Deep Learning-Based User Feedback Classification in Mobile App Reviews

As online users are interacting with many mobile apps under different usage contexts, user needs in an app design process have become a critical issue. Existing studies indicate timely and constructive online reviews from users become extremely crucial for developers to understand user needs and create innovation opportunities. However, discovering and quantifying potential user needs from large amounts of unstructured text is a nontrivial task. In this paper, we propose a domain-oriented deep learning approach that can discover the most critical user needs such as app product new features and bug reports from a large volume of online product reviews. We conduct comprehensive evaluations including quantitative evaluations like F-measure a, and qualitative evaluations such as a case study to ensure the quality of discovered information, specifically, including the number of bug reports and feature requests. Experimental results demonstrate that our proposed supervised model outperforms the baseline models and could find more valuable information such as more important keywords and more coherent topics. Our research has significant managerial implications for app developers, app customers, and app platform providers

A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews

Online reviews provide important demand-side knowledge for product manufacturers to improve product quality. However, discovering and quantifying potential products’ defects from large amounts of online reviews is a nontrivial task. In this paper, we propose a Latent Product Defect Mining model that identifies critical product defects. We define domain-oriented key attributes, such as components and keywords used to describe a defect, and build a novel LDA model to identify and acquire integral information about product defects. We conduct comprehensive evaluations including quantitative and qualitative evaluations to ensure the quality of discovered information. Experimental results show that the proposed model outperforms the standard LDA model, and could find more valuable information. Our research contributes to the extant product quality analytics literature and has significant managerial implications for researchers, policy makers, customers, and practitioners

Do Facebook Activities Increase Sales?

Facebook is a one of the most popular social media platforms and its increasing adoption by business is leading to the shift of traditional marketing to social marketing (Nair 2011). This study investigates two related questions: 1) whether the use of Facebook impacts companies’ sales; 2) whether the increased Facebook activities leads to higher companies’ sales. We find that, on average, companies adopted Facebook have sales 0.1% higher than those not. We also find if a company increases its Facebook posts (interactions) by 1%, its annual sales will increase by roughly 0.06% (0.03%). Our study provides evidence that Facebook activities are significantly and positively associated with companies’ annual sales though their impacts are relatively small in terms of effect size. We also provide caveats to the interpretation of our results and discuss directions for future research

Money Talks: A Predictive Model on Crowdfunding Success Using Project Description

Existing research of crowdfunding mainly focuses on the basic properties of the project such as category and goal, the information content of the project, however, is barely studied. By introducing Elaboration Likelihood Model into crowdfunding context and using a large dataset obtained from Kickstarter, a popular crowdfunding platform, we study the influence of project descriptions in terms of argument quality and source credibility, and investigate their impacts on funding success. We find information disclosed in project descriptions is associated with funding success. We also examine the practical impacts of project description by using a predictive model. Results show that our model can predict with an accuracy rate of 73% (71% in F-measure), which represents an improvement of 15 percentage points over the baseline model and 4 percentage points over the mainstream model. Overall, our results provide insights to researchers, project owners and backers to better study and use crowdfunding platforms

Identifying Product Defects from User Complaints: A Probabilistic Defect Model

The recent surge in using social media has created a massive amount of unstructured textual complaints about products and services. However, discovering potential product defects from large amounts of unstructured text is a nontrivial task. In this paper, we develop a probabilistic defect model (PDM) that identifies the most critical product issues and corresponding product attributes, simultaneously. We facilitate domain-oriented key attributes (e.g., product model, year of production, defective components, symptoms, etc.) of a product to identify and acquire integral information of defect. We conduct comprehensive evaluations including quantitative evaluations and qualitative evaluations to ensure the quality of discovered information. Experimental results demonstrate that our proposed model outperforms existing unsupervised method (K-Means Clustering), and could find more valuable information. Our research has significant managerial implications for mangers, manufacturers, and policy makers

Penguin Has Entered the Home: The Impact of COVID-19 Induced Work From Home on Open Source Software Development

The Open Source Software Development (OSSD) community has been influenced dramatically because the COVID-19 pandemic makes work from home (WFH) the new way of working. While existing studies demonstrate the ef ects of WFH on the productivity of developers, few studies shed light on its impact on contribution to OSSD. The current study investigates the impact of disruptive events like COVID-19 and the changing working conditions on sustained contribution towards open source software development as a community service. The preliminary results based on an analysis of the GitHub ecosystem illustrate that the overall OSSD contributions have witnessed an increase post WFH. Our study has important theoretical and practical implications for scholars and open-source platform owners and users

Competing for Temporary Advantage in a Hypercompetitive Mobile App Market

Hypercompetitive mobile app stores are characterized by rapid innovation and intense competition. App firms must vie for temporary competitive advantage through competitive actions such as releasing product improvements. We study how competitive indicators influence a particular competitive action—app updates—in a mobile game app market. Our results reveal that app firms take action to improve or sustain their temporary competitive advantage, updating their apps when there are opportunities to capitalize on popularity (e.g., rank and rating volume are increasing) and when their apps’ advantages are threatened (e.g., customer ratings are decreasing). We also find that app updates are released in response to competitors’ actions—specifically, when competitors update their apps and new competitors enter ranking lists. Moreover, our findings show that app firms release app updates when an app’s customer rating volume is increasing or when an app firm’s portfolio of game apps is less diverse, relative to its competitors. We conduct additional analyses that show that older app firms are responsive to more competitive indicators than younger ones and major updates are primarily used to respond to serious threats to apps’ competitive positions. Overall, our results indicate that app updates are competitive actions used to improve or sustain temporary advantage when competitive indicators reveal opportunities to improve or threats to apps’ competitive positions

Metabolomics reveals metabolic changes in male reproductive cells exposed to thirdhand smoke.

Thirdhand smoke (THS) is a new term for the toxins in cigarette smoke that linger in the environment long after the cigarettes are extinguished. The effects of THS exposure on male reproduction have not yet been studied. In this study, metabolic changes in male germ cell lines (GC-2 and TM-4) were analyzed after THS treatment for 24 h. THS-loaded chromatography paper samples were generated in a laboratory chamber system and extracted in DMEM. At a paper: DMEM ratio of 50 μg/ml, cell viability in both cell lines was normal, as measured by the MTT assay and markers of cytotoxicity, cell cycle, apoptosis and ROS production were normal as measured by quantitative immunofluorescence. Metabolomic analysis was performed on methanol extracts of GC-2 and TM-4 cells. Glutathione metabolism in GC-2 cells, and nucleic acid and ammonia metabolism in TM-4 cells, was changed significantly by THS treatment. RT-PCR analyses of mRNA for enzyme genes Gss and Ggt in GC-2 cells, and TK, SMS and Glna in TM-4 cells reinforced these findings, showing changes in the levels of enzymes involved in the relevant pathways. In conclusion, exposure to THS at very low concentrations caused distinct metabolic changes in two different types of male reproductive cell lines