Search results

This study investigates how individuals' self-awareness (specifically, private and public self-awareness) and environment-awareness (perceived expertise, similarity and familiarity) shape herd behavior, encompassing discounting one’s information and imitating others. Drawing from latent state-trait theory, this research aims to discern the impact of these factors on purchase intention and behavior.

Longitudinal data from 231 users in Xiaohongshu, China’s leading social commerce platform, were collected to test the proposed model and hypotheses.

The findings from this study show that private self-awareness negatively influences discounting one’s own information and imitating others. Public self-awareness positively affects imitating others, while it does not affect discounting one’s own information. Perceived expertise diminishes discounting one’s own information but does not significantly affect imitating others. Perceived similarity and perceived familiarity are positively related to discounting one’s own information and imitating others. The results confirm different interaction effects between self-awareness and environment-awareness on herd behavior.

First, this contributes back to the latent state-trait theory by expanding the applicability of this theory to explain the phenomenon of herd behavior. Second, this study takes an important step toward theoretical advancement in the extant literature by qualifying that both self- and environment-awareness should be considered to trigger additional effects on herd behavior. Third, this study provides a more enlightened understanding of herd behavior by highlighting the significance of considering the interplay between self- and environment-awareness on herd behavior. Finally, this study also empirically confirms the validity of classifying self-awareness into private and public aspects.

This paper aims to focus on the issue of high employee turnover in the Indian tech industry. An integrative review is conducted to analyse the past and current state of literature, as well as prepare a research agenda for future studies.

A pool of 72 articles published between 2010 and 2022 is reviewed with a special focus on Indian tech employees. This study elucidates the extent and impact of employee retention strategies through content analysis.

Two broad perspectives have been established in the literature: the reasons for quitting and the explanations for staying. By means of a comprehensive review, this paper combines these two aspects of literature and suggests factors under organization’s control to retain competent tech employees.

The study is designed to integrate the two theoretical viewpoints of employee turnover literature by consolidating the reasons behind quitting behaviour and staying intention. Codes combining the two aspects are presented as a valuable resource to retain tech talent.

The present study aims to gauge the experience of gender non-binary (NB) employees in the context of employee value propositions (EVP) in Indian enterprises and make suggestions for organizations to align their gender-aligned interventions with the EVP framework.

Qualitative methodology was used for collecting data through semi-structured interviews and subsequent analysis of the transcripts. The data was gathered from 10 NB participants working in Indian enterprises with the use of non-probabilistic purposive snowball sampling.

The analysis revealed eight themes representing the good, bad and ugly experiences of NB individuals within the context of EVP. These findings underscore the potential of enriching value propositions for employees to promote gender inclusion in corporate settings, contributing to long-term organizational success.

The study offers both theoretical and practical implications for fostering inclusivity at the workplace. It suggests that policymakers and organizations should align EVP with diversity and inclusion initiatives, re-evaluate hiring processes and promotion policies to ensure equal opportunities for NB individuals, provide regular staff training to address biases and implement inclusive insurance policies and representation in employee resource groups (ERGs).

This study provides unique insights into the experiences of NB employees within the framework of EVPs in Indian organizations.

This study assesses the SDG-related well-being of indigenous communities in India using bibliometric analysis and the ADO-TCM framework. It provides insights into their alignment with sustainable development objectives.

This study analysed 74 high-impact journals using bibliometric analysis to evaluate the well-being of India’s indigenous peoples about the SDGs.

This study analyses the well-being of tribal communities in India using existing scholarly articles and the ADO-TCM framework. It emphasises the importance of implementing Sustainable Development Goals (SDGs) to promote the well-being of indigenous populations.

This study uses bibliometric analysis and the ADO-TCM framework to investigate factors impacting tribal community welfare. It proposes theoretical frameworks, contextual considerations and research methodologies to achieve objectives.

With the increasing use of Internet and mobile handsets, mobile-based electronic payment apps (MEPAs) are becoming very popular around the world due to having various advantages. The intention of this exploratory study is to investigate the role/impact of selected factors in adopting three MEPAs of China: Wechat, Alipay and UnionPay based on the extended technology acceptance model (ETAM). After conducting a thorough and careful literature review, this study identified and divided seven such factors into three broad categories: (1) technological factors: perceived ease of use (PEU) and perceived usefulness (PU); (2) personal factors: perceived trust (PT), perceived privacy (PP) and perceived risk (PR); and (3) social factors: social influence (SI) and peer influence (PI) that were assumed to affect the intention to adopt MEPAs (IAMEPAs).

1,597 Chinese individuals were selected through purposive sampling technique who regularly used MEPAs at the time of collecting data. For collecting primary data from the selected respondents, a cross-sectional survey instrument was used. The study utilized IBM SPSS 25 for descriptive statistics and a second generation covariance-based structural equation modeling (CB-SEM) technique through AMOS 25 for testing the hypothesized relationships.

From statistical analysis, it was identified that five factors: PEU, PU, PT, SI and PI have significant positive impact on the dependent variable, IAMEPAs while PR and PP have insignificant influence on IAMEPAs.

This is one of the studies ever conducted to discover the factors that can have impact on the adoption of MEPAs using ETAM. It is strongly expected that this exploratory study can motivate the scholars to commence additional investigations regarding this increasingly popular financial technology (Fin-Tech). In addition, it can be anticipated that the MEPA service providers can widen their service effectiveness according to the users’ opinion reflected in this study. Furthermore, policymakers involved with Fin-Tech can also formulate and implement effective policies and guidelines based on the empirical outcomes.

This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms.

The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model.

The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners.

This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies.

In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

This paper aims to focus on the synthesis of the macrocyclic complexes (Cu and Zn) and their applications as anticorrosive materials in epoxy paint formulation for surface coating application.

A selected macrocyclic Cu(II) and Zn(II) complexes were prepared via template synthesis and characterized using Fourier transform infrared, thermal gravimetric analysis, scanning electron microscope, flexibility, hardness and adhesion of coating films prepared using epoxy paint.

The corrosion resistance of the epoxy-painted films was improved due to the incorporation of the Zn and Cu complexes into the formulation.

It was found that the metal complex-based formulation with Cu(II) and Zn(II) had outperformed the sample blank.

Faraoyść is a translinguistic portmanteau neologism that describes the moment when oppressive systems are shaken and appear to be coming to an end, and joyful, liberated worlds feel within reach. The purpose of this research is to demonstrate that faraoyść helped participants helped participants to expand their situated imaginings, which increased their capacity to imagine decolonized worlds.

This research was guided by faraoyść as a conceptual framework that explores the empirical experience of joy through collaborative world-building activities. These praxis-based exercises were tested in a series of workshops both at the 2020 UNESCO Futures Literacy Summit and in collaboration with Negligence Refugees from Lebanon.

When activated by collaboratively designed speculative objects and stories generated through the lens of faraoyść, participants created spaces of rhizomatic world-building that allowed them to imagine beyond the boundaries of their situated imaginings. Once participants had mapped the ways their imaginations were limited by current colonial systems of power, they were able to reorient their roles and develop new means to act within decolonized systems.

Faraoyść is a novel conceptual framework that contributes to current movements to decolonize futuring and foresight. This paper also introduces the concepts of rhizomatic world-building – an emergent approach to co-imagination, and situated imaginings, which are the systemic frameworks within which one imagines the ways the world has, is, will and must work. In practice, faraoyść is grounded in abundance and the power of liberatory joy to strengthen and celebrate local traditions, storytelling, world-building and community power.

Concrete-filled double skin steel tubes (CFDST) columns are taken more attention due to their ability to withstand high structural loads in structures such as high-rise buildings, bridges' piers, offshore and marine structures. This paper is intended to improve the CFDST column's capacity without the need to increase the column's size to maintain its lightweight by filling it with self-compacted concrete (SCC) containing nanoclay (NC).

First, experimental investigation is conducted to select the optimal NC percentage that improves the mechanical properties. Different mixing method, mixture ingredients, cement content, and NC percentage are considered. Then, slender and short CFDST columns are tested for axial capacity to investigate the effect of adding the optimum NC percentage on column's capacity and failure mode.

The test results show that adding 3% NC by cement weight using dry mixing method to SCC is the optimum ratio. It is concluded that adding 3% NC by cement weight increased the CFDST column's capacity, especially the specimens with higher slenderness ratio. Moreover, it is concluded that more specimens should be tested under various geometric and reinforcement details.

Recently, CFDST tube columns solve many structural and architectural problems that engineers have encountered in traditional systems. Therefore, more studies are required to design high-performance columns capable of carrying complex loads with high efficiency since the traditional design could not achieve the required performance. Since concrete contributes to a large portion in the axial capacity of the CFDST columns, it is proposed to improve the CFDST column's capacity without the need to increase the column's size to maintain its lightweight by filling it with (SCC containing NC. Previous research has affirmed the effectiveness of employing nanoclay in the concrete's workability, durability, microstructures, and mechanical properties.

This research aims to extract Industry 4.0 technological building blocks (TBBs) capable of value generation in collaborative consumption (CC) and the sharing economy (SE). Furthermore, by employing a mixed methodology, this research strives to analyse the relationship amongst TBBs and classify them based on their impact on CC.

Due to the importance of technology for the survival of collaborative consumption in the future, this study suggests a classification of the auxiliary and fundamental Industry 4.0 technologies and their current upgrades, such as the metaverse or non-fungible tokens (NFT). First, by applying a systematic literature review and thematic analysis (SLR-TA), the authors extracted the TBBs that impact on collaborative consumption and SE. Then, using the Bayesian best-worst method (BBWM), TBBs are weighted and classified using experts’ opinions. Eventually, a score function is proposed to measure organisations’ readiness level to adopt Industry 4.0 technologies.

The findings illustrated that virtual reality (VR) plays a vital role in CC and SE. Of the 11 TBBs identified in the CC and SE, VR was selected as the most determinant TBB and metaverse was recognised as the least important. Furthermore, digital twins, big data and VR were labelled as “fundamental”, and metaverse, augmented reality (AR), and additive manufacturing were stamped as “discretional”. Moreover, cyber-physical systems (CPSs) and artificial intelligence (AI) were classified as “auxiliary” technologies.

With an in-depth investigation, this research identifies TBBs of Industry 4.0 with the capability of value generation in CC and SE. To the authors’ knowledge, this is the first research that identifies and examines the TBBs of Industry 4.0 in the CC and SE sectors and examines them. Furthermore, a novel mixed method has identified, weighted and classified pertinent technologies. The score function that measures the readiness level of each company to adopt TBBs in CC and SE is a unique contribution.