iRobot : conceptualising SERVBOT for humanoid social robots

Services are intangible in nature and, as a result, it is often difficult to measure the quality of the service. The service is usually delivered by a human to a human customer and the service literature shows SERVQUAL can be used to measure the quality of the service. However, the use of social robots during the pandemic is speeding up the process of employing social roots in frontline service settings. An extensive review of the literature shows there is a lack of an empirical model to assess the perceived service quality provided by a social robot. Furthermore, the social robot literature highlights key differences between human service and social robots. For example, scholars have highlighted the importance of entertainment and engagement in the adoption of social robots in the service industry. However, it is unclear whether the SERVQUAL dimensions are appropriate to measure social robots’ service quality. This master’s project will conceptualise the SERVBOT model to assess a social robot’s service quality. It identifies reliability, responsiveness, assurance, empathy, and entertainment as the five dimensions of SERVBOT. Further, the research will investigate how these five factors influence emotional and social engagement and intention to use the social robot in a concierge service setting. To conduct the research, a 2 x 1 (CONTROL vs SERVBOT) x (Concierge) between-subject experiment was undertaken and a total of 232 responses were collected for both stages. The results indicate that entertainment has a positive influence on emotional engagement when service is delivered by a human concierge. Further, assurance had a positive influence on social engagement when a human concierge provided the service. When a social robot concierge delivered the service, empathy and entertainment both influenced emotional engagement, and assurance and entertainment impacted social engagement favourably. For both CONTROL (human concierge) and SERVBOT (social robot concierge), emotional and social engagement had a significant influence on intentions to use. This study is the first to propose the SERVBOT model to measure social robots’ service quality. The model provides a theoretical underpinning on the key service quality dimensions of a social robot and gives scholars and managers a method to track the service quality of a social robot. The study also extends the literature by exploring the key factors that influence the use of social robots (i.e., emotional and social engagement)

Additive main effects and multiplicative interaction and yield stability index for genotype by environment analysis and wider adaptability in Barley

Genotype by environment interaction distorts genetic analysis, changes relative ranking of genotypes and a major obstruction for varietal release. AMMI model is a quick and relevant tool to judge environmental behaviour and genotypic stability in comparison to ANOVA, multiplicative model and linear regressions. We evaluated 19 barley genotypes grown at 08 diverse locations to identify discriminating environments and ideal genotypes with dynamic stability. In AMMI ANOVA, the locations and genotype by environment interaction exhibited 66% and 14.7% of the total variation. The initial first two principal components showed significant interaction with 36.0 and 28.4% variation, respectively. AMMI1 biplot showed that the environments Bawal, Ludhiana and Durgapura were high yielding with high IPCA1 scores and located far away from the biplot origin. However, in AMMI1and AMMI2 biplots the locations Hisar, Ludhiana, Karnal, Bathinda and Modipuram were found suitable with low IPCA2 scores. Yield stability index (YSI) was highly useful with ASV ranks and the genotypes DWRB150 and BH1013 and checks BH902, DWRUB52 and DWRB101 were selected for high grain yield and wider adaptability across the locations

The effectiveness of robot-enacted messages to reduce the consumption of high-sugar energy drinks

This exploratory study examines the effectiveness of social robots' ability to deliver advertising messages using different "appeals" in a business environment. Specifically, it explores the use of three types of message appeals in a human-robot interaction scenario: guilt, humour and non-emotional. The study extends past research in advertising by exploring whether messages communicated by social robots can impact consumers' behaviour. Using an experimental research design, the emotional-themed messages focus on the health-related properties of two fictitious energy drink brands. The findings show mixed results for humour and guilt messages. When the robot delivered a promotion message using humour, participants perceived it as being less manipulative. Participants who were exposed to humourous messages also demonstrated a significantly greater intent for future purchase decisions. However, guilt messages were more likely to persuade consumers to change their brand selection. This study contributes to the literature as it provides empirical evidence on the social robots' ability to deliver different advertising messages. It has practical implications for businesses as a growing number seek to employ humanoids to promote their services

A preliminary evaluation of mobile phone apps to curb alcohol consumption

Mobile apps have become increasingly prevalent in modern society, and persuasive technology has a broader market than ever. Mobile-based alcohol cessation apps can promote positive behaviour change in users and improve the overall health of our society. This research aimed to understand the various features users respond to and make design recommendations for alcohol cessation apps. This paper reports on three sources of feedback (user ratings, user reviews, MARS App Quality score) provided on 20 alcohol cessation apps in the Google Play Store. Our findings suggest that self-control type apps received much greater positive user reviews than motivational apps. In addition, this trend was not observed through numeric user ratings. We also speculate on design recommendations for apps that are meant to inhibit alcohol intake

Applicability of joint regression and biplot models for stability analysis in multi-environment barley (Hordeum vulgare) trials

GGE and AMMI biplot methods with Eberhart and Russell regression model were applied on the set of 18 barley (Hordeum vulgare L.) genotypes grown in 6 environments for quick and relevant method vis-a-vis to delineate genotype by environment interaction, stable genotypes and environmental discrimination. The average grain yield over the locations was depicted as 41.97 q/ha, which ranged from 31.82 (Karnal) to 55.52 q/ha (Bhatinda). The genotype DWRB 91 (47.51 q/ha) exhibited the highest grain yield followed by DWRB 121 (46.35 q/ha), DWRB 123 (46.04 q/ha) and DWRB 128 (44.70 q/ha) over the locations. In Eberhart and Russell model, the genotypes DWRB 124 and PL 880 were found suitable for favourable environments and DWRB 128 for poor environments. In AMMI analysis, IPCA 1 and IPCA 2 altogether captured 74.73% of the interaction mean squares, while in GGE biplot, PC 1 and PC 2 captured 36.51% and 26.44% interaction variation,respectively. The genotypes BH 992, DWRB 121, DWRB 123, RD 2897 and checks BH 902 and DWRB 91 were high yielding and as well as found stable in GGE and AMMI 1 biplot. The test environments Durgapura and Modipuram exhibited different niches, whereas, Hisar, Ludhiana, Bhatinda and Karnal were representative with better discriminating ability. Between biplot models applied, the GGE biplots were clear in visualization for polygon view, genotypic stability and environmental discrimination. The GGE method considered both G+GE for biplot generation and found most suitable for stability analysis

Breaking barriers: paving the path to sustainable fashion – insights for a greener apparel and textile sector

Purpose The apparel and textile sector poses a significant environmental challenge due to its substantial contribution to pollution in the form of air, water and soil pollution. To combat these issues, the adoption of sustainable practices is essential. This study aims to identify and analyse the barriers that hinder the progress of sustainability in the apparel and textile industry. By consulting experts in the field, critical barriers were identified and given special attention. Design/methodology/approach To achieve the research objective, an integrated approach involving Interpretive Structural Modelling (ISM) and fuzzy MICMAC decision-making techniques was employed. The results were further validated through the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. Findings The findings highlight that barrier related to clothing disposal, inadequate adaptation to modern technology, challenges affecting sector efficiency and issues related to fashion design are crucial in influencing the remaining six barriers. Based on the outcomes of the DEMATEL method, a comprehensive cause-and-effect diagram was constructed to gain a deeper understanding of these challenges. Practical implications This research provides valuable insights for policymakers and stakeholders in the apparel and textile industry. It offers a strategic framework to address and overcome sustainability barriers, promoting the development of a more environmentally responsible and resilient sector. Originality/value The purpose of this research is to conduct an in-depth investigation of the barriers apparel and textile sectors. It is feasible that both the management team and the medical experts who provide direct patient care could benefit from this research

INVESTIGATING THE ROLE OF PORTER DIAMOND DETERMINANTS FOR COMPETITIVENESS IN MSMEs

After the globalization of market the micro, small and medium enterprises (MSMEs) got numerous opportunities to work in integration with large-scale organizations. Competitive advantage plays a significant role in deciding how organizations can exploit theses opportunities. So, the aim of this paper is to measure the competitive advantage of MSMEs based upon the Porter's diamond model framework. A welldesigned questionnaire is used to collect data about the various determinants of the model. Based upon the frequency of responses, percent point score (PPS) of each casual variable was calculated. By reviewing the result of this study, it is observed that competitiveness among MSME's sectors is mostly affected by market value. As is indicated by maximum PPS score e.g. 68%, followed by highly educated personnel, production and process technology (62%), further study results indicate that there is a need to increase clusters i.e. related and supported industries as depicted by low score (PPS=49%)

Perceived service quality in HRI : applying the SERVBOT Framework

Services are intangible in nature and as a result, it is often difficult to measure the quality of the service. In the service literature, the service is usually delivered by a human to a human customer and the quality of the service is often evaluated using the SERVQUAL dimensions. An extensive review of the literature shows there is a lack of an empirical model to assess the perceived service quality provided by a social robot. Furthermore, the social robot literature highlights key differences between human service and social robots. For example, scholars have highlighted the importance of entertainment value and engagement in the adoption of social robots in the service industry. However, it is unclear whether the SERVQUAL dimensions are appropriate to measure social robot’s service quality. The paper proposes the SERVBOT model to assess a social robot’s service quality. It identifies, reliability, responsiveness, assurance, empathy, and entertainment as the five dimensions of SERVBOT. Further, the research will investigate how these five factors influence emotional engagement and future intentions to use the social robot in a concierge service setting. The model was tested using student sampling, and a total of 94 responses were collected for the study. The findings indicate empathy and entertainment value as key predictors of emotional engagement. Further, emotional engagement is a strong predictor of future intention to use a social robot in a service setting. This study is the first to propose the SERVBOT model to measure social robot’s service quality. The model provides a theoretical underpinning on the key service quality dimensions of a social robot and gives scholars and managers a method to track the service quality of a social robot. The study also extends on the literature by exploring the key factors that influence the use of social robots (i.e. emotional engagement)