterly data were generated from annual figures using the Denton method, and the model was specified in a linear logarithmic form. The model’s variables include per capita consumption of recycled paper, its price, the price of virgin paper, the percentage of internet users, and Gross Domestic Product (GDP). A dummy variable was also included to account for the removal of the import tariff in 2018. The results indicate that recycled paper is a necessary and inelastic good. In the long term, it becomes an inferior good, meaning that as income and welfare increase, consumers and producers shift towards higher-quality virgin paper. In the short term, demand is influenced by changes in GDP, the price of recycled paper, and the internet. The price elasticity of demand is very low (0.012%), suggesting that price changes have a negligible impact on the quantity demanded. The error correction model (ECM) coefficient of -0.51 indicates that half of the short-term disequilibrium is corrected in each period. These findings emphasize the importance of government policies in encouraging recycling and managing the market. Given Iran's limited forest resources, focusing on this sector can contribute to economic and environmental sustainability. Future research could explore the impact of tax and subsidy policies on the paper recycling industry.

This study investigates the role of economic factors such as total annual water withdrawal, total renewable energy consumption, total greenhouse gas emissions, and waste generation in shaping the trajectory of the circular economy. Utilizing a nonlinear multilevel regression model with combined fixed and random effects, the research examines the impact of these variables on the circular economy index in Iran over the period 1993 to 2023. The choice of this model is justified by its ability to incorporate both fixed effects (such as average variable levels) and random effects (to account for unobserved heterogeneity across observations or units). The results indicate that among the factors influencing the circular economy, renewable energy consumption has a positive and statistically significant effect on circular economy index, with a coefficient of 0.004. In other words, higher renewable energy consumption is associated with improvements in the circular economy index, underscoring the positive role of clean and renewable energy in promoting sustainable economic development. Conversely, water withdrawal, greenhouse gas emissions, and waste generation negatively impact the circular economy, with coefficients of -0.091, -0.02, and -0.003, respectively. This implies that increased unsustainable resource use (such as water extraction), higher greenhouse gas emissions, and greater waste generation hinder the development of a circular economy. Strategic and targeted investment in water-efficient technologies, renewable energy development, greenhouse gas mitigation, and comprehensive waste management can serve as fundamental pillars for advancing the circular economy.

Objective: “In the digital age, anthropomorphism—attributing human characteristics to non-human entities in digital environments—plays a pivotal role in improving user experience (UX) and fostering customer loyalty. With the increasing adoption of artificial intelligence (AI) and intelligent interfaces, simulating human interactions such as digital platforms has emerged as a powerful mechanism for creating emotional connections and facilitating interactions. This study seeks to identify the antecedents and consequences of anthropomorphism in digital user environments and assess its impact on user experience and customer loyalty. Methods: Using a qualitative and exploratory approach, this research examines data collected from semi-structured interviews with 16 academic and industry experts using thematic analysis. Sampling was conducted through a snowball technique, and theoretical saturation was considered as the criterion for determining sample size. Results: The findings show that the antecedents of anthropomorphism include six main factors: personalization and optimization of user interactions (including personalized user experiences and emulation of human emotions), human-like interactions and natural communication in digital spaces, social and emotional experiences of users, intelligent responsiveness and optimal interactions, emulation of human and psychological behaviors, and user loyalty and trust enhanced through human-centered interactions. The implications of anthropomorphism include increased social interactions, intelligent responsiveness, personalized experiences, and enhanced customer trust and loyalty. Conclusions: The results indicate that anthropomorphism helps increase user satisfaction and strengthen customer loyalty by personalizing interactions and creating emotional bonds. These insights offer valuable implications for designers and marketers of digital systems who aim to optimize user experiences and develop effective customer retention strategies.

Service providers are central to the growth and competitiveness of platform-based businesses. Their collaborative and conscientious behavior plays a pivotal role in driving value co-creation across these digital ecosystems. This study examines how service providers’ conscientious behavior on the Instagram platform facilitates effective value co-creation, highlighting its pivotal role in creating mutual benefits for both providers and service recipients. This study is applied research aimed at examining service providers’ collaborative behavior on the Instagram platform and employs a descriptive survey design. The statistical population comprised all Instagram users acting as service providers, with a purposive, non-probability sample of 235 participants. Based on theoretical foundations and prior studies, collaborative behavior was conceptualized through four dimensions: conscientious behavior, personal interaction, informational behavior, and altruistic behavior. “The validity of the research instrument was assessed using confirmatory factor analysis, and reliability was confirmed through Cronbach’s alpha and composite reliability. Following data collection, K-Means clustering was applied to classify service providers, and research hypotheses were tested. The results identified two clusters: conservative conscientious service providers and perfectionistic conscientious service providers. Further analyses indicated significant relationships between informational behavior, altruistic behavior, and personal interactions with both clusters. Additionally, significant differences were observed in altruistic behavior, personal interactions, and informational behavior between conservative and perfectionistic conscientious service providers.

Purpose: Financial reporting is a competitive strategy among companies listed on capital markets, providing stakeholders with timely and reliable data. The sensitivity of topic drives development of increasingly sophisticated reporting languages, enhancing financial transparency. Thus, this study examined relationship between audit fees and reporting delays with tone of financial statements in companies on the Iraq Stock Exchange. Design/methodology/approach: Data from companies on the Iraq Stock Exchange were collected from 2016 to 2021, encompassing 33 companies (198 observations). A random-effects multiple regression analysis using panel data was conducted to evaluate the hypotheses. Findings: Results demonstrated audit fees, auditor skill, and auditor turnover positively influence upbeat tone of financial statements. In contrast, prolonged reporting delays lead to more negative tone. Furthermore, earnings management and the debt-to-equity ratio contributed to less optimistic tone in financial statements. Originality/value: Originality of study lies in its exploration of intersection between audit fees, reporting delays, and tone of financial statements, specifically within context of companies on the Iraq Stock Exchange. While prior research has examined relationship between audit fees and financial reporting quality, few studies have investigated how these factors influence tone of statements in emerging markets. Focus on Iraq, an economy with a unique regulatory and market environment, adds new insights into dynamics between audit-related costs, reporting timeliness, and narrative style of financial disclosures. This study also contributes to broader literature by highlighting how both audit-related factors and financial metrics impact tone of reporting, offering a nuanced understanding of corporate communication practices in an underexplored market.

Objective: This study aims to develop a comprehensive framework to support the internationalization of Iranian manufacturing small and medium enterprises (SMEs) during financial crises, addressing challenges posed by economic sanctions, currency devaluation, and global disruptions such as the COVID-19. Methods: A qualitative methodology was utilized, employing thematic analysis of data collected through 20 semi-structured interviews with managers from SMEs in the automotive parts, food, industrial machinery, plastics, and pharmaceutical sectors. Data were analyzed using MAXQDA software, applying open, axial, and selective coding to identify key themes. Validity was ensured through data triangulation, inter-coder reliability (Cohen’s kappa = 0.82), and participant verification of transcripts. Results: The analysis identified key drivers, including currency devaluation (85%), domestic market stagnation (75%), and opportunities in neighboring markets (95%). Major challenges included foreign currency payment difficulties (85%), logistical barriers (80%), and lack of awareness of international regulations (60%). Strategies encompassed flexible currency contracts (90%), barter trade (60%), cryptocurrencies (25%), and export-oriented technological advancements (55%). Market entry pathways, consistent with the Uppsala model, included commercial networks (95%), local agents (95%), and international exhibitions (45%). Outcomes comprised sustainable growth (85%), entry into new markets (75%), and job retention (60%). The proposed Structural Interpretive Model (SIM) integrates the Uppsala model, network theory, and risk management principles. Conclusions: The SIM offers a practical framework for Iranian SMEs, validated through theoretical and practical triangulation, with potential applicability to other sanctioned economies. Recommendations include implementing training programs, enhancing government support, and investing in digital infrastructure to strengthen SME resilience and global competitiveness.

According to the macroeconomic literature, the type of exchange rate regimes has an impact on the impact of different policies on macroeconomic variables. Accordingly, in this study, the effect of external shocks including oil shock, external inflation shock and exchange rate shock on employment in three types of floating, fixed and managed floating exchange rate regimes was investigated and analyzed using dynamic stochastic general equilibrium (DSGE) model. The model included households, firms, government, monetary policymakers, and the external sector. The results of the impulse response functions (IRF) show that the effect of the oil shock on employment shows that the effect of this shock on employment in all three exchange rate systems was positive and caused to increase the employment. The positive effect of the foreign inflation shock on employment in the fixed exchange rate system was greater than that of the floating and managed floating exchange rate systems. Also, the effect of the exchange rate shock on employment in the fixed exchange rate system was positive and increased employment, but in the managed floating exchange rate system and the floating exchange rate system, this effect was negative and decreased the employment.

Blockchain technology, as a decentralized and distributed database, has significant capabilities in enhancing transparency, security, and efficiency can help improve the financial performance of companies through the enhancement of supply chain and export processes. Therefore, we examined the impact of blockchain technology on the financial performance of small and medium businesses, with the mediating role of supply chain efficiency and export performance in tile and ceramic exporting companies in Iran using structural equation modeling. The statistical population consists of experts and specialists of the companies mentioned, particularly in the fields of finance, sales, and production, with a sample size of 261 individuals. The results indicated that the impact of using blockchain technology on supply chain efficiency and export performance is positive and significant. Moreover, the mediating roles of supply chain efficiency and export performance in the relationship between blockchain technology and the financial performance of small and medium businesses are positive, strong, and significant.

Having an accurate estimation of a customer's worth is one of the more important tasks performed by banks in this modern world, especially with the profound number of customers and the complex nature of transactions, along with the massive variance in transactions. In light of this need, we develop a multi-layer stacked ensemble model specifically designed to improve the predictive performance of banking customers in Iran. The first layer consists of 4 different learners (XGBoost, CatBoost, Random Forest, and Gradient Boosting). Each model has its learning capacity and learns from customer behavior and financial characteristics in complementary ways. The second layer consists of a LightGBM classifier, which fuses (by meta-model) the outputs of the first-layer learners into the final prediction. The second set of model hyperparameters were optimized using a Reinforcement Learning (RL)-based SWO to efficiently search for optimal hyperparameters across a high-dimensional space, which is typically not well-explored using classic optimization strategies. Utilizing a repeated 5-fold stratified cross-validation approach, we were able to achieve strong predictive accuracy: Accuracy = 89.70%; Precision = 92.84%; Recall = 92.46%; F-Score = 92.61%; ROC AUC = 0.9632; all of which surpass the single models. Our results provide evidence supporting the successful application of a multi-layer ensemble with metaheuristic hyperparameter optimization in building a viable and powerful customer valuation tool for banks.

Poverty is one of the most challenging issues in the world, especially in developing countries. Much research has been done on poverty and its causes, but few studies have been conducted to examine the macroeconomic dynamics of poverty. The main purpose of this paper is to examine the macroeconomic dynamics of poverty in Iran, and for this purpose, a system dynamics model has been designed, simulated, and used for Iran. The simulation over the period 1990-2021 shows that, on average, at least the first four income deciles in Iran are below the international poverty line ($6.85 per day). According to the simulation results, international sanctions against Iran, hyperinflation, exchange rate shocks, and expansionary monetary and fiscal policies have increased the poverty rate, but in comparison, the impact of international sanctions has been greater than others. Specifically, the simulation shows that international sanctions have increased the poverty rate in Iran by about 10 percent on average. Even economic growth has not been able to significantly reduce the impoverishing effects of monetary shocks and inappropriate economic policies. According to the results, in addition to lifting international sanctions, targeted policies for sustainable economic growth and development should be implemented, and the Central Bank, while maintaining its independence, should refrain from implementing monetary policies that reduce the value of the national currency.

Both Afghanistan and Bangladesh have a per capita income below $500 early 21st century, but now Bangladesh’s per capita income is more than 3 times that of Afghanistan. Surprisingly, at the beginning of the period, Bangladesh have worser than Afghanistan in the corruption perception indices, and hence a small government has been sufficient to experience high economic growth. However, emphasizing small government such as that promoted by the World Bank’s 1980s model is not suffice for low-income, resource-scarce, landlocked countries like Afghanistan. In such contexts, institutions are not capable of transforming financial resources into effective public services. Using Paul Collier’s framework, this paper evaluates how institutional performance and economic policies function in Afghanistan’s failure to achieve development. The aim is to examine the undevelopment trap of bad governance and ineffective policies in Afghanistan. The period 2005–2019 divided into three five-year sub-periods and then each sub-period is analyzed by a stylized-facts-based approach. The findings indicate that during the first period (2005–2009), the economic management had the strongest performance with an average score above the threshold 3, while criteria of the public sector management and institutions had the weakest performance, scoring below the threshold. Since the average of all indices was remained consistently below the Collier’s threshold, it is concluded that Afghanistan entraped in a bad governance trap. This trend is been continued in the second (2010–2014) and third (2015–2019) periods, albeit criteria structural policies, instead of public sector management and institutions in the first period, has worst status.