The Dimensions of Review Comprehensiveness and Its Effect on Review Usefulness: A Latent Dirichlet Allocation Approach

Online review sites like Yelp.com, TripAdvisor.com and AngiesList.com provide values to both business and consumers. A large body of literature investigates drivers of online review usefulness. Review comprehensiveness has been identified as one the most important dimension of review quality and an important predictor of review usefulness. This study contributes to the literature by crafting and operationalizing review comprehensiveness using a text mining approach. We also empirically test the effect of the operationalized review comprehensiveness construct on review usefulness. In practice, online review providers, especially Yelp.com, can benefit from this study by integrating review comprehensiveness in their sorting algorithms

Foundations for knowledge advancement and relevance to practice

Madureira, L., Popovic, A., & Castelli, M. (2023). Competitive Intelligence Empirical Validation and Application: Foundations for Knowledge Advancement and Relevance to Practice. Journal of Information Science. https://doi.org/10.1177/01655515231191221--- The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - IDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMSThe competitive intelligence (CI) construct must be scientifically defined, characterised, empirically validated and accurately measured to grow in science and business. This study aims at elevating the accuracy of the empirical validation of the CI construct suggested and confirmed by Madureira, Popovic and Castelli to serve as the scientific foundation for CI praxis. This construct is selected due to its unmatched recency, thoroughness, universality identified limitations of its empirical validation. We relied on a multistrand design of fully sequential with equivalent status qualitative and quantitative mix-methods followed by the triangulation of the findings and the development of the meta-inferences. Validity, reliability and applicability were tested using computer-aided text analysis and artificial intelligence methods based on 61 in-depth interviews with CI subject matter experts. Contributions to knowledge advancement and relevance to practice derive from the scientific-grade empirical construct validation, providing undisputed levels of accuracy, consistency, applicability, and triangulation of results. This study highlights three critical implications. First, the delimitation of the body of knowledge and recognition of the CI domain serve as the baseline for theory development. Second, the validated construct guarantees reproducibility, replicability and generalisability, laying the foundations for establishing CI science, practice and education. Third, creating a common language and shared understanding will drive the much-claimed definitional consensus. This study thus stands as a foundational pillar in supporting CI praxis in improving decision-making quality and the performance of organisations.publishersversionepub_ahead_of_prin

A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems

Creating product ecosystems has been one of the strategic ways to enhance user experience and business advantages. Among many, customer needs analysis for product ecosystems is one of the most challenging tasks in creating a successful product ecosystem from both the perspectives of marketing research and product development. In this paper, we propose a machine-learning approach to customer needs analysis for product ecosystems by examining a large amount of online user-generated product reviews within a product ecosystem. First, we filtered out uninformative reviews from the informative reviews using a fastText technique. Then, we extract a variety of topics with regard to customer needs using a topic modeling technique named latent Dirichlet allocation. In addition, we applied a rule-based sentiment analysis method to predict not only the sentiment of the reviews but also their sentiment intensity values. Finally, we categorized customer needs related to different topics extracted using an analytic Kano model based on the dissatisfaction-satisfaction pair from the sentiment analysis. A case example of the Amazon product ecosystem was used to illustrate the potential and feasibility of the proposed method.https://deepblue.lib.umich.edu/bitstream/2027.42/153965/1/A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems.pd

Producing power-law distributions and damping word frequencies with two-stage language models

Standard statistical models of language fail to capture one of the most striking properties of natural languages: the power-law distribution in the frequencies of word tokens. We present a framework for developing statisticalmodels that can generically produce power laws, breaking generativemodels into two stages. The first stage, the generator, can be any standard probabilistic model, while the second stage, the adaptor, transforms the word frequencies of this model to provide a closer match to natural language. We show that two commonly used Bayesian models, the Dirichlet-multinomial model and the Dirichlet process, can be viewed as special cases of our framework. We discuss two stochastic processes-the Chinese restaurant process and its two-parameter generalization based on the Pitman-Yor process-that can be used as adaptors in our framework to produce power-law distributions over word frequencies. We show that these adaptors justify common estimation procedures based on logarithmic or inverse-power transformations of empirical frequencies. In addition, taking the Pitman-Yor Chinese restaurant process as an adaptor justifies the appearance of type frequencies in formal analyses of natural language and improves the performance of a model for unsupervised learning of morphology.48 page(s

Selecting the appropriate leading journal in Hospitality and Tourism research: a guide based on the topic-journal fit and the JCR impact factor

Selecting the appropriate academic journal is a priority issue for researchers in the process of publishing a manuscript. If researchers could quantify the research topic in terms of its fit with the journal requirements before the submission of a paper, then the assessment of journal suitability could be much easier. Basing any decision on journal impact factors alone might obviously result in a mismatch, eventual rejection and a consequent loss of time. Taking the twelve leading Tourism and Hospitality journals as a reference, the main research topics mentioned in the abstracts of 20,381 articles are identified, using the Latent Dirichlet Allocation algorithm and other text-mining techniques running the R programming language. Subsequently, a quantitative measure of the fit of the research topics in each journal is offered according to their frequency of occurrence. The results suggested that the importance of the topic-journal fit with respect to the impact factor depended on the variance of the fits among the journals. Finally, a guide of the most suitable journals for the topics is presented, based on the JCR impact factor and the fit of the topic. Some recommendations are likewise offered on the use of this methodology and its limitations

A Framework to Evaluate Software Developer’s Productivity The VALORTIA Project

Currently, there is a lack in companies developing software in relation to assessing their staff’s productivity before executing software projects, with the aim of improving effectiveness and efficiency. QuEF (Quality Evaluation Framework) is a framework that allows defining quality management tasks based on a model. The main purpose of this framework is twofold: improve an entity’s continuous quality, and given a context, decide between a set of entity’s instances on the most appropriate one. Thus, the aim of this paper is to make this framework available to evaluate productivity of professionals along software development and select the most appropriate experts to implement the suggested project. For this goal, Valortia platform, capable of carrying out this task by following the QuEF framework guidelines, is designed. Valortia is a platform to certify users' knowledge on a specific area and centralize all certification management in its model by means of providing protocols and methods for a suitable management, improving efficiency and effectiveness, reducing cost and ensuring continuous quality.Ministerio de Ciencia e Innovación TIN2013-46928-C3-3-

Managing cyber risk in organizations and supply chains

In the Industry 4.0, modern organizations are characterized by an extensive digitalization and use of Information Technology (IT). Even though there are significant advantages in such a technological progress, a noteworthy drawback is represented by cyber risks, whose occurrence dramatically increased over the last years. The information technology literature has shown great interested toward the topic, identifying mainly technical solutions to face these emerging risks. Nonetheless, cyber risks cause business disruption and damages to tangible and intangible corporate assets and require a major integration between technical solutions and a strategic management. Recently, the risk management domain and the supply chain literature have provided studies about how an effective cyber risk management process should be planned, to improve organizational resilience and to prevent financial drawbacks. However, the aforementioned studies are mainly theoretical and there is still a significant lack of empirical studies in the management literature, measuring the potential effects of cyber threats within single companies, and along networks of relationships, in a wider supply chain perspective. The present thesis aims at filling some of these gaps through three empirical essays. The first study has implemented a Grounded Theory approach to develop an interview targeting 15 European organizations. Afterwards, the fuzzy set Qualitative Comparative Analysis (fsQCA) has been performed, in order to ascertain how managers perceive cyber risks. Results contradict studies that focus merely on technical solution, and con\ufb01rm the dynamic capability literature, which highlights the relevance of a major integration among relational, organizational, and technical capabilities when dealing with technological issues. Moreover, the study proposes a managerial framework that draws on the dynamic capabilities view, in order to consider the complexity and dynamism of IT and cyber risks. The framework proposes to implement both technical (e.g. software, insurance, investments in IT assets) and organizational (e.g. team work, human IT resources) capabilities to protect the capability of the company to create value. The second essay extends the investigation of the drawbacks of cyber risks to supply chains. The study conducts a Grounded Theory empirical investigation toward several European organizations that rely on security and risk management standards in order to choose the drivers of systematic IT and cyber risk management (risk assessment, risk prevention, risk mitigation, risk compliance, and risk governance). The evidence gleaned from the interviews have highlighted that investments in supply chain mitigation strategies are scant, resulting in supply chains that perform like they had much higher risk appetite than managers declared. Moreover, it has emerged a general lack of awareness regarding the effects that IT and cyber risks may have on supply operations and relationships. Thus, a framework drawing on the supply chain risk management is proposed, offering a holistic risk management process, in which strategies, processes, technologies, and human resources should be aligned in coherence with the governance of each organization and of the supply chain as a whole. The \ufb01nal result should be a supply chain where the actors share more information throughout the whole process, which guarantees strategic bene\ufb01ts, reputation protection, and business continuity. The third essay draws on the Situational Crisis Communication Theory (SCCT) to ascertain whether and how different types of cyber breaches differently affect the corporate reputation, defined as a multidimensional construct in which perceptions of customers, suppliers, (potential) employees, investors and local communities converge. Data breaches have been categorized into three groups by the literature, meaning intentional and internal to the organization (e.g., malicious employees stealing customers\u2019 data), unintentional and internal to the organization (e.g., incorrect security settings that expose private information), and intentional and external to the organization (e.g., ransomware infecting companies\u2019 software). However, this is among the first study to analyse the different reputational drawbacks these types may cause. Moreover, the study considers that, in the industry 4.0 era, social media analysis may be of paramount importance for organizations to understand the market. In fact, user-generated content (UGC), meaning the content created by users, might help in understanding which dimensions of the corporate have been more attacked after a data breach. In this context, the study implements the Latent Dirichlet Allocation (LDA) automated method, a base model in the family of \u201ctopic models\u201d, to extract the reputational dimensions expressed in UGC of a sample of 35 organizations in nine industries that had a data breach incident between 2013 and 2016. The results reveal that in general, after a data breach, three dimensions\u2014perceived quality, customer orientation and corporate performance\u2014 are a subject of debate for users. However, if the data breach was intentional ad malicious, users focused more on the role of firms\u2019 human resources management, whereas if users did not identify a responsible, users focused more on privacy drawbacks. The study complements crisis communication research by categorizing, in a data breach context, stakeholders\u2019 perceptions of a crisis. In addition, the research is informative for risk management literature and reputation research, analysing corporate reputation dimensions in a data breach crisis setting