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 Task sequencing (TS) has been a focus of empirical investigations in task-based language teaching (TBLT). Previous studies on TS failed to represent classroom contexts because they did not provide learners with corrective feedback (CF). This study aimed to address this gap by examining the combined effects of TS and CF on writing complexity, accuracy, and fluency (CAF). To do so, 113 upper-intermediate EFL learners were selected and divided into two groups. Participants took a pretest at the beginning of the study. Each group performed two three-task sets in simple-to-complex(S-C) or complex-to-simple(C-S) order. In each group, the errors in the first set of tasks were given CF by error codes (ECs), while the errors in the second set were only underlined. Participants in both groups were asked to revise their texts based on the provided CF and take a posttest at the end of the study. Multivariate analysis of variance (MANOVA) was run to analyze the pretest and posttest data. The results showed that the group performing tasks in S-C order and receiving ECs outperformed the others. Findings supported the SSARC (Stabilize, Simplify, Automatize, Reconstruct, Complexify) model. The study has implications for material development.

Neurotransmitters are the chemical messengers nerve cells use to transmit signals to target cells. Neurotransmitters affect the behavioral aspects of investors' performance. Therefore, this study investigated neurotransmitters' effect on individual investors' behavior by using structural equation modeling. To achieve this goal, exploratory and confirmatory factor analysis has been used. The study is applied research and utilizes a survey methodology. The required data were collected through the distribution of questionnaires among 315 individual investors. This study was done in the Tehran stock exchange's second and third seasons of 2023. According to the research, neurotransmitters through eight components of adrenaline or epinephrine, noradrenaline or norepinephrine, dopamine, serotonin, GABA (gamma-aminobutyric add), acetylcholine, glutamate, and endorphins affect the behavior of individual investors. Based on the second-order confirmatory factor analysis, the GABA component (0.39) has the highest impact, and adrenaline or epinephrine (0.25) has the least effect on the behavior of individual investors. The findings indicate the necessity of redefining rationality and considering its effects on investors' decisions and behavior.

Climate change and infectious diseases have emerged as two of the most significant challenges facing the world and its economic systems today. Recent studies have increasingly focused on understanding how phenomena such as the COVID-19 pandemic and the climate change crisis—including rising greenhouse gas emissions and uncertainties in climate policy—affect macroeconomic variables and capital markets. Many experts assert that climate change poses a serious threat to human civilization. In light of these pressing concerns, this paper examines the impact of Climate Policy Uncertainty (CPU) and Infectious Disease Risk (IDR) on the Islamic Dow Jones Index (IDJ). To achieve this, the study utilizes monthly data spanning from January 2016 to March 2021 and employs a Structural Vector Autoregressive (SVAR) model. Additionally, Contour Plot graphs are applied to estimate the interaction effects on the IDJ. The findings reveal contrasting impacts of the CPU and IDR indices on the IDJ. Specifically, an increase in the CPU index leads to higher utilization of the Earth's resources for producing goods and services by companies included in the index, resulting in a positive effect. Conversely, a rise in the IDR index exacerbates economic recession, thereby causing a decline in the IDJ.

Purpose: Neurotransmitters, emotional intelligence, and personality traits are the key factors that affect the behavior and decisions of individual investors. This study aimed to investigate the effects of neurotransmitters, emotional intelligence, and personality traits on the behavior and decisions of individual investors simultaneously, in the Tehran stock exchange. Design/methodology/approach: In terms of purpose, the current research was practical. In terms of method, it was a descriptive survey research. In terms of time, it was a cross-sectional research. The research data were collected through a questionnaire and convenience sampling method. The sample size was 212 individual investors. Composite reliability, average variance extracted, convergent validity and divergent validity were used to measure the validity and reliability of the questionnaire. Data were analyzed using structural equation modeling. Findings: This study includes 19 constructs (variables). These constructs include dopamine, serotonin, norepinephrine, and epinephrine (neurotransmitters); Self-appraisal of emotions, other’s emotion appraisal, use of emotion and regulation of emotion (emotional intelligence); extraversion, neuroticism, openness, conscientiousness, and agreeableness (personality traits); investment horizon, risk attitude, personalization of loss, confidence, and control (investors' behavior); and investors' decisions. The current study includes six main hypotheses and 78 sub-hypotheses. The research findings showed that neurotransmitters, emotional intelligence, and personality traits affect the investors' behavior and decisions. Originality/value: The findings of this study showed that the neurofinance and behavioral finance have a key role in decision making of policymakers and investors. Finally, the findings of this study would provide new horizons in neurofinance and behavioral finance.

Subject and purpose: The intelligent sales management system in Metaverse is the concept of performing sales operations, using smart contracts, machine learning, intelligent process automation and direct monitoring of all network members in Metaverse.The aim of the current research is to provide a model of intelligent sales management system in Metavers. Methodology: In the qualitative research, the statistical population includes experts in the fields of business management and information management, and the sample size was calculated and carried out according to the theoretical saturation point of 14 people using the snowball sampling method. Data collection was done through semi-structured interviews with experts and analysis. Data were collected using open, central and selective coding method. Findings:The findings obtained in the qualitative research showed 112 codes, 29 concepts and 9 categories, which led to the presentation of an intelligent sales management system model in Metaverse. Conclusion:This research led to the presentation of a new model of intelligent sales management system. Keywords: Intelligent management system, Metaverse, Foundation data.

Introduction: Within marketing literature, a widely accepted notion indicates that strategies in marketing significantly differ between business-to-business (B2B) and business-to-consumer (B2C) models. This study thoroughly investigates brand loyalty, examining the nuanced differences in methodologies for building brand loyalty specifically within B2B frameworks, aiming to develop a model tailored for B2B enterprises. Methodology: The research utilizes a sequential exploratory mixed-methods approach. The initial qualitative phase involves systematic reviews and meta-synthesis to identify factors influencing brand loyalty. The subsequent quantitative phase employs techniques such as structural equation modeling (SEM) to analyze the impact of various factors on brand loyalty across different business models. Finally, interviews are conducted to gain insights into tailored strategies that enhance loyalty in B2B contexts. Data for the literature review will come from reputable domestic channels, including academic portals, alongside foreign databases like Scopus and Web of Science. The second phase will gather information from diverse companies in the insurance industry, utilizing convenience sampling for analysis. The third phase targets business experts in B2B settings, selected through purposive judgment sampling to ensure relevant insights are gained. Findings: The initial phase reveals ten critical factors that contribute to brand loyalty in B2B markets. These factors encompass customer satisfaction, perceived value, switching costs, trust, perceived quality, personalized interactions, perceived support, brand image, and commitment. Analysis indicates significant differences in factors influencing brand loyalty between B2B and B2C models. The study concludes with a collection of tailored strategies essential for fostering customer loyalty in B2B operations, including developing strategic frameworks and adopting stringent quality standards. Conclusion: This research expands the understanding of loyalty-building strategies across diverse business models, offering insights that enhance the existing body of knowledge. By proposing a specialized loyalty model for the insurance industry, the study equips practitioners with practical tools to improve operational efficiency and competitive advantage.