Strategic Insights Research Articles

Understanding impulse buying in interest-based e-commerce: the role of content creativity

PurposeThe popularisation of user-generated content has boosted the integration of social media with online retail, especially in the emerging context of interest-based e-commerce (IBEC). As the content brings new incentives for consumers’ impulsivity in consumption, it remains unclear understanding of the mechanism of impulse buying in IBEC from the current literature. This study aims to examine how content creativity encourages impulse buying intention though attitude, and whether the effect is moderated by consumers’ online shopping experience.Design/methodology/approachThis study administered a two-wave online survey to a purposive sample of 286 consumers with shopping experience in Douyin. The hypotheses and a moderated-mediation effect were tested using structural equation modelling in Mplus8.3.FindingsThe empirical results revealed that content creativity, including dual dimensions of relevance and divergence, can drive positive attitude towards platforms through the direct effect of subjective norm and mediation effect through perceived usefulness, which induces an urge to buy impulsively. The mediation effect is further moderated by consumers’ online shopping experience in IBEC.Originality/valueThis study expands the stream of impulse buying research in retailing by proposing a context of IBEC and bringing empirical evidence of the relationship between content and consumers’ urge to buy. The study provides strategic insights for retailers to achieve marketing value by managing consumers’ impulse purchases in IBEC.

The Role of AI in Data Engineering and Integration in Cloud Computing

This article presents a comprehensive analysis of the transformative role of Artificial Intelligence (AI) in revolutionizing data engineering and integration processes within cloud computing environments. The article examines the implementation of AI-driven solutions across multiple dimensions, including automated pipeline generation, intelligent schema matching, anomaly detection, and real-time data integration. Through a mixed-methods approach incorporating both quantitative and qualitative analyses, the article demonstrates significant improvements in data processing efficiency, with organizations achieving up to 67% reduction in processing time and 89% enhancement in accuracy. The article encompasses case studies from financial services, healthcare, and e-commerce sectors, providing concrete evidence of practical applications and scalability. Key findings reveal that AI-powered systems substantially outperform traditional approaches in cost efficiency, scalability, and data quality management, while simultaneously reducing operational overhead. The article also addresses implementation challenges, including legacy system integration and initial deployment complexities, offering strategic insights for organizations pursuing AI integration in their data engineering workflows. These article contribute to the broader understanding of how AI technologies can be effectively leveraged to address the growing challenges of data management in cloud computing environments, while providing a framework for future developments in this rapidly evolving field.

Implementing Enterprise-Scale iOS MDM : A Technical Deep Dive

As organizations increasingly adopt mobile technologies, the need for effective remote management of iOS devices has grown significantly. This technical article examines implementation strategies and real-world performance metrics from a study of Mobile Device Management (MDM) implementations across 12 diverse organizations, encompassing 2,500 enterprise devices during 2023-2024. The article reveals that optimized MDM solutions can achieve 99.99% uptime while reducing infrastructure costs by up to 45%. Key findings demonstrate that effective MDM implementation reduces device management costs by 64%, improves security compliance rates to 99.3%, and decreases incident response time by 73%. Through automated troubleshooting and optimization, organizations experienced a 67% reduction in security incidents and a 42% decrease in IT support overhead while achieving 85% reduction in deployment time and 82% acceleration in app deployment processes. The article comprehensively evaluates architecture design, deployment metrics, security implementation, performance analysis, and scalability considerations. Implementation of advanced security protocols resulted in a 98.7% prevention rate for common mobile security threats, while maintaining 99.99% system reliability. The article also highlights the importance of phased deployment strategies, which demonstrate 43% higher success rates compared to direct full-scale implementations, and provides strategic insights for future planning and infrastructure optimization.

Measuring Research Capacity: Development of the PACER Tool.

Evaluating research activity in research departments and education programs is conventionally accomplished through measurement of research funding or bibliometrics. This limited perspective of research activity restricts a more comprehensive evaluation of a program's actual research capacity, ultimately hindering efforts to enhance and expand it. The objective of this study was to conduct a scoping review of the existing literature pertaining to the measurement of research productivity in research institutions. Using these findings, the study aimed to create a standardized research measurement tool, the Productivity And Capacity Evaluation in Research (PACER) Tool. The evidence review identified 726 relevant articles in a literature search of PubMed, Web of Science, Embase, ERIC, CINAHL, and Google Scholar with the keywords "research capacity" and "research productivity." Thirty-nine English-language studies applicable to research measurement were assessed in full and 20 were included in the data extraction. Capacity/productivity metrics were identified, and the relevance of each metric was data-charted according to 3 criteria: the metric was objective, organizational in scale, and applicable to varied research domains. This produced 42 research capacity/productivity metrics that fell into 7 relevant categories: bibliometrics, impact, ongoing research, collaboration activities, funding, personnel, and education/academics. With the expertise of a Delphi panel of researchers, research leaders, and organizational leadership, 31 of these 42 metrics were included in the final PACER Tool. This multifaceted tool enables research departments to benchmark research capacity and research productivity against other programs, monitor capacity development over time, and provide valuable strategic insights for decisions such as resource allocation.

Strategic insights into the cultivation of pancreatic cancer organoids from endoscopic ultrasonography-guided biopsy tissue

BACKGROUND The frequent suboptimal efficacy of endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) to culture pancreatic cancer (PC) organoids (PCOs) poses a major challenge in the advancement of personalized medicine for advanced PC. AIM To explore how to obtain appropriate puncture tissues from EUS-FNB and optimize the strategy for efficiently constructing PCOs, providing an efficient tool for the advancement of personalized medicine. METHODS Patients who underwent EUS-FNB for the diagnosis of PC tissue were prospectively enrolled. We refined the endoscopic biopsy procedures and organoid cultivation techniques. All tissue specimens verified by on-site pathological assessment were cultured in a semi-suspended medium in a microfluidic environment. We assessed differences in PCOs cultured beyond and below five generations examining patient demographics, specimen and organoid attributes, and the sensitivity of organoids to a panel of clinical drugs through cell viability assays. RESULTS In this study, 16 patients with PC were recruited, one sample was excluded because onsite cytopathology showed no tumor cells. Successful organoid generation occurred in 93.3% (14 of 15) of the EUS-FNB specimens, with 60% (9 of 15) sustaining over five generations. Among these patients, those with a history of diabetes, familial cancer, or larger tumors exhibited enhanced PCO expandability. The key factors influencing long-term PCOs expansion included initial needle sample quality (P = 0.005), rapid initiation of organoid culture post-isolation (P ≤ 0.001), and high organoid activity (P = 0.031). Drug sensitivity analysis revealed a partial response in two patients following therapeutic intervention and surgery and stable disease in four patients, indicating a moderate correlation between organoid response and clinical outcomes. CONCLUSION Optimal initial needle sampling, rapid and precise biopsy sample processing, process isolated samples as soon as possible, and sufficient cellular material are crucial for successful cultivating PCOs. High organoid activity is an important factor in maintaining their long-term expansion, which is essential for shortening the time of drug sensitivity analysis and is the basis of PC research.

Capital Structure Challenges: Strategic Insights from Indonesia's Energy Sector

Capital Structure Challenges: Strategic Insights from Indonesia's Energy Sector

Green degree decision and pricing strategy of dual-channel supply chains

With the prevalence of green supply chains, the government has basic requirements for companies’ green investments and outcomes while consumers increasingly favor green products. Thus, green degree decision has garnered significant attention from manufacturers. This paper incorporates green degree decisions into a dual-channel supply chain and adopts a Stackelberg model to analyze the green degree and pricing strategies under centralized and decentralized decisions. We find that, when the manufacturer only decides on price, dual-channel choice is always the optimal strategy under centralized decision-making; however, under decentralized decision-making, the dual-channel choice will be chosen only when the wholesale price is low. Considering green degree decision, both direct and indirect channel prices increase with the green degree, and the indirect channel price is more sensitive to changes in the green degree under centralized decision-making; and higher green degrees are always advantageous for the retailer, but the manufacturer’s profit initially decreases and then increases as the green degree rises under decentralized decision-making. Moreover, the wholesale price is used as a strategic tool for the manufacturer to control the distribution channel, particularly when the green degree is not introduced, the manufacturer can always ensure the introduction of dual channels. Besides, higher consumers’ environmental awareness is always beneficial to channel members, as it promotes channel prices and green degree. This study provides strategic insights for optimizing pricing and green degree decisions in dual-channel supply chains to achieve better economic and environmental outcomes.

Punching above their weight? Examining player development pathways in Belgian football: a retrospective analysis

PurposeBelgium has consistently produced world-class footballers, outperforming many larger football associations despite having a smaller talent pool and fewer resources. This paper aims to (1) examine the development pathways of Belgian professional players over the past decade and (2) quantify the role of opportunities in European leagues outside the “Big 5” for a comparative analysis of different player development systems. By exploring how Belgian players progress through various leagues and systems, this study seeks to identify strategic insights that can inform the development practices of both Belgian football and other European nations with similar constraints.Design/methodology/approachData on player performance were collected over ten seasons (2013/14 to 2022/23) from 15 European leagues, including the “Big 5” (England, France, Germany, Italy and Spain) and ten other prominent leagues (Austria, Czech Republic, Denmark, Greece, Netherlands, Poland, Portugal, Scotland, Switzerland and Turkey). The variables collected included player name, nationality, team, league, season, appearances, minutes played, age and position. For Belgian players in the sample, the youth academies they attended were also noted.FindingsBelgian players have achieved significant success, particularly in terms of transitioning into the “Big 5” leagues. However, the number of “home-grown” players making appearances and gaining minutes in the Belgian league is relatively low (ranking 13th of the 15 leagues studied). In contrast, Belgian players rank third in terms of total minutes played in the “Big 5” leagues, behind Portuguese and Dutch players. Anderlecht was the most successful Belgian club in developing professional players, and other key contributors were clubs with consistent Champions League participation.Originality/valueWhile substantial research exists on player development in the “Big 5” European leagues, comparatively little attention has been given to other significant European leagues. This paper addresses that gap by examining the role of smaller leagues in developing young talent, despite their limited resources and reduced access to Europe's elite competitions. The findings highlight the need for policy reforms and strategic improvements at both the national and European levels to enhance player development pathways.

An Evolutionary Game Analysis of China’s Power Battery Export Strategies Under Carbon Barriers

With the continuous evolution of international trade, the global market has been steadily expanding while also facing increasing challenges, particularly in relation to the introduction of environmental policies such as carbon barriers. Our research explores how China’s power battery manufacturers can adapt their export strategies to the EU’s carbon barrier policies. Additionally, we examine the roles of government regulations, research institutions, and manufacturers in either facilitating or hindering compliance with carbon reduction objectives. Using evolutionary game theory, we construct models involving government entities, manufacturers, and research institutions to systematically analyze market evolution, strategic interactions, and outcomes among these stakeholders. Our analysis focuses on understanding the competitive dynamics faced by exporting countries under stringent environmental policies and provides strategic insights to guide export strategies. Taking the EU’s carbon barrier policy as a case study, we explore Chinese battery manufacturers’ adaptive strategies and decision-making processes as they respond to shifting market demands and regulatory environments. The findings not only offer valuable insights into exporting countries but also provide policymakers with information on international trade and industrial policy design. Furthermore, we validate our model through numerical simulations and conduct sensitivity analyses on key parameters. The results underscore the importance of governmental adoption of punitive and incentive policies, revealing their substantial impact on stakeholder behavior. Additionally, the study highlights how participants’ pre-cooperation losses and post-cooperation gains influence participation rates and the speed at which stakeholder consensus is reached. By offering a novel approach with which to address carbon barrier challenges, this research contributes valuable perspectives on environmental regulations’ strategic and policy implications in global trade.

In-Situ HF Forming Agents for Sustainable Manufacturing of Iron-Based Oxygen Reduction Reaction Electrocatalysis Synthesized Through Sacrificial Support Method.

Fe-Nx-Cs being suitable to replace scarce and overpriced platinum group metals (PGMs) for cathodic oxygen reduction reaction (ORR) are gaining significant importance in the fuel cell arena. Although the typical sacrificial support method (SSM) ensures the superior electrocatalytic activity of derived Fe-Nx-C, removing silica hard templates always remains a great challenge due to the hazardous use of highly toxic and not environmentally friendly hydrofluoric acid. Herein, strategic insight was given to modified SSM by exploiting the in-situ formation of HF, deriving from the decomposition of NH4HF2 and NaF, to dissolve silica templates, thus avoiding the direct use of HF. First, the suitable molar ratio between the etching agent and the silica was analyzed, revealing that NH4HF2 efficiently dissolved silica even in a stoichiometric amount, whereas an excess of NaF was required. However, both etching agents exhibited conformal removal of silica while dispersed active moieties within the highly porous architecture of derived electrocatalysts were left behind. Moreover, NH4HF2-washed counterparts demonstrated relatively higher performance both in acidic and alkaline media. Notably, with NH4HF2-washed Fe-Nx-C electrocatalyst, a remarkable onset potential of 970 mV (vs RHE) was achieved with nearly tetra-electronic ORR as the peroxide yield remained less than 10 % in the alkaline medium.

Exploring the paradoxical use of ChatGPT in education: Analyzing benefits, risks, and coping strategies through integrated UTAUT and PMT theories using a hybrid approach of SEM and fsQCA

ChatGPT's impact on education is both significant and inevitable, presenting a paradox of threats and benefits. While studies have explored ChatGPT's usage, intention, and motivation, few have addressed its paradoxical use from a benefit-risk-coping perspective, leaving gaps in practical and theoretical solutions. This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) and Protection Motivation Theory (PMT) to elucidate ChatGPT's paradoxical use in education. Using Structural Equation Modeling (SEM) and fuzzy sets Qualitative Comparative Analysis (fsQCA), it aims to provide insights and solutions for managing the benefits, risks, and coping mechanisms associated with ChatGPT in education. Hypotheses and propositions were tested on Taiwanese higher education users (N = 351). Findings from SEM confirm that the perceived threat of ChatGPT's severity decreases the intention to use it, while coping strategies such as self-efficacy and response efficacy strongly predict the intention to use ChatGPT. Additionally, the benefits of performance expectancy and task efficiency significantly increase the intention to use ChatGPT, which in turn significantly increases actual usage behavior. Findings from fsQCA reveal three configurations for both the usage and disusage of ChatGPT in education. The study identifies three constructs with necessary conditions. This research makes a significant theoretical contribution by integrating UTAUT and PMT into a unified framework to elucidate the paradoxical aspects of benefit-risk-coping in the use of ChatGPT. Practical implications for higher educational institutions and their scholars (e.g., students, lecturers, researchers) are also provided through confirmed solutions from the QCA model. These solutions offer strategic insights to leverage the benefits, identify the risks, and cope with the threats associated with using ChatGPT in education.

Regulation of ferroptosis in osteoarthritis and osteoarthritic chondrocytes by typical MicroRNAs in chondrocytes

Osteoarthritis (OA) is a progressive degenerative disorder impacting bones and joints, worsened by chronic inflammation, immune dysregulation, mechanical stress, metabolic disturbances, and various other contributing factors. The complex interplay of cartilage damage, loss, and impaired repair mechanisms remains a critical and formidable aspect of OA pathogenesis. At the genetic level, multiple genes have been implicated in the modulation of chondrocyte metabolism, displaying both promotive and inhibitory roles. Recent research has increasingly focused on the influence of non-coding RNAs in the regulation of distinct cell types within bone tissue in OA. In particular, an expanding body of evidence highlights the regulatory roles of microRNAs in OA chondrocytes. This review aims to consolidate the most relevant microRNAs associated with OA chondrocytes, as identified in recent studies, and to elucidate their involvement in chondrocyte metabolic processes and ferroptosis. Furthermore, this study explores the complex regulatory interactions between long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) in OA, with an emphasis on microRNA-mediated mechanisms. Finally, critical gaps in the current research are identified, offering strategic insights to advance the understanding of OA pathophysiology and guide therapeutic developments in this field.

The Differential Effects of Augmented Reality and Product Presentation Strategies on Brand Recall: An Embodied Cognition Perspective

As augmented reality (AR) technology becomes more prevalent in marketing, its impact on consumer memory processes, particularly brand recall, remains underexplored. This study primarily employs an empirical research method, using two lab experiments to examine the interaction between product presentation formats (AR vs. non-AR) and presentation strategies (separate vs. collocation) in brand recall. Across two experiments, we showed that AR enhances brand recall only in collocation presentations, where multiple products are displayed together, but not in separate presentations of individual products. In single-product contexts, AR formats do not demonstrate a significant advantage over non-AR formats. These findings suggest that AR’s effectiveness is contingent on presentation strategy, highlighting the contextual boundaries of AR’s utility in influencing consumer memory. By integrating embodied cognition theory with associative network theory, this research advances our understanding of how immersive technologies shape brand recall, offering strategic insights for marketers seeking to leverage AR in diverse product presentation scenarios.

Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model

This study addresses the academic gap in the adoption of generative AI systems by investigating the factors influencing technology acceptance and usage behavior in Korean firms. Although recent advancements in AI are accelerating digital transformation and innovation, empirical research on the adoption of these systems remains scarce. To fill this gap, this study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) model, surveying 300 employees from both large and small enterprises in South Korea. The findings reveal that effort expectancy and social influence significantly influence employees’ behavioral intention to use generative AI systems. Specifically, effort expectancy plays a critical role in the early stages of adoption, while social influence, including support from supervisors and peers, strongly drives the adoption process. In contrast, performance expectancy and facilitating conditions show no significant impact. The study also highlights the differential effects of age and work experience on behavioral intention and usage behavior. For older employees, social support is a key factor in technology acceptance, whereas employees with more experience exhibit a more positive attitude toward adopting new technologies. Conversely, facilitating conditions are more critical for younger employees. This study contributes to the understanding of the interaction between various factors in AI technology adoption and offers strategic insights for the successful implementation of AI systems in Korean companies.

Signaling Effects in AI Streamers: Optimal Separation Strategy Under Different Market Conditions

The fusion of livestreaming e-commerce and AI technology is booming, and many firms have started to replace human streamers with AI streamers. Despite their popularity, the acceptance of AI streamers by consumers varies widely and the signaling effects of AI streamers still remain unclear. We build an analytical model and compare scenarios where the acceptance level is either exogenously given or endogenously determined, highlighting the implications for firms’ optimal separation strategy. Our findings suggest that in markets with moderate information asymmetry, using both price and acceptance level as joint signals can be more profitable for high-quality firms. Conversely, in highly asymmetric markets, firms must incur additional costs to distinguish their high-quality products, regardless of the signaling strategy employed. Our paper provides strategic insights for firms aiming to leverage AI streamers in diverse market conditions.

Empowering Consumer Decision-Making: Decoding Incentive vs. Organic Reviews for Smarter Choices Through Advanced Textual Analysis

Online reviews play a crucial role in influencing seller–customer dynamics. This research evaluates the credibility and consistency of reviews based on volume, length, and content to understand the impacts of incentives on customer review behaviors, how to improve review quality, and decision-making in purchases. The data analysis reveals major factors such as costs, support, usability, and product features that may influence the impact. The analysis also highlights the indirect impact of company size, the direct impact of user experience, and the varying impacts of changing conditions over the years on the volume of incentive reviews. This study uses methodologies such as Sentence-BERT (SBERT), TF-IDF, spectral clustering, t-SNE, A/B testing, hypothesis testing, and bootstrap distribution to investigate how semantic variances in reviews could be used for personalized shopping experiences. It reveals that incentive reviews have minimal to no impact on purchasing decisions, which is consistent with the credibility and consistency analysis in terms of volume, length, and content. The negligible impact of incentive reviews on purchase decisions underscores the importance of authentic online feedback. This research clarifies how review characteristics sway consumer choices and provides strategic insights for businesses to enhance their review mechanisms and customer engagement.

A strategic insight into the market for carbon management capacity

This study presents the market for carbon management capacity via carbon capture, utilization, and storage technologies, identifying demand and supply forces, as well as clarifying the potential impact of market and non-market-based shocks on technology developers versus adopters. The paper addresses a prevailing gap in market analysis, introducing a microeconomic framework and unique dataset to identify key players, market forces, and policy incentives shaping the carbon capture, utilization, and storage landscape. The analysis equips industry stakeholders, policymakers, and investors with valuable insights regarding (1) leaders in the design, development, and manufacture of carbon capture, utilization, and storage technologies (supply), (2) early technology adopters (demand) who are at the forefront in the pursuit of sustainable carbon management, and (3) the need to use both market and non-market forces to achieve decarbonization across multiple sectors.

Enhancing bikeshare planning and operation: Strategic use of web-based public feedback

Bikeshare system operators have leveraged online platforms to gather user feedback on station experiences and service quality. This crowdsourced data presents a valuable opportunity to enhance bikeshare planning and operations. However, planners have limited methods to analyze those qualitative data given the sparse nature of this data, which is insufficient for training comprehensive machine learning models. Addressing this challenge, our study employs a novel approach combining spatial analysis and Factor Analysis of Mixed Data (FAMD) to delve into bikeshare users’ perceptions and expectations. Focusing on Chicago’s bikeshare system, we utilize its limited online comments to demonstrate the robustness of our algorithm and its applicability to both large and small datasets. Our findings reveal a geographic pattern in feedback: negative comments on bike rebalancing, station locations, and facilities are concentrated in the city center, while dissatisfaction with the cycling environment is consistent across both urban and peripheral areas. Moreover, we discovered that the demographic and employment characteristics of areas surrounding bikeshare stations significantly influence positive feedback, overshadowing the impact of station design and local infrastructure. Overall, this study offers a quantitative framework for leveraging limited crowdsourced feedback effectively, providing strategic insights for refining bikeshare planning and operational decisions.

Can intergenerational relationships mitigate digital exclusion among China’s elderly population?

ABSTRACT In the burgeoning grey economy, the elderly have been widely praised for their digital readiness dividends. However, there remains a significant gap in the literature regarding solutions to the critical issue of digital exclusion faced by the elderly. This study utilizes the microdata from three waves of the China Family Panel Studies (CFPS) in 2016, 2018, and 2020 to explore how intergenerational relationships influence the digital exclusion of older adults and identify the mechanisms. The findings reveal that strong intergenerational bonds significantly narrow the digital divide for the elderly. The analysis highlights that such relationships have a more pronounced impact on mitigating elderly digital exclusion among early-aged parents, males, residents of rural areas, and families where parents and offsprings cohabit. The mechanisms through which these relationships exert their influence include promoting intergenerational support exchanges, providing digital guidance from children to parents, and enhancing the elderly’s acceptance of digital technology. Furthermore, this paper employs the ‘one-child policy’ as a quasi-natural experiment to examine the moderating role. The findings underscore the value of nurturing parent-child bonds in reducing digital exclusion among the elderly, offering strategic insights for addressing this issue in societies experiencing both ageing populations and declines in birth rates.

The Impact of Quiet Quitting on Turnover Intentions in the Era of Digital Transformation: The Mediating Roles of Job Satisfaction and Affective Commitment, and the Moderating Role of Psychological Safety

This study examines the impact of “quiet quitting” during digital transformation on job satisfaction, affective commitment, and turnover intention. A time-lagged survey was conducted over six months with two waves of data collection from full-time employees in South Korea, resulting in 258 valid responses. Using the PROCESS macro for analysis, the findings reveal that quiet quitting significantly reduces job satisfaction and affective commitment, which, in turn, increases turnover intention. Furthermore, psychological safety serves as a critical moderator, buffering the negative effects of quiet quitting on these outcomes. The study confirms that the influence of quiet quitting on turnover intention operates through the mediating roles of job satisfaction and affective commitment, and organizations with higher psychological safety are better equipped to mitigate these adverse effects. Grounded in Withdrawal Progression Theory, this study empirically demonstrates the progression of quiet quitting into turnover intention and provides strategic insights for organizations to prevent employee turnover and maintain employee engagement during digital transformation.