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The Impact of Customer Reviews on Product Innovation: Empirical Evidence in Mobile Apps
Abstract
Product innovation is important for firms to gain competitive advantages in a dynamic business environment. Traditionally, customers are not very much involved in product innovation processes. With the technology of Web 2.0, online users are enabled and motivated to provide reviews and discussions about product features and use experiences. User generated product reviews have been found to have a word-of-mouth effect as a new element of marketing communication. However, their implication on improving product innovation cycles have not been studied before. Guided by a persuasion theory, we extracted the central and peripheral persuasion cues from user generated reviews and examined their impact on mobile app developers’ product innovation decisions. Using data collected from the Google App store, our empirical study shows that long and easy-to-read user reviews with mildly negative reviews can increase the likelihood of a future mobile app update. Our findings highlight the need for researchers to explore user generated reviews in the context of customer-centered product innovation.
... UGC is a powerful kind of data that includes important information for both health applications' users and service providers (Haggag et al., 2022). This information provides the service providers understanding of the major factors that impact the customers' satisfaction in order to help them update their product regularly, increase publicity, and refine their marketing strategy (Qiao et al., 2018). The data collection stage was performed to collect the UGC located on the SoftwareAdvise. ...
The growing population has increased the demand for people to benefit from home health care (HHC) agencies, therefore, raising the need to use home health software (HHS) products to improve the quality of service and management of HHC companies. HHS products play a key role in managing and scheduling appointments, which leads to saving time and improving the productivity of HHC companies. Analyzing online reviews of HHS products helps the decision-makers in improving their products which leads to more satisfied customers (i.e. HHC agencies) and increases the quality of the HHS applications. In this study, a total of 5338 online reviews were gathered from the SoftwareAdvise website using a customized crawler. After data preprocessing and imputation, machine learning (ML) techniques including the Latent Dirichlet Allocation (LDA) topic model were used to reveal the major topics that influenced the customers' perception. Self-organized maps (SOM) were utilized to segment the customers according to their ratings behavior which eventually led to six groups. Finally, classification and regression trees (CART) were used to predict customer outcomes based on criteria ratings. The results of this study revealed that customers have similar rating behaviors in terms of ease of use and functionality criteria in HHS. In addition, users of HHS were interested mostly in ten features including scheduling, management, support, privacy, integration, ease of use, accessibility, controllability, flexibility, and efficiency. These outcomes help researchers define their features while delivering surveys for evaluating customers' satisfaction with HHS products.
... Many customers use SS through mobile apps. In addition, users of the applications disclose their satisfaction with the services provided publicly through online assessments (Qiao et al., 2018). Identifying the aspects resulting from the CIU of the service is important for service providers, as it influences consumer behaviour, such as electronic word-of-mouth (e-WOM) (Kim et al., 2022). ...
... Temuan ini mendukung penelitian sebelumnya yang menemukan bahwa intensitas kegiatan penelitian dan pengembangan memiliki dampak positif yang signifikan terhadap kinerja inovasi suatu negara (Savrul & Incekara, 2015). Penelitian ini juga sejalan dengan penelitian Qiao et al. (2018) yang menemukan bahwa informasi yang diberikan oleh pelanggan mampu meningkatkan kinerja inovasi. Hal ini juga menegaskan penelitian sebelumnya yang menyatakan bahwa kegiatan penginderaan memiliki dampak yang signifikan dalam hal peningkatan kinerja inovasi produk, proses, dan layanan. ...
Kemampuan untuk memiliki respon yang gesit sangat penting bagi UMKM untuk menghadapi pasar yang cepat berubah ini. Penelitian ini mengkaji pengaruh tiga elemen dasar kapabilitas dinamis, yaitu kemampuan sensing, seizing, dan reconfiguring terhadap kinerja inovasi dan keunggulan kompetitif berkelanjutan pada UMKM berbasis industri kreatif di Jember, Indonesia. Sebanyak 95 UMKM kreatif dilibatkan dalam penelitian ini. Temuan penelitian ini menawarkan implikasi teoritis dan praktis untuk meningkatkan kinerja inovasi dan menciptakan keunggulan kompetitif yang berkelanjutan dengan menerapkan kapabilitas dinamis di sektor UMKM kreatif.
... Furthermore, it enables them to regularly update their apps to improve the user experience (Li et al. 2018). In addition, it allows them to gain accurate insights into their users, which enables them to refine their marketing strategies and increase publicity (Vasa et al. 2012;Qiao et al. 2018). ...
The global mHealth app market is rapidly expanding, especially since the COVID-19 pandemic. However, many of these mHealth apps have serious issues, as reported in their user reviews. Better understanding their key user concerns would help app developers improve their apps’ quality and uptake. While app reviews have been used to study user feedback in many prior studies, many are limited in scope, size and/or analysis. In this paper, we introduce a very large-scale study and analysis of mHealth app reviews. We extracted and translated over 5 million user reviews for 278 mHealth apps. These reviews were then classified into 14 different aspects/categories of issues reported. Several mHealth app subcategories were examined to reveal differences in significant areas of user concerns, and to investigate the impact of different aspects of mhealth apps on their ratings. Based on our findings, women’s health apps had the highest satisfaction ratings. Fitness activity tracking apps received the lowest and most unfavourable ratings from users. Over half of users who reported troubles leading them to uninstall mHealth apps gave a 1-star rating. Half of users gave the account and logging aspect only one star due to faults and issues encountered while registering or logging in. Over a third of users who expressed privacy concerns gave the app a 1-star rating. However, only 6% of users gave apps a one-star rating due to UI/UX concerns. 20% of users reported issues with handling of user requests and internationalisation concerns. We validated our findings by manually analysing a sample of 1,000 user reviews from each investigated aspect/category. We developed a list of recommendations for mHealth apps developers based on our user review analysis.
With the development of big data analytics, consumers' online reviews are becoming increasingly useful for product innovation with hidden innovative ideas that can be extracted. However, these ideas may be only hidden in a small part of the massive reviews. This study aims to investigate the potential of using anomaly detection technology to identify unique reviews for more effective innovation generation. Three classical anomaly detection approaches (including both machine and deep learning) were explored, namely, isolation forest, density-based cluster analysis, and autoencoder methods. Using the consumer reviews on Dyson Vacuum cleaner from Xiaohongshu (one of the most popular social media platforms in China), we tested and compared the application of these three approaches in detecting innovation-relevant reviews. The results indicate that the two machine learning approaches, aka., density-based cluster analysis and isolation forest are too sensitive to the length of the reviews. The deep learning method, autoencoder, on the contrary, shows good stability and capability to detect the unique reviews from the whole dataset. Furthermore, the experts’ rating also confirms the effectiveness of autoencoder in identifying innovation-relevant reviews. Future studies and implications are then discussed.
The growth in mobile applications (apps) is rapid and immense. What was once a technology to satisfy the basic needs of a mobile user has now become a huge source of entertainment, education, games, etc. App development is iterative and incremental; therefore, they are released in improved versions to achieve higher app success in both functional and non-functional aspects. In this generic setting, the increasing prevalence of user reviews and generated ratings offers a wealth of insights, especially for post-development analysis. However, user reviews can be highly variable and inconsistent, as the informal nature of the language allows for misinterpretation, leading to a daunting task. In this paper, an empirical analysis is conducted to assert the effectiveness of NLP techniques in modeling the semantic contexts of user reviews in embedding vectors for predicting mobile app ratings. Here, each contextual macro-category is preprocessed with SMOTE to handle skewed distribution and then subjected to 4 feature selection techniques, e.g. PCA, ANOVA, etc. In the last step, 13 different prediction models (classifiers) are fitted to predict the mobile app rating. The study confirms significantly improved predictive performance in Glove-T and GloVe-W equipped word embedding models. Among prediction models, logistic regression (LOGR) outperforms the equivalent with an overall predictive ability of a greater extent, with an average accuracy score of 67, an average AUC of 0.70, and an F-measure of 0.72, indicative of its high functioning with numerical data and word vectors.
Purpose
Online customer reviews is an important information resource for product innovation. This study aims to investigate the impact of online customer reviews on iterative innovation of software products and the moderating roles of product complexity in the process of online reviews influencing product iterative innovation.
Design/methodology/approach
To empirically test the hypotheses, this paper built a panel data of 500 software products from 2019 to 2021 and applied Poisson regression analysis.
Findings
Empirically results reveal that both sentiment and quantity of online customer reviews have positive effects on iteration innovation of software products. In addition, the authors find that product complexity negatively moderates the relationship between online reviews and iterative innovation.
Practical implications
This study suggests that firms can acquire valuable information from customers’ online reviews for product iterative innovation and improvement. However, for high-complexity products, it may be difficult for enterprises to obtain useful information for iterative innovation from online reviews. On the other hand, this study provides a reference for firms to choose more useful online reviews from the perspective of sentiment.
Originality/value
This paper provides a new finding that there is a positive relationship between online customer reviews and iterative innovation of software products. Moreover, the authors also provide a deeper understanding of how online customer reviews affects iterative innovation by examining the moderating roles of product complexity.
With the development of information technology, the subscription economy and streaming service market have grown rapidly. In particular, music streaming services have disrupted the traditional music industry. Nevertheless, few attempts have been made to understand music streaming services in terms of overall customer satisfaction. This study analyzes social media data to investigate the determinants of customer satisfaction in music streaming services. Topic modeling and text regression were applied to online app reviews for five music streaming services. This study finds that customers comment on factors related to usage environments, price plans, and content. All environment-related factors, some pricing-related factors, and content-related factors have a significant effect on customer satisfaction. In addition, the satisfaction determinants differ for each service. This study is an early attempt to analyze music streaming services from a data-driven perspective and contributes to a comprehensive understanding of music streaming services from the customer's point of view.
In recent years, online reviews have offered a rich new medium for consumers to express their opinions and feedback. Product designers frequently aim to consider consumer preferences in their work, but many firms are unsure of how best to harness this online feedback given that textual data is both unstructured and voluminous. In this study, we use text mining tools to propose a method for rapid prioritization of online reviews, differentiating the reviews pertaining to innovation opportunities that are most useful for firms. We draw from the innovation and entrepreneurship literature and provide an empirical basis for the widely accepted attribute mapping framework, which delineates between desirable product attributes that firms may want to capitalize upon and undesirable attributes that they may need to remedy. Based on a large sample of reviews in the countertop appliances industry, we demonstrate the performance of our technique, which performs statistically significantly better than existing methods. We validate the usefulness of our technique by asking senior managers at a large manufacturing firm to rate a selection of online reviews, and we show that the selected attribute types are more useful than alternative reviews. Our results offer insight in how firms may use online reviews to harness vital consumer feedback.
For many sectors like health care, financial services, or renewable energy, new products and services are generated by an ecology of business firms, nonprofit foundations, public institutions, and other agents. Knowledge to innovate is dispersed across ecologies, so no single firm or small group of firms can innovate alone. Moreover, many new products and services in ecologies such as health care or energy are complex or comprise many parts with unknown interactions. New products, knowledge, business models, and applications all emerge unpredictably over considerable time periods, as various agents in the ecologies of innovation interact with and react to the actions of others. However, the existing organizing structure in these ecologies stifles emergence and precludes much innovation, simply because theory and practice do not adequately address how to organize for complex innovation. We develop a preliminary model for organizing ecologies of complex innovation. We suggest that innovations can continually emerge productively if people work locally in ecologies to set and solve problems of orchestrating knowledge capabilities across the ecology, strategizing across the ecology to create new businesses and applications, and developing public policies to embrace ambiguity. Using examples from biopharmaceuticals and alternative energy, we develop specific organizing ideas that can be examined and elaborated upon. This new direction for organization science integrates existing ideas around a new kind of organizing and shows how organization science can add real value in addressing major challenges of public welfare and safety in the 21st century.
Our increasing reliance on mobile devices has led to the explosive development of millions of mobile apps across multiple platforms that are used by millions of people around the world every day. However, most software engineering research is performed on large desktop or server-side software applications (e.g., Eclipse and Apache). Unlike the software applications that we typically study, mobile apps are 1) designed to run on devices with limited, but diverse, resources (e.g., limited screen space and touch interfaces with diverse gestures) and 2) distributed through centralized "app stores," where there is a low barrier to entry and heavy competition. Hence, mobile apps may differ from traditionally studied desktop or server side applications, the extent that existing software development "best practices" may not apply to mobile apps. Therefore, we perform an exploratory study, comparing mobile apps to commonly studied large applications and smaller applications along two dimensions: the size of the code base and the time to fix defects. Finally, we discuss the impact of our findings by identifying a set of unique software engineering challenges posed by mobile apps.
With the ever-increasing popularity of online consumer reviews, understanding what makes an online review believable has attracted increased attention from both academics and practitioners. Drawing on the elaboration likelihood model (ELM), this study examines four information cues used to evaluate the credibility of online reviews: Argument quality, source credibility, review consistency, and review sidedness, under different levels of involvement and expertise. We conducted an online survey that involved users of Epinions.com, a popular online consumer review website, to test the research model empirically. Consistent with previous research, the results reveal that argument quality, a central cue, was the primary factor affecting review credibility. Participants also relied on peripheral cues such as source credibility, review consistency, and review sidedness when evaluating online consumer reviews. Review sidedness had a stronger impact on review credibility when the recipient had a low involvement level and a high expertise level. However, the other interaction effects were not significant. We discuss the theoretical and practical implications of these results.
The proliferation of product review websites produces a large, publicly accessible information resource for firms that seek to understand consumers’ preferences. To facilitate product design or improvement, we propose a novel econometric preference measurement model, the modified ordered choice model (MOCM), to extract aggregate consumer preferences from online product reviews. Moreover, to categorize customer requirements on the basis of the aggregate consumer preferences estimated by the MOCM model, we extend the Kano model and propose a marginal effect-based Kano model (MEKM). We empirically evaluate the effectiveness of the proposed MOCM model and demonstrate the utility of the proposed MEKM model.
In this study, we focus on the factors that influence online innovation community members' continued participation in the context of open source software development (OSSD) communities. Prior research on continued participation in online communities has primarily focused on social interactions among members and benefits obtained from these interactions. However, members of these communities often play different roles, which have been examined extensively, albeit in a separate stream of research. This study attempts to bridge these two streams of research by investigating the joint influence of community response and members' roles on continued participation. We categorize OSSD community members into users and modifiers and empirically examine the differential effects of community response across these roles. By analyzing a longitudinal data set of activities in the discussion forums of more than 300 OSSD projects, we not only confirm the positive influence of community response on members' continued participation but also find that community response is more influential in driving the continuance behavior of users than that of modifiers. In addition, this research highlights the importance of modifiers, a key subgroup of OSSD participants that has been largely overlooked by prior research.
Online product reviews contain valuable information regarding customer requirements (CRs). Intelligent analysis of a large volume of online CRs attracts interest from researchers in various fields. However, many research studies only concern sentiment polarity in the product feature level. With these results, designers still need to read a list of reviews to absorb comprehensive CRs. In this research, online reviews are analyzed at a fine-grained level. In particular, aspects of product features and detailed reasons of consumers are extracted from online reviews to inform designers regarding what leads to unsatisfied opinions. This research starts from the identification of product features and the sentiment analysis with the help of pros and cons reviews. Next, the approach of conditional random fields is employed to detect aspects of product features and detailed reasons from online reviews jointly. In addition, a co-clustering algorithm is devised to group similar aspects and reasons to provide a concise description about CRs. Finally, utilizing customer reviews of six mobiles in Amazon.com, a case study is presented to illustrate how the proposed approaches benefit product designers in the elicitation of CRs by the analysis of online opinion data.
Disruptive innovations introduce a new performance dimension into a product category, but often suffer from inferior performance on key performance dimensions of their existing substitutes. Hence, the followers of these innovations face an important decision to make: they must choose to improve the new technology either on the key performance dimension shared with the old technology or on the new performance dimension. This paper investigates which path firms should choose when they face such a dilemma in the absence of any cost or capability issues. In doing so, we integrate customer response into the theory of technological evolution and allow preferences on the product choices to be context dependent. We show that context-dependent preferences may encourage the follower to improve the new technology on the new performance dimension. Later, we extend our game to a dynamic one and show that the context-dependent preferences may cause the pioneer to innovate less.
This paper was accepted by J. Miguel Villas-Boas, marketing.
Although universally recognized as an important consideration in building product development (PD) competency, the effect of a firm's ability to vary its PD practices to develop winning products has been given scant attention in large-scale, multiorganizational, quantitative studies. This research explores differences in formal new PD practices among three project types—incremental, more innovative, and radical. Using a sample of 380 business units, this research investigates how development practices differ across these three classes of innovation with respect to the formal PD process, project organization, PD strategy, organizational culture, and senior management commitment. Our results diverge from several commonly held beliefs about formal PD processes and the management of radical versus incremental innovations. Our results indicate that radical projects are managed less flexibly than incremental projects. Instead of being an offshoot of less strategic planning, radical projects are just as strategically aligned as incremental projects. Instead of being informally introduced entrepreneurial adventures, radical projects are often the result of more formal ideation methods. While these results may be counterintuitive to suppositional models of how to radical innovation happens, it is the central theme of this research to show how radical innovation actually happens. Our findings also provide a foundation for reexamining the role of control in the management of innovation. As the level of innovativeness increased, so too did the amount of controls imposed—e.g., less flexibility in the development process, more professional, full-time project leadership, centralized executive oversight for new products, and formal financial assessments of expected NP performance.
Manufacturing firms employ different strategies to improve the performance of their products. In this study, we investigate the roles of environmental dynamism and competitive intensity as antecedents of product and process innovation strategy implementation. Furthermore, we investigate the effect of these initiatives on product innovation performance and product quality performance in manufacturing firms. Based on an analysis of a sample of 207 firms in Australia, the results reveal that environmental dynamism positively relates to both product and process innovation strategy implementation while competitive intensity positively relates to only process innovation strategy implementation. Moreover, product innovation strategy implementation positively relates to product innovation performance while process innovation strategy implementation positively affects product quality performance. Both product innovation performance and product quality performance are found to positively affect business performance. The theoretical and practical implications of the study in the context of Australian manufacturing are discussed.