This study tested for an interaction between applicant skin color (light vs. dark) and racial prejudice (high vs. low) on salespersons’ pre-interview impressions of Hispanic sales applicants. Using a sample of 193 White salespersons, the results of between-subjects ANOVA revealed a significant interaction. High-prejudice salespersons held less (more) favorable pre-interview impressions toward dark-skinned (light-skinned) Hispanic sales applicants than did low-prejudice salespersons. In addition, high-prejudice and low-prejudice salespersons exhibited different skin color biases for the Hispanic sales applicant. The findings suggest that diversity issues in sales employment should extend race and ethnicity paradigms to include skin color.

This study advances the understanding of a recently identified leader behavior known as championing, which is argued to be especially effective in a sales context. Championing occurs when sales managers protect their salespeople from nonessential tasks, enabling them to focus on activities that enhance sales performance. Building on the exploratory work by Peesker et al. (2019), the current study delves deeper into the theoretical underpinnings and practical implications of championing. The first major section is a literature review that relates championing to established leadership theories, including path-goal, transformational, ethical, and servant leadership. This review demonstrates how championing aligns with and reinforces these theoretical frameworks, providing a deeper understanding of the behavior. Following this theoretical groundwork, the authors developed a new multi-item measurement scale for championing by integrating traditional scale development methods with innovative techniques involving artificial intelligence. Specifically, items for the scale were generated using a chain-of-thought prompting process with ChatGPT-3.5. These items were empirically tested through conventional scale development procedures, which included analyzing data from a sample of 254 salespeople using both exploratory and confirmatory factor analysis. This approach resulted in the creation of a robust four-item scale with a one-factor solution that demonstrated strong construct validity. Future researchers can use this measure to examine how championing interacts with other established leader behaviors to generate positive work outcomes, such as reduced role stress, increased trust in managers, and higher sales performance. Overall, this study contributes to a more nuanced understanding of championing by highlighting its relevance within the broader leadership literature, making a strong case for its effectiveness in a sales context, and offering a validated measurement tool for future research.

Past literature shows that investors use mental account when they are facing dividend distribution. That is, investors focus on dividend distribution and irrationally ignore the tradeoff of dividend payment and price decrease. The dividend is thus deemed as bonus to investors. Furthermore, investors tend to buy ex-dividend stocks after the ex-dividend day due to the expectation of price recovery. The buying forces around the ex-dividend day persistently push the price. After the ex-dividend, investors gradually become rational. The stocks thus are priced based on the fundametals. The overprice of stocks due to the ex-dividend events may consequently decreases. Past litearture regarding ex-dividend focuses on the announcemnet effect and ex-divend effect on the ex-dvidend day. The contribution of this project is to combine the issues of ex-dividend and media coverage to investigate whether the investing behavior bias of investors is strengthened after the coverage of ex-dividend news by media. Past literature has confirmed that investors refer to the media coverage when they are making stock picking decisions. However, the past literature did not further investigate the reaction of investors to the ex-dividend news. This study fills the gap of the literature to combine these two issues, which may provide a referecne for investors when they are making investing decisions.

This paper examines the challenges and opportunities facing entrepreneurs in Northeast North Carolina. Entrepreneurship has been practicing for many years in several countries and it has been a driving force for alleviating poverty in many countries especially in developing nations. Many policy makers and scholars strongly believe that economic growth can be achieved through entrepreneurship. The main objective of the paper was to identify the challenges and opportunities for entrepreneurs in Northeast North Carolina. The paper selected ten counties in Northeast North Carolina to examine the challenges that hindered the progress of entrepreneurs because they are not different from other entrepreneurs in developed and developing countries. To address the challenges of women entrepreneurs, it is significant to understand these challenges facing entrepreneurs in the ten selected counties in Northeast North Carolina. The paper concluded and recommended that government should encourage and support entrepreneurs in Northeast North Carolina so as to promote economic growth especially the affected ten counties.

This paper proposes a method for reducing model errors in regressions when modelling macroeconomic variables by using machine learning algorithms and traditional time series regression models. In this paper, machine learning models are subjected to repeated k-fold cross validation and hyperparameter tuning. The linear model uses repeated k-fold cross validation, on the other hand, the traditional time series model Mixed Data Sampling Auto Regressive Distribution Lag model is run without repeated k-fold cross validation and hyperparameter tuning. The results show that integrating repeated k-fold cross validation with hyperparameter tuning increases the overall performance of machine learning algorithms and each model records the average outcome from all folds and runs. These findings demonstrate how machine learning models outperform the traditional time series model.

The purpose of this study was to find out the relationship between financial deepening indicators and the stock market performance in Kenya. The selected financial deepening indicators were; financial savings, private sector credit, broad money supply and intermediation ratio on the stock market performance. Johansen cointegration test was done indicating that the variables co-move towards a long-run equilibrium, a multivariate vector error correction model was run and the estimates obtained. The error correction term was also computed. Empirical results showed that all variables are adequately explained by their own lags and the lags of the other variables, the coefficients are also significant. The error correction model indicated that an increase in private sector credit by one unit in the previous quarter causes the stock market performance to increase by 48% in the current quarter. Variance decomposition tests and impulse response functions indicated how other variables respond to shocks in the other variables and the forecast errors for each of the predicted quarters. The implication of this study is that the policy makers who are; the Government, the Central bank of Kenya and the Capital Markets Authority ought to make policy decisions while considering the effect of the full market. This study concluded that private sector credit is the financial indicator variable that affects the stock market performance the most with a bidirectional relationship.

In the current retail banking environment, effective customer engagement is crucial for the success of organizations, impacting customer satisfaction, loyalty, and overall business performance. Given the competitive landscape and evolving customer expectations, it is essential for banks, especially Absa bank in South Africa, to understand the dynamics of customer engagement. This study aims to investigate how customer engagement initiatives influence customer satisfaction at Absa bank in the Durban area to provide strategic insights for enhancing competitiveness. Using theoretical frameworks such as Sashis customer engagement cycle and Gronroos model of service quality, this research explores the relationship between communication strategies related to organizational change and customer satisfaction levels at Absa bank in Durban. A quantitative research approach was employed, utilizing a structured questionnaire survey and convenience sampling to gather data from walk-in clients at various Absa bank branches. Statistical analyses, including descriptive and inferential methods, were used to analyze the data and test hypotheses. The results of the study offer valuable insights into customer preferences and expectations in the retail banking sector. Customers appreciate consistent messaging across different engagement channels and value personalized communication from the bank. Personalized calls, follow-up phone calls, and nonverbal cues such as a firm handshake were found to significantly impact customer perceptions and satisfaction with Absa banks services. Customers display a high level of trust, satisfaction, and engagement with Absa bank, indicating the success of its customer engagement initiatives. The study also emphasizes the importance of social media in enhancing positive word-of-mouth and shaping consumer perceptions. Based on these findings, it is recommended that Absa bank implements a proactive customer engagement strategy focused on providing positive and personalized experiences to its existing customers. Key strategies include tailoring customer interactions, soliciting feedback, introducing loyalty programs, and utilizing various engagement platforms.