Digitalization has significant power to boost the performance of small and medium-sized enterprises (SMEs). Most scholars approached the phenomenon from different standpoints by examining the effects of information technology skills, digital skills, information systems capabilities, information and communication technologies (ICT) adoption, and digitalization on firm performance. However, although the terms are interrelated, the impact of digital maturity on SMEs' performance has been rarely investigated. Digital maturity examines the extent of digitalization and digital capabilities of organizations, which is essential in evaluating state-of-the-art organizations' digitalization efforts and providing a roadmap for further improvement. Although digitalization offers various benefits to organizations, its direct effect on the performance outcome is still arguable. Prior studies had revealed mixed findings when they assessed the impact of digital maturity on firm performance. Hence, some studies called for research for the examination of possible intervening variables. Consequently, this study examines the effects of digital maturity and organizational agility on firm performance. Furthermore, the moderating role of organizational agility in the digital maturity-firm performance link is also investigated. Data for testing the proposed model was collected from SMEs in the Marmara Region of Turkey using a convenience sampling technique. The structural equation modeling with partial least squares estimation (PLS-SEM) approach was used to analyze 119 responses collected from SMEs via face-to-face and online questionnaires. Eighty-two of the respondents identified themselves as entrepreneurs/partners. 13 respondents are senior executives, 14 are mid-level managers, 8 are white-collar employees, and two stated that they work in different positions than these. The findings show that organizational agility and digital maturity have a positive impact on firm performance. However, when the role of organizational agility is examined as a moderator, it is discovered that the factor reduces the effect of digital maturity on firm performance.

Purpose: The primary purpose of this research is to analyze the online user reviews, where real customer experiences can be observed, with text mining and machine learning approaches, which are seen as a gap in the related literature. This study aims to compare the latent themes uncovered by the topic modeling approach with studies focused on both mobile banking (m-banking) adaptation and service quality features, suggest new aspects and examine the effect of latent topics on customer satisfaction. Design/methodology/approach: This study analyzed 21,526 reviews posted by customers of private and state banks operating in Türkiye. An unsupervised machine learning method, Latent Dirichlet algorithm (LDA), was conducted to reveal topics, and the distribution of all reviews was visualized with the t-SNE algorithm. Random Forest, logistic regression, k-nearest neighbors (kNN) and Naive Bayes algorithms were utilized to predict user satisfaction through the given score. Findings: In total, 11 topics were revealed by considering user reviews based on their experience. Among these topics, perceived usefulness and convenience and time-saving are much more important in the scoring given to m-banking apps. Furthermore, in more detail, seven topics have been identified related to technical and security problems related to m-banking apps. Originality/value: This paper is a pioneer study regarding the method used and sample size reached in the m-banking literature. The findings also provide fresh insight into the post-Covid-19 era, both academically and practically, by providing new features for mobile bank adoption.

The COVID-19 pandemic has deeply affected the airline industry, as it has many sectors, and has created tremendous financial pressure on companies. Flight bans, new regulations, and restrictions increase consumer complaints and are emerging as a big problem for airline companies. Understanding the main reasons triggering complaints and eliminating service failures in the airline industry will be a vital strategic priority for businesses, while reviewing the dimensions of service quality during the COVID-19 pandemic provides an excellent opportunity for academic literature. In this study, 10,594 complaints against two major airlines that offer full-service and low-cost options were analyzed with the Latent Dirichlet Allocation algorithm to categorize them by essential topics. Results provide valuable information for both. Furthermore, this study fills the gap in the existing literature by proposing a decision support system to identify significant service failures through passenger complaints in the airline industry utilizing e-complaints during an unusual situation such as the COVID-19 pandemic.

Micro‐mobility services, which are considered a sustainable alternative to traditional transportation modes, have gained substantial popularity due to advancements in mobile technology. As one of those modes of transportation, shared e‐scooter services have encouraged several startups in urban areas, allowing them to reach massive numbers of consumers in a highly competitive environment. This study aims to explore gains and barriers that affect the intention of consumers to use shared e‐scooter services, all within the framework of sustainability‐driven considerations. The Latent Dirichlet Allocation (LDA) Algorithm was used to analyse 24.798 reviews from the Google Play Store, uncovering eight topics. Those topics were used to discover customer value perceptions in the shared e‐scooter context and compare them with the related literature on perceived value. Besides, their impact on user ratings on the mobile application platform was measured using machine learning algorithms. The study's findings are expected to contribute to developing regulations for shared e‐scooter services, which have gained popularity as an eco‐friendly mode of transportation sustainability in urban areas by introducing a novel perspective.

Companies must retain their customers and maintain long-term relationships in industries with intense competition. Customer churn analysis is defined in the literature as identifying customers who may leave a company to take appropriate marketing precautions. While customer churn research is prevalent in B2C (Business to Customer) business models such as the telecoms and retail sectors, customer churn analysis in B2B (business to business) models is a relatively emerging topic. In this regard, the study carried out a customer churn analysis by considering an ERP (enterprise resource planning) company with a software as a service (SaaS) business model. Different machine learning algorithms analyzed ten features determined by selection methods and expert opinions. According to the analysis results, the random forest algorithm gave the best result. Additionally, it has been observed that the number of products and customer features has a relatively higher weight for the prediction of churner.

Recently, 3D printers have become increasingly prevalent. In addition to being an alternative to conventional manufacturing techniques for professionals and supporting the manufacturing environment, there is a growing opportunity for obtaining the benefits of 3D printers in home settings. Since the number of empirical studies investigating why consumers have intention to use home 3D printers is very limited, this study aims to contribute to the relevant field. A mixed-method research approach was implemented and a model integrating perceived usefulness, ease of use, enjoyment and personal innovativeness was tested on a sample of 416 participants by employing structural equation modelling. Attention was paid to the role of ownership status, and factors affecting intention to use were examined for both owners and non-owners of 3D printers. Findings revealed that the mix of factors and their influence on determining intention to use differed across groups.