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This study aims to examine the impact of consumer risk appetite, biases (specifically negative recency bias), and the importance of reviews in enhancing information quality. By analyzing these variables, the authors gain insights into their role in enriching the overall information spectrum available to consumers. The findings contribute to a better understanding of how risk appetite, biases and consumer reviews shape the quality of information.

The questionnaire assessed the relationship between dependent and independent variables by asking participants to rate their experiences in relevant scenarios. Variance-based structural equation modeling with the ADANCO program was used to examine the data. ADANCO software is used explicitly for variance-based structural equation modeling. To evaluate research models and test hypotheses, partial least square path modeling is used.

The efficiency of reviews and ratings is greatly influenced by consumer risk appetite. Businesses should focus on clients who are willing to take risks and balance positive and negative feedback. It is essential to comprehend how customers understand reviews. Credibility is increased by taking biases into account and encouraging unbiased criticism. Promoting thorough reviews strengthens influence. Monitoring and making use of these elements improve online reputation and commercial success.

The research has limitations due to the simplicity of the attributes taken into account and the requirement for a larger sample size. Overcoming barriers to promote consistent client feedback is essential, and tailored emails can help with assessment generation. Increased customer participation in writing evaluations can be achieved by removing obstacles and highlighting the advantages of participation.

Businesses and buyers rely on this “organically” generated content as the basis of their promotional strategy and buying decisions. Most of the research is related to consumer reviews, their behavior and the importance of social validation. However, some critical aspects related to this need further investigation.

The current and modern electrical distribution networks, named smart grids (SGs), use advanced technologies to accomplish all the technical and nontechnical challenges naturally demanded by energy applications. Energy metering collecting is one of these challenges ranging from the most basic (i.e., visual assessment) to the expensive advanced metering infrastructure (AMI) using intelligent meters networks. The AMIs’ data acquisition and system monitoring environment require enhancing some routine tasks. This paper aims to propose a methodology that uses a distributed and sustainable approach to manage wide-range metering networks, focused on using current public or private telecommunication infrastructure, optimizing the implementation and operation, increasing reliability and decreasing costs.

Inspired by blockchain technology, a collaborative metering system architecture is conceived, managing massive data sets collected from the grid. The use of cryptography handles data integrity and security issues.

A robust proof-of-concept simulation results are presented concerning the resilience and performance of the proposed distributed remote metering system.

The methodology proposed in this work is an innovative AMI solution related to SGs. Regardless of the implementation, operation and maintenance of AMIs, the proposed solution is unique, using legacy and new technologies together in a reliable way.

The purpose of this study is to define and develop a new technological development path for latecomer firms in developing countries.

An analytical framework for development based on the technological capability (TC) dimensions is developed and examined in the drilling sector. Since the process of TC accumulation is dynamic, the case study approach is the best method for an exploratory theory-building study. Through a comparative case study of two Iranian drilling contractors, a new path for the technological development of latecomer oil service companies is proposed.

The study of two cases indicates that despite having similar scope and levels of TC, one of them demonstrated superior technical performance. To address this difference, the concept of operational efficiency is introduced which is considered the outcome of increasing the depth of TC.

Although upgrading the level of technological and innovation capability is an important path for technological development, latecomers that suffer from various disadvantages can perform their routine activities with superior performance and develop through their basic operational/production capabilities. Also, specialized indicators designed for assessing the level and depth of TC in the drilling industry have important insights for evaluating the technological and competitive position of oil service companies.

To the best of the author’s knowledge, this study takes the first step in defining and elaborating on the concept of depth of TC as a development path for latecomers. It also introduced a novel approach to the global operational/production efficiency frontier as a target for their catch-up.

The purpose of this study is to examine the critical failure factors (CFFs) linked to various types of process improvement (PI) projects such as Kaizen, Lean, Six Sigma, Lean Six Sigma and Agile. Proposing a mitigation framework accordingly is also an aim of this study.

This research undertakes a systematic literature review of 49 papers that were relevant to the scope of the study and that were published in four prominent databases, including Google Scholar, Scopus, Web of Science and EBSCO.

Further analysis identifies 39 factors that contribute to the failure of PI projects. Among these factors, significant emphasis is placed on issues such as “resistance to cultural change,” “insufficient support from top management,” “inadequate training and education,” “poor communication” and “lack of resources,” as primary causes of PI project failures. To address and overcome the PI project failures, the authors propose a framework for failure mitigation based on change management models. The authors present future research directions that aim to enhance both the theoretical understanding and practical aspects of PI project failures.

Through this study, researchers and project managers can benefit from well-structured guidelines and invaluable insights that will help them identify and address potential failures, leading to successful implementation and sustainable improvements within organizations.

To the best of the author’s knowledge, this paper is the first study of its kind to examine the CFFs of five PI methodologies and introduces a novel approach derived from change management theory as a solution to minimize the risk associated with PI failure.

This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these capabilities on the organizational-level resources of dynamic capabilities and organizational creativity, ultimately influencing the overall performance of government organizations.

The calibration of artificial intelligence capabilities scale was conducted using a combination of qualitative and quantitative analysis tools. A set of 26 initial items was formed in the qualitative study. In the quantitative study, self-reported data obtained from 344 public managers was used for the purposes of refining and validating the scale. Hypothesis testing is carried out to examine the relationship between theoretical constructs for the purpose of nomological testing.

Results provide empirical evidence that the presence of artificial intelligence capabilities positively and significantly impacts dynamic capabilities, organizational creativity and performance. Dynamic capabilities also found to partially mediate artificial intelligence capabilities relationship with organizational creativity and performance, and organizational creativity partially mediates dynamic capabilities – organizational creativity link.

The application of artificial intelligence holds promise for improving decision-making and problem-solving processes, thereby increasing the perceived value of public service. This can be achieved through the implementation of regulatory frameworks that serve as a blueprint for enhancing value and performance.

There are a limited number of studies on artificial intelligence capabilities conducted in the government sector, and these studies often present conflicting and inconclusive findings. Moreover, these studies indicate literature has not adequately explored the significance of organizational-level complementarity resources in facilitating the development of unique capabilities within government organizations. This paper presents a framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory.

Agile principles have been widely used in software development team practice since the creation of the Agile Manifesto. Studies have examined variables related to agile principles without systematically considering the relationships among key team, agile methodology, and process variables underlying the agile principles and how these variables jointly influence the achievement of software development agility. In this study, the authors tested a team/methodology–process–agility model that links team variables (team autonomy and team competence) and methodological variable (iterative development) to process variables (communication and collaborative decision-making), which are in turn linked to software development agility (ability to sense, respond and learn).

Survey data from one hundred and sixty software development professionals were analyzed using structural equation modeling methods.

The results support the team/methodology–process–agility model. Process variables (communication and collaborative decision-making) mediated the effects of team (autonomy and competence) and methodological (iterative development) variables on software development agility. In addition, team, methodology and process variables had different effects on the three dimensions of software development agility.

The results contribute to the literature on organizational IT management by establishing a team/methodology–process–agility model that can serve as a basis for developing a core theoretical foundation underlying agile principles and practices. The results also have practical implications for organizations in understanding and managing holistically the different roles that agile methodological, team and process factors play in achieving software development agility.

This paper aims to propose a novel model that addresses the limitations of current practices, through considering quantitative and qualitative criteria in the decision-making process for equipment replacement.

Literature review and consultation with professionals in the heavy construction industry was conducted to identify the criteria influencing the replacement of construction machines. A questionnaire survey using analytic hierarchy process and multi-attribute utility theory was used to rank these criteria and establish their utility scores. Sensitivity analysis was performed to assess how adjustments in the weights of main criteria would impact equipment replacement decisions.

The identified criteria were classified into three categories: economic, technical and socioenvironmental, encompassing a total of 15 criteria. The findings indicated that salvage value/meeting payback period/maximizing profitability held the highest importance in the replacement process, followed by considerations like high repair and maintenance cost; working condition and economic conditions. Safety and social benefits scored the least among all criteria and categories.

This study focuses on earth-moving equipment and involves experts from the Eastern Province of Saudi Arabia. The model introduces a novel methodology to aid decision-makers, particularly contractors and project managers, in determining when to replace heavy construction equipment, which results in resource efficiency and time saving.

The model integrates expertise and knowledge from experts to establish criteria for replacing construction equipment. This research aims to improve the functionality of the decision-making process regarding the acquisition or replacement of equipment throughout its lifespan.

Drawing upon psychological ownership theory, the aim of this study was to uncover the coexisting mediating effects of knowledge sharing and hiding on the relationship between employees’ organizational psychological ownership (OPO) and their innovative work behavior (IWB). The moderating role of organizational context in these mediating relationships was further examined to determine the moderated mediation paths.

This study mainly used a survey-based research method and collected data from 512 professionals from both public and private organizations in Pakistan to test our proposed hypotheses.

The results showed that coexisting knowledge sharing and hiding mediated the relationship between employees’ OPO and IWB. Furthermore, organizational context moderated the mediated relationships, providing support for the moderated mediation framework.

The results highlight the significance of fostering employees’ OPO to enhance their IWB by promoting knowledge sharing and preventing knowledge hiding. This study also urges managers to consider the contingency effect of organizational contexts when promoting employees’ IWB in emerging economies.

The results obtained in this study suggest that the knowledge behavior paradox occurs in organizations, and distinct organizational contexts play crucial but differential roles in intervening in the effect of employees’ OPO on their IWB. This study empirically validated this complex mechanism in an important emerging economy in Asia.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

Managerial practices are essential in the success of information technology (IT) projects of digital transformation (DT). However, the literature has not yet specified all these managerial practices. This study aims to address this gap by investigating the influence of change management on the success of IT projects of DT. Additionally, the author examines the consequences on SMEs in the economic context of the Democratic Republic of the Congo (DRC).

This study draws on a research model that elucidates aspects of the resource-based view (RBV) framework, notably the transformation of human resources based on change management. This model demonstrates the relationship between change management, DT and IT project success, which facilitates the performance and resilience of SMEs. To empirically validate and test the developed research model, we gathered 299 responses from SME managers in the DRC through cross-sectional data collection using a structured questionnaire. The author performed statistical analyses using variance-based structural equation modeling (PLS-SEM) with the help of SmartPLS 3.0.

This paper reveals how SME managers can succeed in DT projects with the change management of human resources. Furthermore, it establishes that the success of IT projects of DT is an essential for enhancing the performance and resilience of SMEs in the DRC.

This study contributes to the information systems (IS) literature on developing countries by highlighting the DRC context. Little research deals with the success factors of DT projects and their organizational impact on SMEs in developing countries. This study thus enriches the IS literature by filling this void.