FIIB business review 

About: FIIB business review is an academic journal published by SAGE Publishing. The journal publishes majorly in the area(s): Computer science & Medicine. It has an ISSN identifier of 2319-7145. Over the lifetime, 125 publications have been published receiving 135 citations. The journal is also known as: FBR.

Entrepreneurial Start-up Motivations and Growth of Small and Medium Enterprises in Tanzania: The Role of Entrepreneur’s Personality Traits

Abstract: This study aims to analyze the mediating effect of personality traits in the relationship between entrepreneurial start-up motivations and small and medium enterprises (SMEs) growth. A gap has been identified in the literature, particularly in the indirect association of critical factors that may be beneficial to the growth of SMEs. In this study, a cross-sectional survey design was used because data were collected once in a time. Furthermore, structural equation modeling (SEM) was used to analyze data obtained from 367 SMEs’ owners. The findings indicate that entrepreneurial start-up motivations such as a desire for achievement, financial rewards, social recognition and a desire for independence are positively and significantly related to SMEs’ growth. Also, entrepreneurial start-up motivations positively and significantly influence personality traits among entrepreneurs. It was found that there is a significant link between personality traits and the SMEs’ growth. Additionally, the findings indicate that personality traits mediate the relationship between entrepreneurial start-up motivations and SMEs’ growth. The study’s findings have implications for the practices and theories in the entrepreneurship context.

A Systematic Literature Review on Personal Financial Well-Being: The Link to Key Sustainable Development Goals 2030

Abstract: This study presents systematic literature review (SLR) of financial well-being which is crucial for attaining several key UN Sustainable Development Goals 2030 (SDG 1, 3, 10 and 16). After applying the criteria of selection, the study included 133 publications from 79 high-impact journals, using Web of Science (WoS) Core Collection database. Unlike previous studies, the study contributes to the existing body of knowledge by conducting systematic review of financial well-being literature from a holistic perspective and presenting the most recent and up-to-date research findings in the area. VOSviewer, a software tool was used to create bibliometric networks. The results of this systematic review study suggested the following conclusions: (a) financial well-being is a dynamic and multidimensional construct; (b) studies studying antecedents of financial well-being are far more in number than consequences; (c) majority of the previous studies are based on quantitative research methods (112), that is, secondary data research (75); (d) financial well-being has been mostly quantified using subjective measures; (e) the previous studies seems to be dominated by developed countries like the USA, Canada, Germany, and, England posing several limitations in practice; (f) Financial well-being was mostly studied with ‘poverty’, ‘behavior’, ‘income’, ‘health’ and ‘growth’. Limitations and future research directions of the current study are discussed.

Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches

Abstract: The study’s goal is to analyse and predict customer reviews of insurance products using various machine learning techniques. We gathered consumer rating data from the Yelp website and filtered the initial data set to only include insurance reviews. Following cleaning, the filtered summary texts were graded as positive, neutral or negative sentiments, and the AFINN and Valence Aware Dictionary for Sentiment Reasoning (VADER) sentiment algorithms were used to rate those sentiments. Furthermore, the current investigation employs five supervised machine learning approaches to divide customer ratings of insurance companies into three sentiment groups. The results of the current study revealed that the majority of customer reviews for the insurance products were negative, with the average number of words with negative sentiment being higher. In addition, current research discovered that while all of the approaches (decision tree, K Neighbours classifier, support vector machine (SVM), logistic regression and random forest classifier) can correctly classify review text into sentiment class, logistic regression outperforms in high accuracy. We analysed and predicted customer review messages using a variety of machine learning methods, which could help companies better understand how customers respond to their products and services. As a result, companies can learn how to use machine learning methods to better understand the behaviour of their customers.

Consumers’ Willingness to Pay for Agri-Food Products Delivered with Electric Vehicles in the Short Supply Chains

Abstract: This study aims at investigating the consumers’ willingness to pay (WTP) for agri-food products delivered in the farmers markets with electric vehicles (EVs). The empirical analysis has been carried out by administering an online questionnaire to 273 consumers within a hypothetical context. A multiple price list has been provided in order to measure the price premium for 1 kg of oranges transported by EVs comparing with the ones transported by traditional vehicles. A Tobit regression model has been used for the data analysis in order to identify the most influential factors affecting the consumers’ WTP. Results suggest that consumers’ WTP is mainly influenced by environmental attitudes, knowledge and possibility of engaging in responsible purchasing, and the approval of friends and relatives (social norms). This study delivers insights into support both farmers’ choice to invest in EVs and policymakers to develop policies promoting this transition process. The main limitations of this study are related to the use of a random sample of respondents and to the social desirability bias linked to self-reported questions.

The Landscape of Digital Natives Research: A Bibliometric and Science Mapping Analysis

Abstract: This article provides a panoramic view of the research undertaken so far in the field of digital natives to highlight important trends, examine the intellectual structure and provide recommendations for future research. The study uses 983 publications from the Scopus database to analyse the productivity, impact and research performance of nations, journals, authors and institutions using indicators such as impact factor, h-index and citation counts. This review study uses bibliometric and science mapping analysis to assess the most recent developments and trends in ‘digital native’ research. VOSviewer is used to conduct keyword network analysis, co-authorship analysis and reference co-citation analysis, followed by SciMAT analysis to provide an evolution map and a cluster of themes. The analysis identified key contributors to the field, high-impact papers and geographic areas where field research is concentrated. The main research gaps were then identified, indicating future research avenues. The findings of this study will provide fresh higher-level insights into the developing field of digital native research for educators, computing executives, business managers and research scholars. Such information would be useful in establishing digital native recruitment tactics by the respective industry. The research will also aid in the development of policies for digital natives.