Search results

This study aims to explore the mechanism of abusive supervision differentiation on employee work-family conflict, and examine the chain mediating role of work-related rumination and organizational citizenship behavior/deviant workplace behavior, as well as the moderating role of work-family boundary segmentation preference.

The authors designed two studies: Study 1 was a scenario experiment with 120 Master of Business Administration students. To further explore this finding, the authors conducted a multiwave survey in Study 2 with 345 employees from various organizations.

The results of Study 1 showed that abusive supervision differentiation had a positive effect on work-related rumination, and work-related rumination mediated the relationship between differentiated abusive supervision and organizational citizenship behavior/deviant workplace behavior. The results of Study 2 not only confirmed the conclusions of Study 1 but also revealed that organizational citizenship behavior/deviant workplace behavior significantly affected work-family conflict. Abusive supervision differentiation had a positive effect on work-family conflict through work-related rumination and organizational citizenship behavior/deviant workplace behavior. In addition, work-family boundary segmentation preference negatively moderated the relationship between organizational citizenship behavior and work-family conflict.

First, to the best of the authors’ knowledge, this study is the first paper to test the spillover effect of abusive supervision differentiation on the family domain through a chain mediation model. It extends the research on abusive supervision differentiation from the work domain to the family domain. Second, previous research has highlighted role conflict or role insufficiency as significant factors contributing to work-family conflict. However, this study suggests that abusive supervision differentiation from workplace managers can also trigger work-family conflict, providing a new perspective in the study of precursors to work-family conflict.

This study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.

This study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.

This study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.

These findings can potentially help market participants, policymakers, stock exchanges and regulators.

Unlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

The purpose of this paper is to introduce the simple synthesis process of thermal-insulation coating by using three different nanoparticles, namely, nano-zinc oxide (ZnO), nano-tin dioxide (SnO₂) and nano-titanium dioxide (TiO₂), which can reduce the temperature of solar cells.

The thermal-insulation coating is designed using sol-gel process. The aminopropyltriethoxysilane/methyltrimethoxysilane binder system improves the cross-linking between the hydroxyl groups, -OH of nanoparticles. The isopropyl alcohol is used as a solvent medium. The fabrication method is a dip-coating method.

The prepared S1B1 coating (20 Wt.% of SnO₂) exhibits high transparency and great thermal insulation property where the surface temperature of solar cells has been reduced by 13°C under 1,000 W/m² irradiation after 1 h. Meanwhile, the Z1B2 coating (20 Wt.% of ZnO) reduced the temperature of solar cells by 7°C. On the other hand, the embedded nanoparticles have improved the fill factor of solar cells by 0.2 or 33.33%.

Findings provide a significant method for the development of thermal-insulation coating by a simple synthesis process and low-cost materials.

The thermal-insulation coating is proposed to prevent exterior heat energy to the inside solar panel glass. At the same time, it can prevent excessive heating on the solar cell’s surface, later improves the efficiency of solar cell.

This study presents a the novel method to develop and compare the thermal-insulation coating by using various nanoparticles, namely, nano-TiO₂, nano-SnO₂ and nano-ZnO at different weight percentage.

The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms.

Using structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses.

Financial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains.

We examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.

Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.

This study focuses on the self-organized cooperative consumption of platform participants on social media platform, and reveals how the brand owner cooperates with two-sided customers to achieve value co-creation.

The authors adopted a case study approach to explore how a Chinese beauty startup developed collaborative networks from 2013 to 2022, and tracked the the changes of network structure and cooperation mechanism.

The study finds that the brand owner cooperates with two-sided customers to integrate resources and establish diverse relational trust, which enhances the evolution of a heterogeneous collaborative network for value co-creation.

The study builds upon traditional dyadic actor-to-actor interactions between providers and customers, develops a novel interaction framework of actor-to-network to explain the value co-creation by collaborative networking, reveals the self-organized mechanism of cooperative consumption on social media.

This article seeks to understand how live-streaming technology (i.e. interactivity and effective use of live-streaming shopping’s information presentation tool) impacts consumers’ credibility perception regarding live streamers.

The authors empirically examined their hypotheses with data (n = 405) collected from a survey of consumers who engage in live-streaming shopping.

The results demonstrate that vicarious learning strategies (both coactive and independent) can shape consumers’ benefit perceptions (i.e. virtual presence and psychological proximity), and further have a positive effect on consumers’ personal value (i.e. perceived live streamer credibility). Furthermore, the consumers’ perception of the live streamers’ credibility positively affects their purchase intention and ultimately influences their purchase behavior.

Building on the vicarious learning theory and means-end chain (MEC) model, this study investigates the mechanism of the IT features of live-streaming shopping in reducing consumers’ uncertainty about live streamers. This study reveals the value of vicarious learning experiences in reducing consumers’ uncertainty and further enhancing their purchase behavior.

Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks.

In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle.

To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission.

The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.

To investigate the mechanical properties of geopolymer concrete at elevated temperatures.

The investigation involved studying the influence of partially replacing fly ash with ground granulated blast furnace slag (GGBS) at different proportions (5%, 10%, 15%, 20% and 25%) on the composition of the geopolymer. This approach aimed to examine how the addition of GGBS impacts the properties of the geopolymer material. The chemical NaOH was purchased from the local supplier of Jamshedpur. The alkali solution was prepared with a concentration of 12 M NaOH to produce the concrete. After several trials, the alkaline-to-binder ratio was determined to be 0.43.

The compressive strength values at 28 days for specimens FG1, FG2, FG3, FG4 and FG5 are 35.42 MPa, 41.26 MPa, 44.79 MPa, 50.51 MPa and 46.33 MPa, respectively. The flexural strength values at 28 days for specimens FG1, FG2, FG3, FG4 and FG5 are 5.31 MPa, 5.64 MPa, 6.12 MPa, 7.15 MPa and 6.48 MPa, respectively. The split tensile strength values at 28 days for specimens FG1, FG2, FG3, FG4 and FG5 are 2.82 MPa, 2.95 MPa, 3.14 MPa, 3.52 MPa and 3.31 MPa, respectively.

This approach allows for the examination of how the addition of GGBS affects the properties of the geopolymer material. Four different temperature levels were chosen for analysis: 100 °C, 300 °C, 500 °C and 700 °C. By subjecting the geopolymer samples to these elevated temperatures, the study aimed to observe any changes in their mechanical.

The current study aims to empirically analyze the influence of different information sources, together with the persuasiveness of recommender systems (RSs) on the consumer’s purchase intention (PI). It also expands the research on RSs from the point of view of consumer behavior and psychology, considering perceived usefulness and relevance. In addition, it analyzes how different types of personalized recommendations, along with non-personalized ones, influence PI.

The proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers.

This study proves that both information search and RSs influence PI, being complementary rather than mutually exclusive. Recommender systems’ findings indicate that the PI is primarily influenced by the perceived relevance of RSs, the information provided by manufacturers and reviews. Moreover, only the influence of the perceived usefulness of personalized recommendations strongly affects PI. Conversely, non-personalized recommendations do not affect PI.

Developers should focus on increasing the perceived usefulness and relevance of RSs. Thus, they could adopt the hybridization of RSs with the aggregation of both personal shopping behavior and social network contacts. It should integrate information signals from multiple sources to include sentiment extracted from reviews or links to the manufacturer’s page. Furthermore, the recommendation of discounted products must be only for products preferred by customers, because only these influence the PI.

This research provides a structural model that examines together, for the first time, the influence on the PI of the main RSs and sources of information.