Search results

The Web 3.0 has been hugely enabled by smartphones and new generation mobile applications. With the growing adoption of smartphones, the use of mobile applications has grown exponentially and so has the development of mobile applications. This study is an attempt to understand the issues and challenges faced in the mobile applications domain using discussions made on Twitter based on mining of user generated content.

The study uses 89,908 unique tweets to understand the nature of the discussions. These tweets are analyzed using descriptive, content and network analysis. Further using transaction cost economics, the findings are reviewed to develop practice insights about the ecosystem.

Findings indicate that the discussions are mostly skewed toward a positive polarity and positive user experiences. The tweeters are predominantly application developers who are interacting more with marketers and less with individual users.

Most of these applications are for individual use (B2C) and not for enterprise usage. There are very few individual users who contribute to these discussions. The predominant users are application reviewers or bloggers of review websites who use the recently developed applications and discuss their thoughts on the same.

The results may be useful in varied domains which are planning to expand their reach to a larger audience using mobile applications and for marketers who primarily focus on promotional content.

The domain of mobile applications on social media is still restricted to promotions and digital marketing and may solely be used for the purpose of link building by application developers. As such, the discussions could provide inputs towards mobile phone manufacturers and ecosystem providers on what are the real issues these communities are facing while developing these applications.

The study uses mixed research methodology for mining experiences in the domain of mobile application developers using social media analytics and transaction cost economics. The discussion on the findings provides inputs for policy-making and possible intervention areas.

This paper aims to examine the relevance of Big Data to forensic accounting practice and education by gathering opinions from a sample of academics and practitioners in China.

The authors conduct a survey of academics and practitioners regarding the desired demand, importance and content of Big Data educational skills and topics for forensic accounting education to effectively respond to challenges and opportunities in the age of Big Data.

Results indicate that the demand for and interest in Big Data/data analytics and forensic accounting will continue to increase; Big Data/data analytics and forensic accounting should be integrated into the business curriculum; many of the suggested Big Data topics should be integrated into forensic accounting education; and some attributes and techniques of Big Data are important in improving forensic accounting education and practice.

Readers should interpret the results with caution because of the sample size (95 academics and 103 practitioners) and responses obtained from academics and practitioners in one country (China) that may not be representative of the global population.

The results are useful in integrating Big Data topics into the forensic accounting curriculum and in redesigning the forensic accounting courses/programs.

The results have implications for forensic accountants in effectively fulfilling their responsibilities to their profession and society by combating fraud.

This study provides educational, research and practical implications as Big Data and forensic accounting are advancing.

This chapter examines the ways social media, analytics, and disruptive technologies are combined and leveraged by political campaigns to increase the probability of victory through micro-targeting, voter engagement, and public relations. More specifically, the importance of community detection, social influence, natural language processing and text analytics, machine learning, and predictive analytics is assessed and reviewed in relation to political campaigns. In this context, data processing is examined through the lens of the General Data Protection Regulation (GDPR) effective as of May 25, 2018. It is concluded that while data processing during political campaigns does not violate the GDPR, electoral campaigns engage in surveillance, thereby violating Articles 12 and 19, in respect to private life, and freedom of expression accordingly, as stated in the 1948 Universal Declaration of Human Rights.

The purpose of the study was to investigate factors promoting innovation and application of internet of things in academic and research information organizations.

Quantitative research design involved survey of selected academic and research information organizations in public and private chartered institutions. Information professionals, digital content managers, information systems and technologists that normally consume big data and technological resources were involved in the process of data collection using structured questionnaire and content analysis. Information organizations and information practitioners were selected from public and private academic and research institutions.

Innovation of internet of things has increasingly transformed and changed academic and research information organizations as the source of knowledge in addition to expanding access to education, data, information and communication anywhere anytime through hyperconnectivity and networking. Internet of things technologies such as mobile of things, web of things, digital information systems and personal devices are widely applied by digital natives in academic and research information organizations. Mobilization platform and devices is the single biggest provider of data, information and knowledge in academic and research organizations. Modern trends in education and knowledge practices in academic institutions and information organizations depends upon internet of things, digital repositories, electronic books and journals, social media interfaces, multimedia applications, information portal hubs and interactive websites, although challenges regarding inadequate information communication technology infrastructure and social computing facilities still persist.

Information organizations in public and private chartered academic and research institutions were adopted in the study. Respondents handling and supporting information management, planning and decision-making provided the necessary data. Information professionals, digital content managers, information systems and technologists are proactively involved in data and information analytics.

Academic and research information organizations are powerhouses that provide knowledge to support research, teaching and learning for sustainable development and the betterment of humanity and society. Innovation of internet of things and associated technologies provides practical aspects of attaining sustainable information development practices in the contemporary knowledge society. Internet of things technologies, principles of economies of scale and investment and customer needs entail that information organizations and practitioners should provide appropriate and smart systems and solutions.

Modern academic and research information organizations have the social corporate responsibility to offer technological innovations to heighten access to knowledge and learning in academic and research institutions. Economically, innovation and application of internet of things provide unlimited access to big data and information in organizations all the time anywhere anytime.

Data management is a growing phenomenon that information practitioners need to fully understand in the digital economies. Information professionals need to embrace and appreciate innovation and application of internet of things technologies whose role in sustainable development practices is critical in academic and research organizations.

Four years into a five-year contract with General Motors to be the exclusive website vendor to its U.S. network of more than 4,000 dealers, CDK Digital faced a crucial contract renewal at the end of 2012. The case follows Melissa McCann, director of strategic marketing, and Chris Reed, CMO, as they prepared for a critical meeting in July 2011: a presentation to the customer relationship management (CRM) subcommittee of the Chevrolet dealer council. Although GM dealers, like all auto dealers in the United States, were independent franchisees, GM saw the renewal of CDK Digital's exclusive contract as a collaborative decision between dealers and GM. According to Ed Vogt, GM's executive in charge of the renewal, if the dealer councils said no, the contract would not be renewed.

This case challenges students to use CDK's big data and analytics capabilities to address the inherent conflict between dealers and manufacturers: when marketing to potential customers, manufacturers wanted consistency across dealer websites to maximize sales of their targeted brands, while dealers wanted flexibility to sell what they had in inventory.

After analyzing the case, students will be able to:

• Demonstrate how big data and analytics can be used to solve channel conflict

• Explain how franchisors and franchisees have different perspectives on the value of data on retail operations

• Recognize benefits of big data and analytics beyond the obvious potential improvements to marketing and operational effectiveness

• Articulate the value of data analytics for channel management

• Appraise the benefits of real-time website customization

Demonstrate how big data and analytics can be used to solve channel conflict

Explain how franchisors and franchisees have different perspectives on the value of data on retail operations

Recognize benefits of big data and analytics beyond the obvious potential improvements to marketing and operational effectiveness

Articulate the value of data analytics for channel management

Appraise the benefits of real-time website customization

The pandemic COVID-19 brought in large challenges globally among the workforce. There were reports of how employee layoffs and pay-cuts were gradually becoming prominent across industries based on media reports. However, there were no attempts to develop a typology of challenges faced by the workforce.

This study mined user-generated content from Twitter to bring out a typology of challenges due to the sudden turbulence that is faced from the pandemic. A case study has also been conducted by taking in-depth interviews in the academic sector to deep dive into the nature of these problems.

The study findings indicate that these challenges are basically stemming from challenges surrounding infrastructure readiness, digital readiness, changing nature of deliverables, workforce demand versus supply problems and challenges surrounding job losses.

There is a need to explore the linkages through inferential research infrastructure readiness, digital readiness, changing nature of deliverables, workforce demand versus supply problems and challenges surrounding job losses on employee welfare during pandemics.

The authors provide inductive insights based on a data-driven research methodology surrounding the sudden challenges faced and possible mechanisms to address these issues faced by a stressed workforce catering to multiple stakeholders.

There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition and to assess competitive environment of the business. The purpose of this paper is to help companies with social media competitive analysis and transformation of social media data into knowledge creation for decision-makers, specifically for app-based food delivery companies.

Three online app-based food delivery companies, i.e. Swiggy, Zomato and UberEats, were considered in this study. Twitter was used as the data collection platform where customer’s tweets related to all three companies are fetched using R-Studio and Lexicon-based sentiment analysis method is applied on the tweets fetched for the companies. A descriptive analytical method is used to compute the score of different sentiments. A negative and positive sentiment word list is created to match the word present on the tweets and based on the matching positive, negative and neutral sentiments score are decided. The sentiment analysis is a best method to analyze consumer’s text sentiment. Lexicon-based sentiment classification is always preferable than machine learning or other model because it gives flexibility to make your own sentiment dictionary to classify emotions. To perform tweets sentiment analysis, lexicon-based classification method and text mining were performed on R-Studio platform.

Results suggest that Zomato (26% positive sentiments) has received more positive sentiments as compared to the other two companies (25% positive sentiments for Swiggy and 24% positive sentiments for UberEats). Negative sentiments for the Zomato was also low (12% negative sentiments) compared to Swiggy and UberEats (13% negative sentiments for both). Further, based on negative sentiments concerning all the three food delivery companies, tweets were analyzed and recommendations for business provided.

The results of this study reveal the value of social media competitive analysis and show the power of text mining and sentiment analysis in extracting business value and competitive advantage. Suggestions, business and research implications are also provided to help companies in developing a social media competitive analysis strategy.

Twitter analysis of food-based companies has been performed.

Big data analytics (BDA) increasingly provide value to firms for robust decision making and solving business problems. The purpose of this paper is to explore information quality dynamics in big data environment linking business value, user satisfaction and firm performance.

Drawing on the appraisal-emotional response-coping framework, the authors propose a theory on information quality dynamics that helps in achieving business value, user satisfaction and firm performance with big data strategy and implementation. Information quality from BDA is conceptualized as the antecedent to the emotional response (e.g. value and satisfaction) and coping (performance). Proposed information quality dynamics are tested using data collected from 302 business analysts across various organizations in France and the USA.

The findings suggest that information quality in BDA reflects four significant dimensions: completeness, currency, format and accuracy. The overall information quality has significant, positive impact on firm performance which is mediated by business value (e.g. transactional, strategic and transformational) and user satisfaction.

On the one hand, this paper shows how to operationalize information quality, business value, satisfaction and firm performance in BDA using PLS-SEM. On the other hand, it proposes an REBUS-PLS algorithm to automatically detect three groups of users sharing the same behaviors when determining the information quality perceptions of BDA.

The study offers a set of determinants for information quality and business value in BDA projects, in order to support managers in their decision to enhance user satisfaction and firm performance.

The paper extends big data literature by offering an appraisal-emotional response-coping framework that is well fitted for information quality modeling on firm performance. The methodological novelty lies in embracing REBUS-PLS to handle unobserved heterogeneity in the sample.

Due to the high penetration of social media and mobile devices in the recent decade, especially with the coronavirus, digital media tools have become a priority for marketing managers. Digital content marketing (DCM) is one of the crucial ingredients of the digital marketing strategy of businesses, which proposes value to the audience through brand-related and relevant content. The tourism industry is also trapped in the digital wave and has witnessed fundamental changes in how customers communicate. The growth of investment in DCM in this industry to introduce tourist attractions and acquire tourists calls for more research to explore multiple aspects of these initiatives' implementation. Despite the importance of DCM, there is no clear understanding of its implementation's various components. Therefore, the primary goal of the current study is to design a new comprehensive framework of DCM implementation that integrates its antecedents, process, and consequences in the tourism industry.

The mixed method was applied to achieve the research goal. The initial criteria and main components of the framework were identified with a comprehensive literature review to develop the framework. To enrich the initial criteria, some semi-structured interviews with experts were conducted; then, the extracted criteria and sub-criteria were prioritized and weighted using the quantitative best-worst method (BWM).

The results indicate that the proposed integrated framework contains three categories of antecedents, processes, and consequences and 12 main concepts. The weights and ranks of the extracted concepts and their sub-criteria are calculated using BWM.

The proposed framework helps managers have a big picture of the DCM strategy to successfully implement and consider the multiple dimensions of such initiatives. The proposed framework provides actionable insight for digital marketing decision-makers to manage such projects effectively and plan appropriate actions for progress.

A review of content marketing reveals that there are few studies conducted that integrate the components of the DCM implementation process, including antecedents, process, and consequences. This research is one of the first in the field of DCM implementation in the tourism industry to fill this theoretical gap. The main contribution of this research is to design a new integrated framework for DCM implementation that offers a holistic view of antecedents, process, and consequences.

LibraryThing is a Web 2.0 tool allowing users to catalogue books using data drawn from sources such as Amazon and the Library of Congress and has facilities such as tagging and interest groups. This study seeks to evaluate whether LibraryThing is a valuable tool for libraries to use for promotional and user engagement purposes.

This study used a sequential mixed methods three-phase design: the identification of LibraryThing features for user engagement or promotional purposes, exploratory semi-structured interviews and a questionnaire.

Several uses of LibraryThing for promotional and user engagement purposes were identified. The most popular reason libraries used LibraryThing was to promote the library or library stock, with most respondents using it specifically to highlight collections of books. Monitoring of patron usage was low and many respondents had not received any feedback. LibraryThing was commonly reported as being easy to use, remotely accessible, and having low cost, whilst its main drawbacks were the 200 book limit for free accounts, and it being a third-party site. The majority of respondents felt LibraryThing was a useful tool for libraries.

LibraryThing has most value as a promotional tool for libraries. Libraries should actively monitor patron usage of their LibraryThing account or request user feedback to ensure that LibraryThing provides a truly valuable service for their library.

There is little research on the value of LibraryThing for libraries, or librarians' perceptions of LibraryThing as a Web 2.0 tool.