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With the increasing agility of IT enterprises, it is crucial to identify suitable managerial strategies for controlling information system development (ISD) projects in the new agile working environments. These environments are characterized by the collaborative nature of work and the recurring nature of communication. This study aims to explore how perceived transparency in ISD processes, controlled by transparency strategies, impacts project quality.

In collaboration with a firm that implemented a customized Scaled Agile Framework, questionnaires were distributed to employees involved in ISD projects. The goal was to understand the influence of perceived transparency in ISD processes on project quality.

Our research demonstrates that perceived transparency in ISD processes enhances project quality through knowledge exchange by strengthening goodwill trust among team members. Additionally, transparency improves project quality through client feedback by strengthening competence trust of clients toward the team. Goodwill trust of clients toward the team and competence trust among team members have less impact on project quality enhancement.

This study reveals the nomological network among the perceived transparency, different types of trust among stakeholders, social interactions among stakeholders, and project outcomes in agile ISD environments. This nomological network has been overlooked by previous studies that biased toward top-down, interorganizational communication. It highlights that not all types of trust among stakeholders are involved in the processes through which perceived transparency influences ISD project quality in agile working environments. Additionally, it exposes the limitations of transparency strategies for controlling projects in agile IT enterprises.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

This study investigated the visibility of carbon emissions allowances accounting in the financial reports of 32 clean development mechanism (CDM) projects in the UAE to uncover the obstacles to setting consistent standards for carbon emission accounting. As carbon emissions are monetized as credits, consistent accounting standards can aid decision-makers in the development of carbon emission mitigation strategies.

This study used a grounded theoretical framework for exploring the terms used in the policy documents of international accounting bodies regarding accounting standards and guidelines for carbon emission credits. Raw qualitative data were gathered, and an inductive approach was used by analyzing documents from various sources using the qualitative data text analysis software QDA Miner 6.

The findings showed that the financial statement reports of the corporations did not include disclosure of the carbon credit account. This omission was due to the lack of global standardization of carbon credit accounts and emission allowance recognition. This may hinder the production of a comprehensive report containing accurate and valuable financial information relevant to all stakeholders.

The study is among the first to use a grounded theoretical framework to investigate whether corporations are applying common standards and guidelines for carbon emissions accounting.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

Conceptual and empirical research over the past 20 years has moved the social media (SM) literature beyond the embryotic stage to a well-developed academic discipline. As the lead article in the special issue in the Journal of Research in Interactive Marketing on Cutting-Edge Research in Social Media and Interactive Marketing, this review and agenda article has two key goals: (1) to review key SM and interactive marketing research over the past three years and (2) to identify the next wave of high priority challenges and research opportunities.

Given the “cutting-edge” research focus of the special issue, this review and research agenda paper focused on articles published in 25 key marketing journals between January 2021 and March 2024. Initially, the search request was for articles with “social media, social selling, social commerce” located in the article title, author-selected key words and journal-selected keywords. Later, we conducted searches based on terminology from articles presented in the final review. In total, over 1,000 articles were reviewed across the 25 journals, plus additional ones that were cited in those journals that were not on the initial list.

Our review uncovered eight key content areas: (1) data sources, methodology and scale development; (2) emergent SM technologies; (3) artificial intelligence; (4) virtual reality; (5) sales and sales management; (6) consumer welfare; (7) influencer marketing; and (8) social commerce. Table I provides a summer of key articles and research findings for each of the content areas.

As a literature review and research agenda article, this paper is one of the most extensive to date on SM marketing, and particularly with regard to emergent research over the past three years. Recommendations for future research are integrated through the paper and summarized in Figure 2.

Consumer welfare is one of the eight emergent content areas uncovered in the literature review. Specific focus is on SM privacy, misinformation, mental health and misbehavior.

The study aims to provide insights in the sensemaking process and the use of business analytics (BA) for project selection and prioritisation in start-up settings. A major focus is on the various ways start-ups can understand their data through the analytical process of sensemaking.

This is a comparative case study of two start-ups that use BA in their projects. The authors follow an interpretive approach and draw from the constructivist grounded theory method (GTM) for the purpose of data analysis, whereby the theory of sensemaking functions as the sensitising device that supports the interpretation of the data.

The key findings lie within the scope of project selection and prioritisation, where the sensemaking process is implicitly influenced by each start-up's strategy and business model. BA helps start-ups notice changes within their internal and external environment and focus their attention on the more critical questions along the lines of their processes, operations and business model. However, BA alone cannot support decision-making around less structured problems such as project selection and prioritisation, where intuitive judgement and personal opinion are still heavily used.

This study extends the research on BA applied in organisations as tools for business development. Specifically, the authors draw on the literature of BA tools in support of project management from multiple perspectives. The perspectives include but are not limited to project assessment and prioritisation. The authors view the decision-making process and the path from insight to value, as a sensemaking process, where data become part of the sensemaking roadmap and BA helps start-ups navigate the decision-making process.

The purpose of this study was to evaluate the influence of knowledge management (KM) drivers on perceived KM results in a public higher education institution. A structured theoretical model based on leadership, people, processes, knowledge processes, technology, learning and KM results was developed and tested.

A survey was conducted with the employees of a public higher education institution where an administrative reform was initiated. A valid sample of 257 respondents was obtained. The data were obtained from the application of a structured questionnaire based on the KM drivers and their results. A five-point Likert-type scale was used to measure respondents' answers. The main data analysis technique was structural equation modeling.

The results indicate knowledge processes, leadership and people factors have a positive and significant impact on KM results. On the other hand, organizational processes, technology and learning factors were not significant. However, the service length of servants in the institution influences the perception of knowledge drivers.

Public institutions should be attentive to people with more time of service because they may have difficulties with technological advances, reorganization of processes and adaptation to new ways of sharing knowledge.

This study advances on the analysis of KM results in the public sector and tests the moderation effect of time of service.

The study explores the extent to which business analytics can address business problems using the task-technology fit theory.

The qualitative research approach of pattern matching was adopted for data analysis and 12 semi-structured interviews were conducted. Four propositions derived from the literature on task-technology fit are compared to emerging core themes from the empirical data.

The study establishes the relationships between various forms of fit, arguing that the iterative application of business analytics improves problem understanding and solutions, and contends that both under-fit and over-fit can be acceptable due to the increasing costs of achieving ideal fit and potential unaffected outcomes, respectively. The study demonstrates that managers should appreciate that there may be a distinction between those who create business analytics solutions and those who apply business analytics solutions to solve problems.

Extant studies on business analytics have not focused on how the match between business analytics and tasks affects the level to which problems can be addressed that determines business value. This study enriches the literature on business analytics by linking business analytics and business value through problem resolution demonstrated by task-technology fit. To the authors’ knowledge, this study might be the first to apply pattern matching to study the fit between technology and tasks.

Prior research suggests a chief executive officer’s (CEO) background is highly predictive of the strategic predisposition. This paper aims to focus on the need for accuracy in the categorization of CEO background and the impact that modest, nuanced changes in coding definitions yield.

This study evaluates the use of biographic and demographic information of CEOs to provide a more nuanced and expansive approach to understanding the influence of legal education and experience on business strategy. Propositions as to more nuanced coding definitions are developed. Building upon Fligstein (1987), a proof-of-concept example is developed using CEO information available for 2010. That data is then reexamined using an altered method (Modified Fligstein) to discern changes in the number of CEOs contained within the background categories.

The two categorizations performed reveal that substantial differences in the number of CEOs coded into a category can come from relatively small changes in categorical definitions. In comparing the first categorization to the second, each of the vocational categories experienced a change, ranging from a decrease of 11.1% to an increase of 142.9%.

This study informs both theory and practice by increasing the efficacy of the use of biographic and demographic information to assess the strategic orientation of executives. It postulates and demonstrates that simple changes in the categorical definition produce significant changes and can skew empirical results that reduce the utility of prior studies.