Search results

The notion of luxury has changed due to the democratization and the fast adoption pace of intangible luxury service providers embedding voice-controlled assistants. In particular, Amazon’s Echo Look is expected to further democratize luxury services and consumer experience; indeed, this camera-based device acts as a personal consultant as it provides fashion advices based on the pictures of its users. Nonetheless, as the Echo Look proliferates the market, concerns around its potential disruption to the marketplace alongside usage-related issues, such as privacy, will arise. The paper aims to discuss these issues.

The research adopted an exploratory approach using elite interviewing. The sample included 17 experts in the fields of digital marketing, fashion consulting, information technology security and retailing.

The study pioneers the discussion on how interactive voice-activated assistant (IVA) devices can potentially outweigh concerns around safety and privacy through providing luxury-like personalized services. To that end, the Echo Look device was taken as the key example based on the unique features and benefits it provides to its users, namely, in being perceived as a personal fashion consultant.

The literature on intangible luxury based on IVA devices is still nascent. This study fills a considerable gap in the literature related to retailers’ technological innovations aimed at democratizing luxury-like personalized experiences.

Austrian economics figures centrally in organizational entrepreneurship research. However, researchers have focussed almost entirely on the Austrian school's “gales of creative destruction” and “entrepreneurial discovery” metaphors, which are rooted in equilibrium assumptions and thus downplay the more subjective and dynamic aspects of entrepreneurship. The purpose of this paper is to question such assumptions, proposing instead a “kaleidic” metaphor drawn from the radical subjectivist strand of Austrian economics. The paper develops, grounds, and enriches the theoretical concepts this metaphor embodies in order to advance the general understanding of entrepreneurship as a radically subjective, disequilibrium phenomenon, as well as the specific knowledge of entrepreneurs’ career and venture experiences. In doing so, the paper highlights creative imagination as a wellspring of entrepreneurship.

The paper employs a case study design to inductively develop the theoretical concepts embodied in the kaleidic metaphor and deductively ground them in the accounts 12 entrepreneurs provided about their career and venture experiences. The paper employs symbolist methods to develop thicker descriptions, generate alternative understandings, and facilitate richer interpretations. Moreover, the paper adopts a reflexive approach in considering the study's implications.

The results suggest the kaleidic metaphor comprises five overarching ideas that resonate, often very strongly, with entrepreneurs.

The study is the first to theoretically develop and empirically ground the ideas the kaleidic metaphor embodies. The paper contributes to a growing body of conceptual work and joins a handful of empirical studies by organizational entrepreneurship scholars using the radical Austrian perspective.

This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.

Managing risk and making decisions presents an increasing challenge to doctors as they are encouraged to adopt a partnership approach with patients to dealing with risk, within a “risk society” constructed around individuality, uncertainty, blame and responsibility. In‐depth interviews, stimulated by clinical vignettes, were used to explore the key position of doctors within this risk society. Analysis, sensitised through contemporary texts, revealed unexpected findings that portrayed doctors as reflexive jugglers of risk. Discourses in this study revealed indecision and uncertainty, balanced against needs to preserve professional roles and engage patients in addressing risk, whilst preventing widespread harm and conflict. In concluding, the alternative approaches to risk with older people will suggest a more trusting and positive process that presents a real opportunity for truly sharing risk and decisions that benefit both doctor and patient.

Discusses the reconfiguration of a CD‐ROM workstation for use with the Census Test Disc in addition to the ERIC database. Lists the new batch files to transfer users from one system to another, and the new menu and user instructions installed.

Digital technologies have made it possible for organizations to enhance service delivery and provide consumers a way to experience a product or service before even seeing it in person. Prior work has validated consumer purchase decision-making models like the Engel, Kollat and Blackwell (EKB) model in digital and multi-channel purchase environments. This research aims to explore the various impacts of digital technologies, specifically virtual reality (VR) and augmented reality (AR) features, on the different stages of the EKB model in a high involvement purchase decision context. In addition, the use of such technologies is examined as a competitive advantage for sales agents.

An in-depth market analysis of VR and AR technologies related to residential real estate is conducted. Then the research questions are analyzed through detailed feedback gathered from 33 prospective home-buyers and realtors in the USA.

Insights from buyers and sales agents (realtors in the context of this research) suggest that VR and AR technologies can enhance consumer information search and expedite the time consumers spend evaluating purchase alternatives. As these technologies advance in their customization features, they may also increase the conversion between alternative evaluation and purchase, but only if they are realistic and provide high quality experiences. Finally, digital technologies are viewed as becoming a necessity in high involvement sales contexts, suggesting that additional features or innovative ways to use these technologies may serve as sources of competitive advantage for sales agents.

This is one of the first research studies to explore the perceived impacts of VR and AR technologies on the stages of the EKB model consumer decision model. It builds on prior work and offers direction for future research.

WHEN ARTHUR Brown goes into a company, it's with his business instincts sharpened and his ears pinned back. He is on the look‐out for the balance sheet which doesn't quite add up, the production process which doesn't seem to be working as well as it should, and listening for the off‐the‐cuff comments from shopfloor and boardroom.

The purpose of this study is to examine the new normal within a continuum of three types of disruption, each of varying duration. References to the new normal draw attention to the periodic and rising importance of different levels, types, and consequences of game-changing disruption for those in governance roles.

In this conceptual research, given the discussion of a return to normalcy near the expected end of the COVID-19 pandemic, the authors organize the literature on disruption in governance into a disruption continuum – emergency, crisis and super crisis – to demonstrate the differences in each type of disruption to establish a distinct view of the new normal.

Within the three types of disruption, the first two suit the rational authority model in which disruption is turned over to those in governance roles. However, the rational authority model comes under attack in the super crisis and is increasingly associated with the post-truth era.

In Type 3 disruptions or super crises, the failure of those in control to set the parameters of the new normal raises concerns that the center no longer holds, and as a result, the assumption of an attentive public splinter into multiple contending publics, each with its version of data, facts and images.

The new normal is typically treated after the result of a black swan or rare and surprising long-lived disruption. In this work, the formulation of the recurrence, ubiquity and controversy engendered by super crises suggests that it is one of the features attenuating and giving rise to fractious incivility in the post-truth era.

This paper aims to acquaint readers with Louis J. DeLamarter and the innovative strategies he used to extend the profitability of the street railway in the USA.

Initially the researchers were interested in street railways (trolleys) and why they had flourished in Grand Rapid, Michigan when they were failing in other parts of the USA. Extensive research in local and national newspapers, business journals and trade publications of the time led to the story of Louis J. DeLamarter. His innovative approach to running the street railway in Grand Rapids MI emerged as the cause of the Grand Rapids Railway Company's unique, though temporary, success.

DeLamarter conceived and implemented a number of innovative marketing practices which were rooted in a differentiation strategy: he focused on the customer whereas other transportation companies did not. His strategy made the company successful after others became bankrupt. His ideas were lauded in the industry, adopted, and are still used in public transportation today. Only external factors such as the depression, the advent of the automobile and burdensome regulations undermined his success.

Despite extensive searches, no original papers by DeLamarter could be located.

Public service companies worldwide can learn about the importance of customer focus from DeLamarter's work. Additionally, managers can learn that even the most successful innovative strategy will only yield a short‐term competitive advantage if the industry faces unfavorable external factors.

The story of Louis J. DeLamarter and his innovations in public transportation are presented for the first time.

The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert.

This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models’ performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion.

The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable.

The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.