Obsessive-Compulsive Disorder and Mobile Technology

Obsessive-compulsive disorder (OCD) is characterized by the presence of persistent and unwanted obsessions, which take the form of intrusive thoughts. These lead to widespread anxiety and/or compulsions which take the form of repetitive acts to relieve anxiety. In 2020, for the author's master's degree final project, she decided to propose the creation of a mobile application for people with OCD, whose main purpose was to reduce the anxiety caused by it. Although mobile applications already exist for the treatment of OCD, it was necessary to fill some gaps and improve them. This chapter will examine the techniques that were applied on the investigation of the author's project—a competitor analysis and an exploratory qualitative research—and understand how they can help to retain some information that is beyond the literature review, and what needs to be retained from them to know which features and functionalities are most useful in an app that aims to support therapeutic intervention in individuals with OCD, as well as possible gaps that could be improved.

Examining the Role of Social Media Analytics in Providing Competitive Intelligence

This study examines the role of social media analytics (SMA) in providing competitive intelligence (CI). Building on CI theory, the data from qualitative semi-structured interviews with respondents belonging to social media, manufacturing, telecommunication, IT and service industries were analyzed using Nvivo coding and matrix queries. The results show that SMA provides an expanded CI beyond the previous limits of customers/markets and competitors, including insights on supply chains, costs and information-flow. Moreover, SMA-driven CI can provide visibility to supply chain uncertainties enabling improvements in demand planning and inventory management. SMA can provide CI about competitors’ strengths and weaknesses and customers’ dynamics; however, the bi-directional nature of CI could be determinantal if SM-linked customers are not educated/kept informed. Matrix query results illuminate the differences/similarities in respondents’ views. Academically, the study shows that SMA provides expanded CI to businesses beyond previously known scope of competitor analysis.

Dynamic Measurement and Evaluation of Hotel Customer Satisfaction Through Sentiment Analysis on Online Reviews

Both researchers and practitioners have attached great importance to the measurement and evaluation of hotel customer satisfaction. However, there are several problems in the dimensions, methods and conclusions. Thus, it is urgent to standardize the theories, methods, and techniques. The purpose of this study is to propose a dynamic measurement and evaluation framework for hotel customer satisfaction through sentiment analysis on online reviews. The framework consists of five steps: (1)The corpus is obtained from online review sites; (2)From the perspective of managers, the useful texts are recognized; (3)Based on the useful texts, a three-layer index system is created; (4)The center term-based short sentence sentimental orientation (CTSSSO) algorithm is developed to compute emotional intensity, then dynamically measure the customer satisfaction; (5)The dynamic important performance competitor analysis (DIPCA) is adopted for dynamic evaluation of customer satisfaction.The feasibility of our framework was demonstrated through a case study on the online reviews of two five-star hotels.

Dynamic Measurement and Evaluation of Hotel Customer Satisfaction through Sentiment Analysis on Online Reviews

Both researchers and practitioners have attached great importance to the measurement and evaluation of hotel customer satisfaction. However, there are several problems in the dimensions, methods and conclusions. Thus, it is urgent to standardize the theories, methods, and techniques. The purpose of this study is to propose a dynamic measurement and evaluation framework for hotel customer satisfaction through sentiment analysis on online reviews. The framework consists of five steps: (1)The corpus is obtained from online review sites; (2)From the perspective of managers, the useful texts are recognized; (3)Based on the useful texts, a three-layer index system is created; (4)The center term-based short sentence sentimental orientation (CTSSSO) algorithm is developed to compute emotional intensity, then dynamically measure the customer satisfaction; (5)The dynamic important performance competitor analysis (DIPCA) is adopted for dynamic evaluation of customer satisfaction.The feasibility of our framework was demonstrated through a case study on the online reviews of two five-star hotels.

Examining the Role of Social Media Analytics in Providing Competitive Intelligence

This study examines the role of social media analytics (SMA) in providing competitive intelligence (CI). Building on CI theory, the data from qualitative semi-structured interviews with respondents belonging to social media, manufacturing, telecommunication, IT and service industries were analyzed using Nvivo coding and matrix queries. The results show that SMA provides an expanded CI beyond the previous limits of customers/markets and competitors, including insights on supply chains, costs and information-flow. Moreover, SMA-driven CI can provide visibility to supply chain uncertainties enabling improvements in demand planning and inventory management. SMA can provide CI about competitors’ strengths and weaknesses and customers’ dynamics; however, the bi-directional nature of CI could be determinantal if SM-linked customers are not educated/kept informed. Matrix query results illuminate the differences/similarities in respondents’ views. Academically, the study shows that SMA provides expanded CI to businesses beyond previously known scope of competitor analysis.

Examining the role of social media analytics in providing competitive intelligence- The impacts and limitations

This study examines the role of social media analytics (SMA) in providing competitive intelligence (CI). Building on CI theory, the data from qualitative semi-structured interviews with respondents belonging to social media, manufacturing, telecommunication, IT and service industries were analyzed using Nvivo coding and matrix queries. The results show that SMA provides an expanded CI beyond the previous limits of customers/markets and competitors, including insights on supply chains, costs and information-flow. Moreover, SMA-driven CI can provide visibility to supply chain uncertainties enabling improvements in demand planning and inventory management. SMA can provide CI about competitors’ strengths and weaknesses and customers’ dynamics; however, the bi-directional nature of CI could be determinantal if SM-linked customers are not educated/kept informed. Matrix query results illuminate the differences/similarities in respondents’ views. Academically, the study shows that SMA provides expanded CI to businesses beyond previously known scope of competitor analysis.

Assessing Customer Satisfaction: A Case in Logistics Service Company

This study aims to assess customer satisfaction of a logistics service company in Indonesia. Several researches employed the classical importance-performance analysis (IPA) to accomplish the objective; instead, this research used importance performance competitor analysis (IPCA). IPCA is considered as a remedy for IPA since the later has been criticized for several reasons, e.g., the relative structure of IPA and its lack of competitors’ consideration. A case study has been conducted to show the applicability of IPCA. Result shows that several service attributes have to be improved since they have lower performance than the competitor. Recommendations that can be done as a part to continuous improvement were provided. Finally, we also show that using IPA—without considering the competitor—would bring the management to a misleading interpretation. Keywords—Customer satisfaction; importance performance competitor analysis; logistics service

Can an Index Approach Improve Social Marketing Competitor Analysis?

Background: Competitive forces influence social marketing efforts. Indeed, social marketers often find themselves “shadow boxing” various forms of competition throughout their interventions. Despite the seminal role of competition as a threat to social marketing intervention efficacy, few empirical studies have undertaken competitive analysis or compared the usefulness of competitive typologies. Thus, this paper proposes an index approach to categorize competitive typologies relevant to a specific social marketing intervention in terms of their ease of use, intuitiveness and generalisability to the broader social cause domain. The proposed index approach is illustrated with empirical data, undertaking a competitive analysis of forces obstructing efforts to address educational inequality in Australia, then comparing the ease of use, intuitiveness and generalisability of 15 competitive typologies noted in the social marketing literature to produce a competitor analysis index. Research Question: Which competitive typologies most effectively frame forces that inhibit educational equality social marketing efforts in Australia? Methods: Via interviews and focus groups, qualitative data were collected from 46 students from low socioeconomic status (LSES) backgrounds at six universities and sought to understand the influence of their home residence’s geographical remoteness on their university participation. The analysis revealed eight participant-identified differential competitors experienced by students from regional, rural and remote settings (LSES-R, n = 25, 54.4%) that were not experienced by those from metropolitan areas (LSES-M, n = 21, 45.6%). Fifteen competitive typologies were identified in the social marketing literature, and their capacity to frame these eight differential forces in terms of their ease of use, intuitiveness and generalisability was critiqued. Findings: Unlike their metropolitan counterparts, LSES-R participants experienced situational (n = 3), dispositional (n = 3) and goal pursuit (n = 2) competitive forces. The most effective competition typologies comprised two classification options that were distinctly different and could classify both the unfriendly and friendly competition that exists in social marketing. Five competitor typologies were identified as easy to use, intuitive and generalizable to the broader educational inequality domain. Together, these five competitor typologies form a competitor analysis index for educational inequality researchers and practitioners to enhance their intervention efficacy. Recommendations: Despite widespread agreement as to the importance of competitor analysis in social marketing, the efficacy of various typologies has received little attention. Social marketers are encouraged to critique competitor typologies before selecting those which enable effective decision-making. Furthermore, it is recommended that social marketers use a competitor analysis index comprised of multiple typologies to better capture the nebulous nature of the many different types of competitors that exist in a specific social marketing context. Limitations: The educational inequalities cause and qualitative method may constrain generalisability, but they exemplify the importance of competition typology choice and model how competitor analysis indexes can be developed.

Explainable Artificial Intelligence-Based Competitive Factor Identification

Competitor analysis is an essential component of corporate strategy, providing both offensive and defensive strategic contexts to identify opportunities and threats. The rapid development of social media has recently led to several methodologies and frameworks facilitating competitor analysis through online reviews. Existing studies only focused on detecting comparative sentences in review comments or utilized low-performance models. However, this study proposes a novel approach to identifying the competitive factors using a recent explainable artificial intelligence approach at the comprehensive product feature level. We establish a model to classify the review comments for each corresponding product and evaluate the relevance of each keyword in such comments during the classification process. We then extract and prioritize the keywords and determine their competitiveness based on relevance. Our experiment results show that the proposed method can effectively extract the competitive factors both qualitatively and quantitatively.