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Driven by Davis’s (1989) technology acceptance model (TAM) and Westaby’s (2005) behavioral reasoning theory (BRT), the purpose of this study is to develop and test a conceptual model and examine consumers’ acceptance of artificial intelligence (AI) chatbots for apparel shopping.

Data from 353 eligible US respondents was collected through a self-administered questionnaire distributed on Amazon Mechanical Turk, an online panel. Confirmatory factor analysis and path analysis were used to test all hypothesized relationships using the structural equation model.

The results show that optimism and relative advantage of “reasons for” dimensions have a positive and significant influence on perceived ease of use (PEU), while innovativeness and relative advantage have a positive and significant influence on perceived usefulness (PUF). Discomfort and insecurity have no significant impact on PEU and PUF. However, complexity has a negative and significant impact on PEU but not on PUF. Additionally, PEU has a positive influence on PUF. Both PEU and PUF have a positive and significant influence on consumers’ attitudes toward using AI chatbots, which, in turn, affects the intention to use AI chatbots for apparel shopping. Overall, this study identifies that optimism, innovativeness and relative advantage are enablers and good reasons to adopt AI chatbots. Complexity is a prohibitor, making it the only reason against adopting AI chatbots for apparel shopping.

This study contributes to the literature by integrating TAM and BRT to develop a research model to understand what “reasons for” and “reasons against” factors are enablers or prohibitors that significantly impact consumers’ attitude and intention to use AI chatbots for apparel shopping through PEU and PUF.

This research paper aims to investigate the critical factors influencing the live commerce industry and their implications for Key Opinion Leaders (KOLs) and brands.

The study comprehensively reviews previous research, develops relevant hypotheses and utilizes personal information from 66 anchors, along with data from 23,000 product links obtained from the backends of live commerce platforms.

The study emphasizes that KOLs with higher traffic significantly influence Gross Merchandise Volume (GMV). Intriguingly, KOLs with lower traffic levels exhibit a more pronounced effect on Return on Investment (ROI), highlighting their significance in driving profitability. Furthermore, the study explores the correlation between KOL hashtags and GMV/ROI and the intricate relationship between product types and KOL hashtags.

The findings significantly enhance the understanding of live shopping behavior and provide valuable insights for business management strategies. Practitioners can leverage this empirical evidence to make informed decisions, utilizing extensive data samples of KOLs and brands.

This research contributes unique insights into the live-streaming commerce industry using backend data from Live Streaming E-commerce platforms. The findings are more accurate based on market data than previous studies that relied on platform reviews or questionnaires. Additionally, this paper investigates the impact of KOLs on the performance of live e-commerce from three perspectives: GMV, ROI and hot-selling products.

This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs).

Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique.

Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs.

This study adds to the literature by understanding how customers’ technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct.

The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs.

The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention.

The purpose of this study is to understand the profit implications of analytics-driven centralized discriminatory pricing at the headquarter level compared with sales force price delegation in the purchase of an aftermarket good through an indirect retail channel with symmetric information.

Using individual-level loan application and approval data from a North American financial institution and segment-level customer risk as the price discrimination criterion for the firm, the authors develop a three-stage model that accounts for the salesperson’s price decision within the limits of the latitude provided by the firm; the firm’s decision to approve or not approve a sales application; and the customer’s decision to accept or reject a sales offer conditional on the firm’s approval. Next, the authors compare the profitability of this sales force price delegation model to that of a segment-level centralized pricing model where agent incentives and consumer prices are simultaneously optimized using a quasi-Newton nonlinear optimization algorithm (i.e. Broyden–Fletcher–Goldfarb–Shanno algorithm).

The results suggest that implementation of analytics-driven centralized discriminatory pricing and optimal sales force incentives leads to double-digit lifts in firm profits. Moreover, the authors find that the high-risk customer segment is less price-sensitive and firms, upon leveraging this segment’s willingness to pay, not only improve their bottom-line but also allow these marginalized customers with traditionally low approval rates access to loans. This points out the important customer welfare implications of the findings.

Substantively, to the best of the authors’ knowledge, this paper is the first to empirically investigate the profitability of analytics-driven segment-level (i.e. discriminatory) centralized pricing compared with sales force price delegation in indirect retail channels (i.e. where agents are external to the firm and have access to competitor products), taking into account the decisions of the three key stakeholders of the process, namely, the consumer, the salesperson and the firm and simultaneously optimizing sales commission and centralized consumer price.

Ohmic heating generates temperature with the help of electrical current and resists the flow of electricity. Also, it generates heat rapidly and uniformly in the liquid matrix. Electrically conducting biofluid flows with Ohmic heating have many biomedical and industrial applications. The purpose of this study is to provide the significance of the effects of Ohmic heating and viscous dissipation on electrically conducting Casson nanofluid flow driven by peristaltic pumping through a vertical porous channel.

In this analysis, the non-Newtonian properties of fluid will be characterized by the Casson fluid model. The long wavelength approach reduces the complexity of the governing system of coupled partial differential equations with non-linear components. Using a regular perturbation approach, the solutions for the flow quantities are established. The fascinating and essential characteristics of flow parameters such as the thermal Grashof number, nanoparticle Grashof number, magnetic parameter, Brinkmann number, permeability parameter, Reynolds number, Casson fluid parameter, thermophoresis parameter and Brownian movement parameter on the convective peristaltic pumping are presented and thoroughly addressed. Furthermore, the phenomenon of trapping is illustrated visually.

The findings indicate that intensifying the permeability and Casson fluid parameters boosts the temperature distribution. It is observed that the velocity profile is elevated by enhancing the thermal Grashof number and perturbation parameter, whereas it reduces as a function of the magnetic parameter and Reynolds number. Moreover, trapped bolus size upsurges for greater values of nanoparticle Grashof number and magnetic parameter.

There are some interesting studies in the literature to explain the nature of the peristaltic flow of non-Newtonian nanofluids under various assumptions. It is observed that there is no study in the literature as investigated in this paper.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.

This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments.

A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms.

The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments.

This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio.

Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing.

This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

With the upgrade of natural language interaction technology, the simulation extension of intelligent voice assistants (IVAs) and the uncertainty of products and services have received more and more attention. However, most of the existing research focuses on investigating the application of theories to explain consumer behavior related to intention to use and adopt IVAs, while ignoring the impact of its privacy issues on consumer resistance. This article especially examines the negative impact of artificial intelligence-based IVAs’ privacy concerns on consumer resistance, and studies the mediating effect of perceived creepiness in the context of privacy cynicism and privacy paradox and the moderating effect of anthropomorphized roles of IVAs and perceived corporate social responsibility (CSR) of IVAs’ companies. The demographic variables are also included.

Based on the theory of human–computer interaction (HCI), this study addresses the consumer privacy concerns of IVAs, builds a model of the influence mechanism on consumer resistance, and then verifies the mediating effect of perceived creepiness and the moderating effect of anthropomorphized roles of IVAs and perceived CSR of IVAs companies. This research explores underlying mechanism with three experiments.

It turns out that consumers’ privacy concerns are related to their resistance to IVAs through perceived creepiness. The servant (vs. partner) anthropomorphized role of IVAs is likely to induce more privacy concerns and in turn higher resistance. At the same time, when the company’s CSR is perceived high, the impact of the concerns of IVAs’ privacy issues on consumer resistance will be weakened, and the intermediary mechanism of perceiving creepiness in HCI and anthropomorphism of new technology are further explained and verified. The differences between different age and gender are also revealed in the study.

The research conclusions have strategic reference significance for enterprises to build the design framework of IVAs and formulate the response strategy of IVAs’ privacy concerns. And it offers implications for researchers and closes the research gap of IVAs from the perspective of innovation resistance.

This quantitative study aims to examine the determinants that impact the behavioral intention to use mobile payment (m-payment) among Generation Z (Gen Z) customers in Indonesia.

The theoretical model comprises seven latent variables: effort expectancy, performance expectancy, social influence, facilitating conditions, promotional activities, perceived security and behavioral intention. In addition, the two moderating factors of education and gender are used to investigate the significant effect of the determinants on intention to adopt m-payment. This study obtained the final data size of 430 respondents. The data analysis is conducted using structural equation modeling.

The results substantiate the significance of promotional activities, perceived security, performance expectancy, effort expectancy and social influence, on the behavioral intention to accept m-payment systems. Gender is revealed to significantly moderate two constructs: social influence and promotional activities, on the m-payment usage intention. Meanwhile, education moderates the effect of perceived security on behavioral intention.

This research is expected to fill the gap because only a few studies discuss the determinants affecting m-payment usage in Indonesia, especially among Gen Z-ers. Furthermore, the new findings associated with the role of two moderating factors become important practical implications because most of the prior studies often ignore the moderating factors.

It is now well-established that good water quality is associated with economic prosperity, reduced incidence on public health and the good functioning of the various ecosystems found in our environment. Water contamination is mostly related to both diffused (agricultural lands and geologic rock degradations) and point sources of pollution. Mauritius has many water resources which depend solely on precipitation for their replenishment. Water parameters which are of relevance include total dissolved solids (TDS), temperature, pH, electrical conductivity, turbidity, dissolved oxygen, dissolved and particulate organic carbon and major cations and anions. The traditional methods of analysis for these parameters are mostly using electrical and optical methods (probes and sensors in the field), while chemical titrations, Flame AAS and High-Performance Liquid Chromatography techniques are carried out in the laboratory. Image Classification techniques using neural networks can also be used to detect the presence of contaminants in water. In addition to basic water quality parameters, the field sensors range have been extended to cover important major ions and can now be integrated with Artificial Intelligence (AI)-based models for the prediction of variations in water quality to better protect human health and the environment, reduce operation costs of water and wastewater treatment plant unit processes.