International Journal of Digital Entrepreneurship and Business

Exploring the Dynamics of Pay Later in E-Commerce: Trust, Security, Satisfaction, and Continuance Intent

Amanda, Pepey Riawati Kurnia, Rike Penta Sitio — Tue, 31 Oct 2023 00:00:00 +0700

The ever-evolving landscape of the financial technology (FinTech) sector has witnessed a surge in the popularity of non-cash transactions on e-commerce platforms. Among these, the Pay Later option has gained considerable prominence as it allows users to defer payments, akin to traditional credit cards. This research delves into the crucial factors influencing users’ intention to continue using the Pay Later feature on e-commerce platforms, with a particular focus on the role of user satisfaction. We conducted an online survey using Google Forms to collect empirical data, targeting Pay Later users on e-commerce apps within the Jabodetabek region. Our study comprised a purposive sample of 401 respondents. The data was subjected to advanced statistical analysis, specifically variance-based structural equations modeling (SEM) employing the partial least squares (PLS) path modeling approach facilitated by SMARTPLS 3.0. Our findings indicate that user perception of security, trust, and user interface directly impacts their intention to continue using Pay Later. However, perceived enjoyment and perceived usefulness did not exhibit a direct correlation with continuance intention. Notably, user satisfaction emerges as a crucial mediator in the relationship between perceived security, trust, perceived usefulness, perceived enjoyment, and user interface on the one hand and users’ intention to continue utilizing Pay Later on the other. The implications of this study extend to stakeholders in the e-commerce industry, offering valuable insights that can be harnessed to enhance customer continuance intention and foster the widespread adoption of the Pay Later system within e-commerce platforms.

The Factors Affecting Profitability of Manufacturing Company Listed on the Indonesian Stock Exchange

Ronaldo Atmaja, Bahtiar Usman — Tue, 31 Oct 2023 00:00:00 +0700

This study aimed to investigate the effects of liquidity, intangible assets, working capital, leverage, company size, and efficiency on the profitability of manufacturing companies listed on the Indonesian Stock Exchange (IDX). A purposive sampling method was used to obtain 74 manufacturing companies listed from 2017 to 2021 based on the specified criteria. The sample selection criteria encompassed manufacturing companies listed on the IDX, representing diverse sectors, including the basic and chemical industry, consumer goods industry, and miscellaneous industry. The selection was restricted to companies not undergoing delisting and initial public offering procedures during the research period to ensure a balanced panel data structure, and the financial statements of these companies were required to meet all criteria for each respective variable. Furthermore, the statistical analysis instrument used was panel data regression with a random effect as the best econometric model selected. The results showed that liquidity, working capital, leverage, company size, and efficiency positively affected profitability. The predictor of intangible assets did not significantly affect profitability. Meanwhile, this study was expected to contribute in the form of managerial implications to consider liquidity, working capital, leverage, company size, and efficiency to enhance profitability.

Electronic Word of Mouth as a Predictor of Purchase Intention: Evidence from Instagram and TikTok in Indonesia

Chikal Adi Pratama, RR Dyah Astarini — Tue, 31 Oct 2023 00:00:00 +0700

In contemporary times, the social media landscape is shaped by the dominance of Instagram and TikTok. These two platforms are prominently used for marketing various products and services. Therefore, this research aimed to examine the influence of electronic word of mouth (e-WOM) on purchase intention for e-WOM recipients on Instagram and TikTok. The dimensions tested include quality, quantity, credibility, and information provider expertise. In this context, the quantitative research analyzed the causal relationships based on the developed hypotheses. The purposive sampling method used was guided by specific criteria, namely individuals who were users of Instagram and TikTok, encountered e-WOM communication through these platforms, specifically originated from influencers, and refrained from making purchases of particular products or services. Furthermore, data analysis was conducted using the Multiple Linear Regression statistical instrument. The results showed that e-WOM quality, quantity, credibility, and information provider expertise positively influenced purchase intention. This research had managerial implications, indicating the need to enhance e-WOM quality, quantity, credibility, and information provider expertise to increase purchase intention on product or service influencers communicate. The implications included increasing the number of reviews, summarizing the product or service, improving recommendation ratings, enhancing confidence, and improving the ability to evaluate the products or services.

The Behavioral Finance of MSME in Indonesia: Financial Literacy, Financial Technology (Fintech), and Financial Attitudes

Fadilah Siti Rahayu, Asep Risman, Iwan Firdaus, Luna Haningsih — Tue, 31 Oct 2023 00:00:00 +0700

The study investigates the critical factors affecting financial behavior in Micro, Small, and Medium Enterprises (MSMEs). It analyzes the role of financial literacy, attitudes, and financial technology (Fintech) on financial management behavior among 1.1 million DKI Jakarta MSME stakeholders in 2020 through random sampling. The study reveals that heightened financial literacy positively influences financial management behavior. A solid foundation in financial knowledge, encompassing personal and corporate finance, leads to improved financial practices. This validates theories about cognitive biases, heuristics, and practical financial decision-making. Additionally, it emphasizes the pivotal role of positive financial attitudes in shaping financial management behavior. Stakeholders with constructive attitudes, such as financial self-control and proactive financial approaches, make sound financial decisions. This aligns with theories of planned behavior, which underscore attitudes in guiding future financial actions. Conversely, the influence of financial technology (Fintech) on MSME financial behavior is limited, primarily due to a lack of awareness and understanding among small and micro-enterprise operators regarding Fintech products and services. This study underscores the importance of promoting financial literacy, nurturing positive financial attitudes, and enhancing Fintech awareness among MSMEs. It offers actionable insights for policymakers, financial institutions, and business support organizations, enabling them to bolster MSMEs’ financial knowledge and practices, thereby supporting their role as economic powerhouses.

Comparative Analysis of Financial Distress Models in Indonesian Multi-Industrial Manufacturing During COVID-19

Siska Fifi Andriani, Liana Rahardja, Adi Rizfal Efriadi, Ramitha Janira Cindi — Tue, 31 Oct 2023 00:00:00 +0700

Financial distress is a critical financial condition characterized by a company’s financial performance decline, resulting in reduced net income and challenges in meeting short- and long-term financial obligations. Left unaddressed, financial distress can ultimately lead to bankruptcy. Various predictive models, including the Altman Z-Score, Springate, Grover, Zmijweski, and Zavgren methods, are employed to forecast such distress. This study aims to assess the predictive accuracy of these models in analyzing financial distress and predicting bankruptcy across a diverse spectrum of manufacturing companies. Employing a quantitative and descriptive methodology, the research focuses on manufacturing firms listed on the IDX for 2016-2020, encompassing the impact of the COVID-19 pandemic. Data collection employs a purposive sampling method, with statistical analysis involving the computation of financial ratios from each bankruptcy prediction model. The study assesses the accuracy levels and error types of these models. Results indicate that Altman Z-Score, Springate, Grover, and Zmijweski demonstrated accuracy rates of 46.15%, 35.90%, 82.05%, and 69.23%, respectively. These models exhibit various error types and rates. In contrast, the Zavgren method displayed a remarkable accuracy rate of 100%, with no identified errors, establishing it as the most reliable predictor of bankruptcy, particularly within the Multi-Industrial Manufacturing Sector.