Search results

This study aims to explore the relationship between elderly users' trust in public digital cultural services (PDCS) and their intention to use PDCS, and reveal the factors affecting their intentions from the perspective of trust to make recommendations that will increase their intention to use PDCS.

Combined with the trust building model and social exchange theory, this study constructed a conceptual model of elderly users' intention to use PDCS. Data collected from Chinese elderly users who have reached the age of 60 through questionnaire surveys were tested using the structural equation model with partial least squares. Finally, the authors proposed a model of elderly users' intention to use PDCS.

This study finds that elderly users' trust positively affects their intention to use PDCS from two aspects: service features and user features of PDCS. Concerning the service features, system quality directly affects elderly users' trust in PDCS most significantly, followed by information quality and service reputation. Concerning the user features, perceived value has a higher impact on elderly users' trust than that of service features, and information literacy and information quality directly affect perceived value.

This study adds new knowledge to the users' behavior of PDCS and enriches the prior description of PDCS. The recommendations made in this study provide a series of strategies for practitioners and researchers to improve the elderly users' intention to use PDCS and bridge the silver digital divide, which offers new ideas for improving the efficiency of PDCS.

The study aims to examine the impact of cultural dependency stemming from exchange rate fluctuations (specifically the US dollar) on herding behavior in the housing market across 31 provinces of Iran from Q2 2011 to Q1 2022, using a spatial econometrics approach. After confirming the presence of spatial effects, the Dynamic Spatial Durbin Panel Model with Generalized Common Effects (SDM-DPD(GCE)) was selected from various spatial models for these provinces.

The study examines the impact of cultural dependency stemming from exchange rate fluctuations (specifically the US dollar) on herding behavior in the housing market across 31 provinces of Iran from Q2 2011 to Q1 2022, using a spatial econometrics approach. After confirming the presence of spatial effects, the Dynamic Spatial Durbin Panel Model with Generalized Common Effects (SDM-DPD(GCE)) was selected from various spatial models for these provinces.

The model estimation results indicate that fluctuations in the free market exchange rate of the dollar significantly and positively impact the housing market in both target and neighboring regions, fostering herding behavior characterized by cultural dependency within the specified timeframe. Additionally, the study found that variables such as the inflation rate, population density index and the logarithm of stock market trading volume have significant and positive impacts on the housing market. Conversely, the variable representing the logarithm of the distance from the provincial capital, Tehran, significantly and negatively impacts the housing market across Iranian provinces.

Given that housing is a fundamental need for households, the dramatic price increases in this sector (for instance, a more than 42-fold increase from 2011–2021) have significantly impacted the welfare of Iranian families. Currently, considering the average housing price in Tehran is around 50 million Tomans, and the average income of worker and employee groups is 8 million Tomans (as of 2021), the time required to purchase a 100-square-meter house, even with a 30% savings rate and stable housing prices, is approximately 180 years. Moreover, the share of housing and rent expenses in household budgets now constitutes about 70%. The speculative behavior in this market is so acute that, despite 25 million of Iran’s 87 million population being homeless or renting, over 2.5 million vacant homes (12% of the total housing stock) are not used. Therefore, various financial behaviors and decisions affect Iran’s housing market. Herd behavior is triggered by the signal of national currency devaluation (with currency exchange rates increasing more than 26-fold between 2011 and 2021) and transactions at higher prices in certain areas (particularly in northern Tehran) (Statistical Center of Iran, 2023). Given the origins of housing price surges, a price increase in one area quickly spreads to other regions, resulting in herd behavior in those areas (spillover effect). Consequently, housing market spikes in Iran tend to follow episodes of currency devaluation. Therefore, considering the presented discussions, one might question whether factors other than economic ones (such as herd behavior influenced by dependence culture) play a role in the rising housing prices. Or, if behavioral factors were indeed contributing to the increase in housing prices, what could be the cause of this herd movement? Has the exchange rate, particularly fluctuations in the free market dollar rate, triggered herd behavior in the housing market across Iran’s provinces? Or has the proximity and neighborhood effect been influential in the increase or decrease in housing prices in the market?

The purpose of this article is to analyze the existing studies on the application of artificial intelligence (AI) in lean construction management (LCM). Further, this study offers a classification scheme that specifies different categories of AI tools, as applied to the field of LCM to support various principles of LCM.

This research adopts the systematic literature review (SLR) process, which consists of five consecutive steps: planning, searching, screening, extraction and synthesis and reporting. As a supplement to SLR, a bibliometric analysis is performed to examine the quantity and citation impact of the reviewed papers.

In this paper, seven key areas related to the principles of LCM for which AI tools have been used are identified. The findings of this research clarify how AI can assist in bolstering the practice of LCM. Further, this article presents directions for the future evolution of AI tools in LCM based on the current emerging trends.

This paper advances the LCM systems by offering a lens through which construction managers can better understand key concepts in the linkage of AI to LCM.

This research offers a new classification scheme that allows researchers to properly recall, identify and group various applications of AI categories in the construction industry based on various principles of LCM. In addition, this study provides a source of references for researchers in the LCM discipline, which advances knowledge and facilitates theory development in the field.

This study aims to empirically investigate how knowledge paradox affects collaboration performance. Knowledge paradox, which arises from the simultaneous need for knowledge sharing and protection, is common in interorganizational collaboration. Using the ambidexterity perspective, this paper aims to reexamine the effect of the knowledge paradox on collaborative performance to explore the moderating roles of structural and contextual ambidexterity.

This study used a sample of 153 firms involved in vertical and horizontal collaboration, collected via questionnaires. Hypotheses were tested using hierarchical regression analysis.

This study demonstrates that the stronger the knowledge paradox is, the higher the potential for value creation. Thus, knowledge paradox has a positive impact on collaborative performance. The functions of structural ambidexterity and contextual ambidexterity strengthen this positive relationship.

This paper not only expands the theoretical application of the knowledge paradox and ambidexterity theory in the context of interorganizational relationships but also provides significant managerial implications. By comprehending the dynamics of the knowledge paradox and the role of ambidexterity, managers can make well-informed decisions to enhance their collaborative performance.

The purpose of this study is to examine the relationship between positive emotion, self-efficacy, job satisfaction and turnover intention in the context of resource building during the socialization process of new faculty members, particularly in the context of the coronavirus disease 2019 (COVID-19) pandemic.

The study utilizes a quantitative research design and employs purposive sampling to obtain 554 valid questionnaires. The study analyzes the relationship between positive emotion, self-efficacy, job satisfaction and turnover intention and examines the influence of strategic human resource management (SHRM) on these variables.

The results of the study reveal that SHRM positively influences positive emotion and self-efficacy, which, in turn, positively impact job satisfaction. However, positive emotion is negatively related to turnover intention.

This study contributes to the existing literature on human resource management (HRM) by examining the impact of strategic HRM on the socialization process of new faculty members. The findings of the study have significant practical implications for the implementation of HRM in research-oriented universities.

Although prior research has employed various variables to predict player churn, the dynamic evolution of the behavioral patterns of players has received limited attention. In this study, churn prediction models are developed by incorporating the progress level, in-game purchase, social interaction, behavioral pattern and behavioral variability (BV) of players in social casino games (SCGs). The study distinguishes churn prediction between two player groups: monetizers and non-monetizers.

This study employs three machine learning techniques—logistic regression, decision trees and random forests—using real-world player data from an SCG company to construct churn prediction models. Two experiments were conducted. In Experiment 1, BV was combined with four other variable categories to effectively predict churn behaviors across all players (n = 52,246). In Experiment 2, churn prediction models were developed separately for monetizers (n = 16,628) and non-monetizers (n = 35,618).

The findings from Experiment 1 indicate that incorporating BV significantly improves the overall performance of churn prediction models. Experiment 2 demonstrates that churn prediction models achieve better performance and predictive accuracy for monetizers and non-monetizers when BV is calculated over the 3-day to 7-day and 7-day to 14-day windows, respectively.

This study introduces BV as a novel variable category for churn prediction, emphasizing within-person variability and demonstrating its effectiveness in enhancing model performance. Churn prediction models were independently constructed for monetizers and non-monetizers, utilizing different time windows for variable extraction. This approach improves predictive performance and highlights key differences in critical variables influencing churn across the two player groups. The findings provide valuable insights into churn management strategies tailored for monetizers and non-monetizers.

This study aims to explore the development of trust in relation to security and privacy concerns, as well as the influence of perceived risk on the intention to use e-wallets. The research focused on the Colombian context because of its characterization as an underdeveloped financial system that has experienced considerable security and privacy violations in recent years. Additionally, this geographical area is relatively under-researched, and the target demographic for this investigation was Generation Z, given their pivotal role in driving the adoption of e-wallets.

Based on measurement scales that had already been tested in the academic literature on mobile payment systems, a questionnaire was developed and distributed electronically. A total of 424 responses were obtained from young Colombians. Structural equation modeling (SEM), specifically the PLS-SEM method, was used to process the data to study the explanatory and predictive power of the proposed model.

The findings revealed that security and privacy have a positive and significant effect on perceived trust, and that this at the same time has a positive and significant effect on attitude, perceived usefulness, perceived ease and intention to use, as well as a significant but negative effect on perceived risk. On the other hand, perceived risk showed an inverse, but not significant, relationship with intention to use.

This research explored the adoption of e-wallets by young people in Colombia, which has been widely claimed in the academic literature. In a turbulent context with a high distrust of financial institutions, as well as a change in digital money consumption patterns, it is critical to understand the factors that contribute to the adoption of mobile payment services. The findings, in addition to contributing to the academic debate, have important implications for e-wallet providers, as they offer information that allows designing strategies to attract and keep current and potential users. At the same time, the recommendations by the authors allow the design of tools, especially related to security and privacy, to improve their trust and build loyalty, thus contributing to the consolidation and development of the mobile payment system.

Este estudio explora el desarrollo de la confianza en relación con las preocupaciones sobre seguridad y privacidad, así como la influencia del riesgo percibido en la intención de utilizar monederos electrónicos. La investigación se centró en el contexto colombiano debido a su caracterización como un sistema financiero subdesarrollado que ha experimentado violaciones considerables de seguridad y privacidad en los últimos años. Además, esta área geográfica está relativamente poco investigada, y el público objetivo de esta investigación fue la Generación Z, dado su papel clave en la adopción de monederos electrónicos.

Basado en escalas de medición ya probadas en la literatura académica sobre sistemas de pago móvil, se desarrolló y distribuyó electrónicamente un cuestionario. Se obtuvieron un total de 424 respuestas de jóvenes colombianos. Se utilizó el modelado de ecuaciones estructurales, específicamente el método PLS-SEM, para procesar los datos y estudiar el poder explicativo y predictivo del modelo propuesto.

Nuestros hallazgos revelaron que la seguridad y la privacidad tienen un efecto positivo y significativo en la confianza percibida, y que esto, a su vez, tiene un efecto positivo y significativo en la actitud, la utilidad percibida, la facilidad percibida y la intención de uso, así como un efecto significativo pero negativo en el riesgo percibido. Por otro lado, el riesgo percibido mostró una relación inversa, pero no significativa, con la intención de uso.

Esta investigación exploró la adopción de monederos electrónicos por parte de los jóvenes en Colombia, un tema ampliamente mencionado en la literatura académica. En un contexto turbulento con alta desconfianza hacia las instituciones financieras, así como un cambio en los patrones de consumo de dinero digital, es crucial comprender los factores que contribuyen a la adopción de servicios de pago móvil. Nuestros hallazgos, además de contribuir al debate académico, tienen importantes implicaciones para los proveedores de monederos electrónicos, ya que ofrecen información que permite diseñar estrategias para atraer y retener a usuarios actuales y potenciales. Al mismo tiempo, nuestras recomendaciones permiten diseñar herramientas, especialmente relacionadas con la seguridad y la privacidad, para mejorar la confianza y fomentar la lealtad, contribuyendo así a la consolidación y desarrollo del sistema de pago móvil.

本研究探讨了与安全性和隐私问题相关的信任发展, 以及感知风险对使用电子钱包意图的影响。研究集中在哥伦比亚的背景下, 因为该国的金融系统被认为是欠发达的, 近年来经历了相当大的安全性和隐私侵犯问题。此外, 这一地理区域的研究相对较少, 本研究的目标人群为Z世代, 考虑到他们在推动电子钱包采纳中的关键作用。

基于已经在移动支付系统学术文献中测试过的测量尺度, 开发并电子分发了一份问卷。共获得了424份哥伦比亚年轻人的回应。使用结构方程建模中的PLS-SEM方法处理数据, 以研究所提出模型的解释力和预测力。

研究发现, 安全性和隐私对感知信任有显著正向影响, 同时, 感知信任对态度、感知有用性、感知易用性和使用意图也有显著正向影响, 并对感知风险有显著负向影响。另一方面, 感知风险与使用意图之间呈现出反向但不显著的关系。

本研究探讨了哥伦比亚年轻人对电子钱包的采纳, 这在学术文献中已有广泛讨论。在一个对金融机构高度不信任的动荡背景下, 以及数字货币消费模式的变化中, 理解促使移动支付服务采纳的因素至关重要。我们的研究结果不仅有助于学术讨论, 还对电子钱包提供商具有重要意义, 因为这些结果提供了信息, 有助于设计吸引和留住当前及潜在用户的策略。同时, 我们的建议有助于设计特别与安全性和隐私相关的工具, 以提高用户信任并建立忠诚度, 从而促进移动支付系统的巩固和发展。

The research aims to explore the influence mechanism of peer feedback and users' knowledge contribution behavior. This study draws on the social identity theory and considers social identity as a mediating factor into the research framework.

This paper collected users' activity data of 142,191 ideas submitted by 76,647 users from the MIUI community between October 2010 and May 2018 via Python software, and data were processed using Stata 16.0.

The results indicate that knowledge feedback and social feedback positively influence users' knowledge contribution (quantity and quality), respectively. User's cognitive identity positively mediates the relationship between peer feedback and knowledge contribution behavior, affective identity positively mediates the relationship between peer feedback and knowledge contribution behavior, while evaluative identity positively mediates the relationship between peer feedback and knowledge contribution quality, but there is no mediating effect between peer feedback and knowledge contribution quantity.

This study advances knowledge management by highlighting peer feedback on online innovation communities. By demonstrating the significant mediating effect of social identity, this study empirically clarifies the relationships of peer feedback (knowledge feedback and social feedback) to specific dimensions of knowledge contribution, thereby providing managerial guidance to the online innovation community on incentivizing and managing user interaction to foster the innovation development of firms.

The proliferation of digital communication on social media provides new opportunities for businesses to take advantage of Internet memes to boost customer engagement. Academic literature on digital communications mostly focuses on popular forms such as selfies, branded posts, and branded emoticons. Less attention has been paid to brand memes and their implications for brand management. Based on the cue utilization theory, this research aims to investigate the informational cues of brand memes foster brand partnerships.

The structural equation modeling and importance-performance matrix analysis were used to empirically validate the research hypotheses with 595 respondents to an online survey.

Three informational cues of brand memes (i.e. comprehensibility, novelty, and meme-brand congruity) stimulated consumers' attitudes, which in turn impacted consumer-brand relationships. Another brand meme informational cue, sarcasm, negatively moderated the relationships between the three informational cues and consumer-brand relationships.

Our findings indicate that a brand can engage consumers in conversations on social media and foster long-term consumer-brand relationships through brand memes.

The paper aims to examine the digital economy's influence on China's regional innovation and development. It focuses on direct effects and spatial spillover across regions, and the mediating role of human capital. This analysis is vital for policy and strategic planning in the digital era.

This study uses panel data from 30 Chinese provinces (2004–2019) and uses the entropy method to quantify the digital economy's development. It investigates its impact on regional innovation using a dynamic spatial Durbin model (SDM) and mediation effect model, assessing direct effects, spatial spillover and human capital's mediating role. Various control variables are included for comprehensive analysis.

Findings show the digital economy significantly boosts regional innovation, acting as a growth driver. However, impacts vary regionally, with the central region gaining more than the eastern and western areas. Spatial spillover effects are mixed, showing negative short-term and positive long-term impacts under different weight matrices. Human capital is crucial for fostering innovation through the digital economy.

The paper offers unique insights into the spatial dynamics of the digital economy's impact on regional innovation in China. It advances understanding of the digital economy's role in regional development using innovative methods like the entropy method and dynamic SDM. Highlighting human capital as a key mediating factor enriches discussions on digital economy strategies for regional innovation.