Chinese consumers’ conspicuous perspectives: the context of smartphone purchase behavior

This study investigated the key antecedent factors of Chinese consumers attitudes towards smartphones, focusing on the conspicuous value. An analytical framework with three key themes in the smartphone purchasing attributes in relation to the conspicuous attitudes was validated based on Structural Equation Modelling (SEM) using 426 survey data collected in China. It has been found that ‘conspicuous value’ was the basic perception which has a relation with three key factors, ‘fashionableness’ and ‘innovativeness’, and ‘instore shopping atmosphere’ in smartphone purchasing attributes. This implies that the analytical framework developed from this study is applicable to the research topic as a useful analytical tool kit. From the empirical study based on this framework, it has been found that only ‘fashionableness’ has the significant impact on their purchase intention, whereas, ‘innovativeness’ and ‘instore shopping atmosphere' did not have a significant impact on their purchase intention of smartphones. Chinese consumer behavior from the cultural context has attracted researchers so far, however, the practical and feasible analytical framework covering the cultural aspect and smartphone attributes has been lacked. This study proposed a practical analytical framework with the Chinese cultural value ‘conspicuousness’ and focus on the smartphone shopping attributes. Moreover, the empirical research outcome with the survey data based on the proposed framework can provide actionable implications for the relevant marketers and researchers

WILL YOU CARRY THAT WATCH? INVESTIGATING FACTORS THAT AFFECT CONTINUANCE INTENTION OF SMARTWATCHES

The interest in wearable technologies, especially smartwatches rise day by day parallel with technological developments and an increasing need to monitor health. In line with those developments, this study aims to investigate the role of perceived ease of use, perceived usefulness, user satisfaction, healthology in explaining smartwatch continuance intention. In addition, this study investigates the relationships between perceived ease of use, perceived usefulness, healthology and user satisfaction. Questionnaire method was used to gather data from actual smartwatch consumers in Turkey and the data analyzed by utilizing structural equation modeling. Findings demonstrate that the most powerful variable to explain smartwatch continuance intention is perceived usefulness, whereas perceived ease of use contributes to user satisfaction the most. Also, healthology is positively related to both user satisfaction and continuance intention. The results also highlight the importance of continuance intention to increase intention to recommend smartwatches to other people

Explicating Consumer Adoption Of Wearable Technologies: A Case Of Smartwatches From The Asean Perspective

This research aims to determine the key antecedent factors in consumers\u27 adoption of and their intention to recommend smartwatch wearable technology. The proposed research model combines the current technology acceptance and innovation diffusion theories with perceived aesthetic and perceived privacy risk to explain individuals\u27 smartwatch adoption and subsequent recommendation to other people. Based on a sample of 299 completed individual online surveys, the research employed partial least squares (a variance-based analysis method) for the model and hypotheses testing. The results showed some similarities as well as differences from the previous literature. The study found that performance expectancy, habit, and perceived aesthetic were the main predictors of smartwatch adoption. Compatibility was the antecedent factor of performance expectancy, and innovativeness directly influenced user adoption and effort expectancy. Consequently, user smartwatch adoption usually led to recommendation

User acceptance of smart watch for medical purposes : an empirical study

This study aims to investigate the most effective and interesting variables that urge use of the smartwatch (SW) in a medical environment. To achieve this aim, the study was framed using an innovative and integrated research model, which is based on combining constructs from a well-established theoretical model’s TAM and other features that are critical to the effectiveness of SW which are content richness and personal innovativeness. The Technology Acceptance Model (TAM) is used to detect the determinants affecting the adoption of SW. The current study depends on an online questionnaire that is composed of (20) items. The questionnaire is distributed among a group of doctors, nurses, and administration staff in medical centers within the UAE. The total number of respondents is (325). The collected data were implemented to test the study model and the proposed constructs and hypotheses depending on the Smart PLS Software. The results of the current study show that the main constructs in the model contribute differently to the acceptance of SW. Based on the previous assumption, content richness and innovativeness are critical factors that enrich the user’s perceived usefulness. In addition, perceived ease of use was significantly predictive of either perceived usefulness or behavioral intention. Overall findings suggest that SW is in high demand in the medical field and is used as a common channel among doctors and their patients and it facilitates the role of transmitting information among its users. The outcomes of the current study indicate the importance of certain external factors for the acceptance of the technology. The genuine value of this study lies in the fact that it is based on a conceptual framework that emphasizes the close relationship between the TAM constructs of perceived usefulness and perceived ease of use to the construct of content richness, and innovativeness. Finally, this study helps us recognize the embedded motives for using SW in a medical environment, where the main motive is to enhance and facilitate the effective roles of doctors and patients

Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis

The current study is based on an integrated research model developed by combining constructs from the Technology Acceptance Model (TAM) and other features affecting smartwatch effectiveness, such as content richness and user satisfaction (SAT). TAM is used to locate factors influencing the adoption of the smartwatch (ASW). Most importantly, the current study focuses on factors influencing smartwatch acceptance and use in the medical area, facilitating and enhancing the effective role of doctors and patients. The present study's conceptual framework examines the close association between two-term TAM variables of perceived ease of use (PEU) and perceived usefulness (PU) and the constructs of user satisfaction and content richness. It also incorporates the flow theory (EXP) to measure the effectiveness of the smartwatch. The study also uses the flow theory to assess involvement and control over ASW. The study used a sample of 489 respondents from the medical field, including doctors, nurses, and patients. The study employed a hybrid analysis method combining Structural Equation Modeling (SEM) and an Artificial Neural Network (ANN) based on deep learning. The study also used Importance-Performance Map Analysis (IPMA) to determine the relevance and performance of the variables influencing ASW. Based on the ANN and IPMA analyses, user satisfaction is the most crucial predictor of intention to use a medical smartwatch. Applying the structural equation model to the sample shows that SAT, PU, PEU, and EXP significantly influence intention to use a medical smartwatch. The study also revealed that content richness is an important factor that enhances users' PU. The current study could enable healthcare provider practitioners and decision-makers to identify factors for prioritisation and to strategise their policies accordingly. Methodologically, this study indicates that a “deep ANN architecture” can determine the non-linear associations between variables in the theoretical model. Overall, the study finds that smartwatches are in high demand in the medical field and are useful in information transmission between doctors and their patients

Prediction of the intention to use a smartwatch : a comparative approach using machine learning and partial least squares structural equation modeling

This study makes use of a cohesive yet innovative research model to identify the determinants of the adoption of smart watches using constructs from the Technology Acceptance Model (TAM) and constructs of smartwatches, including effectiveness, content richness, and personal innovativeness. The chief objective of the study was to encourage the use of smartwatches for medical purposes so that the role of doctors can be made more effective and to facilitate access to patient records. Our conceptual framework highlights the association of TAM constructs (i.e., perceived usefulness and perceived ease of use) with the content richness, the construct of user satisfaction, and innovativeness. To measure the effectiveness of the smartwatch, an external factor based on the flow theory was added, which emphasizes the control over the smartwatch and the degree of involvement. The study employs data from 385 respondents involved in the field of medicine, such as doctors, patients, and nurses. The data were gathered through a survey and used for evaluation of the research model using partial least squares structural equation modeling (PLS-SEM) and machine learning (ML) models. The significance and performance of factors impacting THE adoption of smartwatches were also identified using Importance-Performance Map Analysis (IPMA). User satisfaction is the most important predictor of intention to adopt a medical smartwatch according to the ML and IPMA analyses. The fitting of the structural equation model to the sample showed a high dependence of user satisfaction on perceived usefulness and perceived ease of use. Furthermore, two critical factors, innovativeness and content richness, are demonstrated to enhance perceived usefulness. However, one should consider that perceived usefulness or behavioral intention could not be determined based on perceived ease of use. In general, the findings suggest that smartwatch usage could become critically important in the medical field as a mediator that allows doctors, patients, and other users to access essential information

Social media and knowledge integration based emergency response performance model

Emergency Response (ER) during the flood is increasingly being characterized as a complex phase in disaster management as it involves multi-organizational settings. This scenario causes miscommunication, lack of coordination and difficulty in making life-saving decisions, which decreases organisational performance. Accordingly, Knowledge Integration (KI) can reduce and resolve problems of coordination and communications which lead to decisions being made at a proper time, thereby increasing the task of Non- Government Organisations (NGOs)’ capabilities to achieve better performance. Moreover, use of Social Media (SM) provides many advantages that may assist in eliminating KI’s challenges and enhancing its dissemination at low cost, particularly for NGOs that work in disparate places. Despite this, current research into the improvement of task performance using KI through SM in the emergency response context is, unfortunately, limited. Most of the studies are not empirical and there is a lack of theoretical foundation for improving task performance using KI, in addition to using SM to facilitate KI in the flood disaster ER. Hence, it is important to address these issues. The main objective of this study is to identify the factors that influence the Emergency Response Task Performance (ERTP). In this research, the factors which affect the performance of ER tasks were elicited through a review of the literature to identify the essential factors influential NGOs’ emergency response. Then, this study developed an ERTP model by combining Knowledge-Based Theory (KBT) of the firm and the Task-Technology Fit (TTF) theory, used to utilise technology. This study applied a quantitative approach to examine these factors. Based on purposive sampling, questionnaires were distributed to over 700 staff and volunteers working for 12 NGOs in Sudan. Smart PLS 2.0 M3 and IBM SPSS Statistics version 24 were used to analyse the data. The results revealed that KI is a significant factor related to ERTP. In addition, it was found that the SM usage factor was significantly related to KI. Furthermore, this study discovered significant differences among the various experiences of volunteers and staff when it comes to utilising SM for knowledge integration in the context of ER response. The results of the study contribute to the body of knowledge by providing a model for ER managers, team members in NGOs and decision-makers to use it as a guideline for successfully assessing and validating ERTP. Additionally, it sets guidelines that may be useful for NGOs in the effective use of social media as a platform for integrating knowledge. Finally, this study provides recommendations to flood decision-makers who are considering enhancing the performance of the tasks within their organisations

Probabilistic Algorithms, Lean Methodology Techniques, and Cell Optimization Results

There is a significant technology deficiency within the U.S. manufacturing industry compared to other countries. To adequately compete in the global market, lean manufacturing organizations in the United States need to look beyond their traditional methods of evaluating their processes to optimize their assembly cells for efficiency. Utilizing the task-technology fit theory this quantitative correlational study examined the relationships among software using probabilistic algorithms, lean methodology techniques, and manufacturer cell optimization results. Participants consisted of individuals performing the role of the systems analyst within a manufacturing organization using lean methodologies in the Southwestern United States. Data were collected from 118 responses from systems analysts through a survey instrument, which was an integration of two instruments with proven reliability. Multiple regression analysis revealed significant positive relationships among software using probabilistic algorithms, lean methodology, and cell optimization results. These findings may provide management with information regarding the skillsets required for systems analysts to implement software using probabilistic algorithms and lean manufacturing techniques to improve cell optimization results. The findings of this study may contribute to society through the potential to bring sustainable economic improvement to impoverished communities through the implementation of efficient manufacturing solutions with lower capital expenditures