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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.

Although digital transformation (DT) has emerged as an important phenomenon for both research and practices, the influences remain inconclusive and inadequate. The emerging artificial intelligence (AI) technologies further complicate the understanding and practices of DT while understudied yet. To address these concerns, this study takes a process perspective to empirically investigate when and how digital-intelligence transformation can improve firm performance, aiming to enrich the literature on digital-intelligence transformation and strategic information systems (IS) field.

Drawing on the dynamic capability view and business agility, we took a process perspective to conceptualize and empirically examine the influence of digital-intelligence transformation and the process characteristics. Taking a continuous panel dataset of listed Chinese firms covering 2007 to 2020, we investigated digital-intelligence transformation’s effect on firm performance and the moderating roles of three strategic aspects: pace, scope and rhythm.

This study found that digital-intelligence transformation positively affects firm performance and is moderated by the characteristics of transformation processes (i.e. pace, scope and rhythm). Specifically, the high-paced and rhythmic transformation processes facilitate the positive relationship, while the large scope undermines the benefits of transformation. These relationships hold across various endogeneity and heterogeneity analyses.

Our findings provide valuable implications for digital-intelligence transformation and strategic IS field. First, this study enriches existing literature on digital-intelligence transformation by empirically investigating the influence from a process perspective. Moreover, this study provides insights into a comprehensive understanding of the complexity of digital-intelligence transformation and the influences of AI. Finally, this study provides practical implications on how to make digital-intelligence transformation to benefit firm performance.

This paper systematically reviews and scrutinises the current development of studies concerning digital platform use in firms’ internationalisation. It also provides a research agenda for future work. Despite more than two decades of research, the study of how traditional firms enter foreign markets using digital platforms remains in its infancy. This paper contributes insights into the academic and managerial relevance of this field.

A systematic review of the literature based on content analysis was undertaken, drawing from peer-reviewed journal articles in international business, international marketing, information systems, and electronic commerce. The articles were published between 1999 and 2023, and the review employed a vote-counting method.

This analysis of 61 papers indicates that research on digital platform use in firms’ internationalisation is growing rapidly. However, the field itself is fragmented, and the research findings are inconsistent. This dynamic area reflects a growing trend, is dominated by several theories, relies primarily on survey data, and frequently uses China as a research context.

In acknowledging the vibrancy and managerial importance of this field, the authors offer a comprehensive overview of the existing studies to serve as a repository of knowledge on digital platform use in internationalisation for both academics and practitioners. Based on the aforementioned analysis, this study develops a reconciling framework to address current research gaps and identify future study directions.

While service failures and recoveries (SFR) constitute a well-explored research domain, such negative consumption experiences have been understudied in the luxury context. The current study undertakes a systematic review to capture current knowledge and stimulate research in this vital domain.

This study adopts a systematic literature review methodology and employs the Theory-Context-Characteristics-Methods (TCCM) framework.

Guiding theories, industry and national contexts, relevant constructs and their relationships, and research methods employed in studying SFR in luxury consumption are identified and synthesized.

A comparative analysis of the luxury SFR with broader extant knowledge is offered. Further, hospitality is revealed as the dominant context, meriting its emphasis.

This paper aims to explore the concept of “actor engagement” within the context of the sharing economy, a novel and dynamic business model. Specifically, it investigates the formation of actor engagement and its relationship with value creation within this business model.

Drawing on Storbacka et al. (2016)actor engagement framework and service-dominant logic service ecosystem model, unstructured data (text and images) from the Airbnb platform in seven countries and text- and image-mining techniques such as machine learning are used to measure the research variables and test the model by PLS-SEM.

The results indicate that affective engagement has a more significant impact on behavioural engagement than cognitive engagement for multidimensional actor engagement. Service providers’ engagement – directly, and through customer engagement – influences value creation for service providers (i.e. performance). Moreover, national-level moderator (i.e. economic, competitiveness, technology, social and political factors) plays a significant moderating role in our model.

This study encourages future research to explore how actor engagement leads to value creation for all actors on the different sharing economy platforms.

The findings provide practical insights for service providers to engage their customers and platform managers, especially in an international context, on managing their relationships with both customers and service providers in different countries.

This study advances the current literature on actor engagement and its role in value creation by providing a better understanding of the role of the national context in this process through unstructured data analysis.

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.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

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.