2,531 research outputs found

    An Antitrust Analysis of the Case for Wireless Network Neutrality

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    The ongoing debate about possible implementation of regulatory rules requiring โ€œnetwork neutralityโ€ for wireless telecommunications services is inherently about whether to impose a prohibition on the ability of network operators to control their vertical relationships. Antitrust analysis is well suited to analyze whether a wireless network neutrality rule is socially beneficial. Implementing network neutrality rules would be akin to using a per se antitrust rule regarding vertical relationships instead of the rule of reason analysis typically applied to vertical relationships in antitrust. Per se rules are used to prevent actions that rarely, if ever, have any pro-competitive benefits, such as price-fixing agreements. Rule of reason analysis is used when there are potential efficiency gains from the actions under investigation. Some vertical practices of the wireless carriers, such as bandwidth restrictions, may appear to be anticompetitive, but may also have plausible efficiency justifications so should be judged under rule of reason analysis. Economic examination of the wireless industry shows significant competition between networks which reduces the concern about vertical relationships, but some areas that should be monitored by antitrust and regulatory authorities. We propose several regulatory changes that would likely increase wireless competition and lessen the perceived need for prophlactic network neutrality rules while at the same time allowing efficiency-enhancing vertical relationships.network neutrality, wireless internet, antitrust,

    The prevention of mobile phone theft: a case study of crime as pollution; rational choices and consumer demand.

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    This thesis makes two contributions to environmental criminology. The first contribution is a rational choice event model for mobile phone thieves. This is based on interviews with 40 mobile phone thieves. In addition, the deterrent effects of 23 designs of phone are assessed. Comparisons are made between the responses of offenders and non-offenders; and between experienced offenders and less experienced offenders. The results show that mobile phone thieves make discerning choices about which model of phone to steal at the point of theft. The factors affecting handset choice reflect Clarke s (1999) CRAVED characteristics. Mobile phone thieves are differentially deterred by a variety of design solutions, the most effective of which reduce the resale value of stolen handsets. In contrast with offenders, non-offenders are more easily deterred, and statistically significantly more deterred for five of the 23 designs presented in this thesis; do not appreciate the importance of resale value; and are not so aware of the possibilities for circumventing or neutralising security technology. The differences between offender and non-offender responses mean that offenders are arguably best placed to assess product use and misuse in the process of designing-out crime. The second contribution of this thesis is a Mobile Phone Theft Index which controls for phone availability in the absence of handset sales data. Mobile phone theft is arguably a form of pollution (Roman and Farrell, 2002) and can, therefore, be controlled using traditional pollution control instruments (Farrell and Roman, 2006). Informing the public of their risk of victimisation according to handset ownership would make security a marketable aspect of handset design, incentivising industry to decrease theft rates. Industry action to date shows evidence of obstructionism and pre-regulatory initiatives (Newman, 2004) meaning that a novel instrument such as the Index is necessary to alter the current status quo where industry costs UK society an estimated ยฃ1.2 billion per year (Mailley and Farrell, 2006)

    User Experience of Mobile Devices:Physical Form, Usability and Coolness

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    Definition of final crime risk assessment mechanism to measure the risk of theft of electronic products and proof them against theft

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    This report presents research conducted as part of a two-year European project (Project Marc) which aims to develop a mechanism to assess the risk of theft of electronic products and to take steps to make that mechanism operational. The view of the authors, reflected throughout this report, is that the task of developing such a tool is vital yet daunting. It is vital because of the need to build upon the gains made within other sectors and the need to seize the opportunity presented by the realisation that crime trends can be explained in terms of the supply of opportunities, that reducing the supply of opportunities will reduce crime and that these tasks are not the sole responsibility of the police. It is daunting because in spite of extensive evidence for the efficacy of well-designed and implemented opportunity reduction measures, the problem comes when the crime to be prevented (theft of electronic products) is widespread but not generally devastating to its victims and when opportunity reduction finds itself in tension with commercial interests. The report sets out the process of developing a crime risk assessment mechanism and the justification for pursuing the options taken. Initial consultation with a variety of stakeholders yielded the common view that the crime risk assessment mechanism presented must a) measure both risk and protection (ensuring that the two are commensurate), b) reflect the perspectives of those who would be tasked with implementing it and c) reflect the language of stakeholders from a variety of European states. Taking these views on board, the authors conducted an extensive consultation with stakeholders from four sectors (insurance, consumersโ€™ organisations, law enforcement and manufacturers of electronic products) from ten European member states. Participants were asked to rate a variety of electronic products in terms of both vulnerability and security and to explain the ratings they gave. Their responses were used to develop two checklists which incorporate a variety of factors, weighted according to the frequency with which they were expressed. The authors suggest that the crime vulnerability checklist developed measurement. The security measurement by checklist was concluded to be inappropriate, since it would lead to limited and unimaginative security, and a case-by-case assessment by domain experts is advocated, in the light of measured vulnerability. A two-pronged approach to rating of electronic products (and possibly services) is outlined based upon approaches already deployed in relation to food standards

    ๊ฐœ๋ฐœ ์ดˆ๊ธฐ ๋‹จ๊ณ„์—์„œ์˜ ์†Œ์…œ AI ๊ฐœ์ธ๋น„์„œ ํ‰๊ฐ€ ๋ชจ๋ธ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2022.2. ์œค๋ช…ํ™˜.This dissertation aims to propose a user evaluation model to evaluate social AI personal assistants in the early stage of product development. Due to the rapid development of personal devices, data generated from personal devices are increasing explosively, and various personal AI services and products using these data are being launched. However, compared to the interest in AI personal assistant products, its market is still immature. In this case, it is important to understand consumer expectations and perceptions deeply and develop a product that can satisfy them to spread the product and allow general consumers to easily accept the product promptly. Accordingly, this dissertation proposes and validates a user evaluation model that can be used in the early stage of product development. Prior to proposing this methodology, main characteristics of social AI personal assistants, the importance of user evaluation in the early stage of product development and the limitations of the existing user evaluation model were investigated in Chapter 2. Various technology acceptance models and evaluation models for social AI personal assistant products have been proposed, evaluation models that can be applied in the initial stage of product development were insufficient, however. Moreover, it was found that commonly used evaluation measures for assessment of hedonic value were much fewer compared to measures for utilitarian value. These were used as starting points of this dissertation. In Chapter 3, the evaluation measures used in previous studies related to social AI personal assistant were collected and carefully reviewed. Through systematic review of 40 studies, the evaluation measures used in the past and limitation of related research were investigated. As a result, it was found that it was not easy to develop a prototype for evaluation, so it was possible to make the most of the products that have already been commercialized. In addition, all evaluation items used in previous studies were collected and used as the basis for the evaluation model to be proposed later. As a result of the analysis, considering the purpose of the social AI personal assistant, the role as supporting the user emotionally through social interaction with the user is important, but it was found that the evaluation measures related to hedonic value that are commonly used were still insufficient. In Chapter 4, evaluation measures that can be used in the initial stage of product development for social AI personal assistant were selected. Selected evaluation measures were used to evaluate three types of social robots and relationship among evaluation factors were induced through this evaluation. A process was proposed to understand to various opinions related to social robots and to derive evaluation items, and a case study was conducted in which a total of 230 people evaluated three social robots concept images using the evaluation items finally selected through this process. As a result, it is shown that consumersโ€™ attitude toward products was built through the utilitarian dimension and the hedonic dimension. In addition, there is positive relationship between ease of use and utility in the utilitarian dimension, and among aesthetic pleasure, attractiveness of personality, affective value in the hedonic dimension. Moreover, it is confirmed that the evaluation model derived from this study showed superior explanatory power compared to the previously proposed technology acceptance model. In Chapter 5, the model was validated again by applying the evaluation measure and the relationship among evaluation factors derived in Chapter 4 to other products. 100 UX experts with expertise in the field of social AI personal assistants and 100 users who use the voice assistant service often, watched two concept videos of the voice assistant service to help users in the onboarding situation of mobile phones and evaluated these concepts. As a result of the evaluation, there is no significant difference in the evaluation results between the UX expert and the real user group, so the structural equation model analysis was conducted using all the data obtained from the UX expert and the real user group. As a result, results similar to those in Chapter 4 are obtained, and it is expected that the model could be generalized to social AI personal assistant products and applied for future research. This dissertation proposes evaluation measure and relationship among evaluation factors that can be applied when conducting user evaluation in the initial stage of social AI personal assistant development. In addition, case studies using social AI personal assistant products and services were conducted to validate it. With the findings of this study, it is expected that researchers who need to conduct user evaluation to clarify product concepts in the early stages of product development will be able to apply evaluation measures effectively. It is expected that the significance of this dissertation will become clearer if further research is conducted comparing the finished product of social AI personal assistants with the video type stimulus in the early stage of development.๋ณธ ๋…ผ๋ฌธ์€ ์ตœ๊ทผ ๋น ๋ฅด๊ฒŒ ๋ฐœ์ „ํ•˜๊ณ  ์žˆ๋Š” social AI personal assistant์˜ ๊ฐœ๋ฐœ ์ดˆ๊ธฐ ๋‹จ๊ณ„์— ํ™œ์šฉ ๊ฐ€๋Šฅํ•œ ์‚ฌ์šฉ์ž ํ‰๊ฐ€ ํ•ญ๋ชฉ์„ ๊ฐœ๋ฐœํ•˜๊ณ  ํ‰๊ฐ€ ํ•ญ๋ชฉ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๊ฒ€์ฆํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๊ฐœ์ธ ๋””๋ฐ”์ด์Šค์˜ ๋ฐœ๋‹ฌ๋กœ ์ธํ•ด, ๊ฐ ๋””๋ฐ”์ด์Šค์—์„œ ์ƒ์„ฑ๋˜๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ํญ๋ฐœ์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๊ณ , ์ด๋ฅผ ํ™œ์šฉํ•œ ๊ฐœ์ธ์šฉ AI ์„œ๋น„์Šค ๋ฐ ์ œํ’ˆ์ด ๋‹ค์–‘ํ•˜๊ฒŒ ์ œ์•ˆ๋˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ทธ ๊ด€์‹ฌ์— ๋น„ํ•ด, social AI personal assistant ์ œํ’ˆ์˜ ์‹ค์ œ ์‹œ์žฅ์€ ์•„์ง ์„ฑ์ˆ™ํ•˜์ง€ ์•Š์€ ๋‹จ๊ณ„์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ์ œํ’ˆ์„ ๋น ๋ฅด๊ฒŒ ํ™•์‚ฐ์‹œํ‚ค๊ณ  ์ผ๋ฐ˜ ์†Œ๋น„์ž๋“ค์ด ์‰ฝ๊ฒŒ ์ œํ’ˆ์„ ์ˆ˜์šฉํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š”, ์†Œ๋น„์ž์˜ ๊ธฐ๋Œ€์™€ ์ธ์‹์„ ์ถฉ๋ถ„ํžˆ ์ดํ•ดํ•˜๊ณ  ๊ทธ๋ฅผ ์ถฉ์กฑ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ์ œํ’ˆ์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์ด์— ๋”ฐ๋ผ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ œํ’ˆ ๊ฐœ๋ฐœ ์ดˆ๊ธฐ ๋‹จ๊ณ„์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์‚ฌ์šฉ์ž ํ‰๊ฐ€ ํ•ญ๋ชฉ์„ ์ œ์•ˆํ•˜๊ณ  ํ‰๊ฐ€ ํ•ญ๋ชฉ ๊ฐ„ ๊ด€๊ณ„๋ฅผ ๋„์ถœํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋จผ์ € 2์žฅ์—์„œ๋Š” social AI personal assistant์˜ ํŠน์ง•, ์ œํ’ˆ ๊ฐœ๋ฐœ ์ดˆ๊ธฐ ๋‹จ๊ณ„์—์„œ ์ด๋ฃจ์–ด์ง€๋Š” ์‚ฌ์šฉ์ž ํ‰๊ฐ€์˜ ์ค‘์š”์„ฑ ๋ฐ ๊ธฐ์กด ์‚ฌ์šฉ์ž ํ‰๊ฐ€ ๋ชจ๋ธ์˜ ํ•œ๊ณ„์ ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๊ธฐ์กด์— ๊ธฐ์ˆ  ์ˆ˜์šฉ ๋ชจ๋ธ ๋ฐ AI personal assistant ์ œํ’ˆ์˜ ํ‰๊ฐ€ ๋ชจ๋ธ๋“ค์ด ๋‹ค์–‘ํ•˜๊ฒŒ ์ œ์•ˆ๋˜์–ด ์™”์œผ๋‚˜, ์ œํ’ˆ ๊ฐœ๋ฐœ ์ดˆ๊ธฐ ๋‹จ๊ณ„์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํ‰๊ฐ€ ๋ชจ๋ธ์€ ๋ถ€์กฑํ•˜์˜€๊ณ , ์ œํ’ˆ ์ „๋ฐ˜์„ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋Š” ํ‰๊ฐ€ ๋ชจ๋ธ์˜ ๋ถ€์žฌ๋กœ ๋Œ€๋ถ€๋ถ„์˜ ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ๊ฐ€์ง€ ์ด์ƒ์˜ ํ‰๊ฐ€ ๋ชจ๋ธ์„ ๊ฒฐํ•ฉ, ์ˆ˜์ •ํ•˜์—ฌ ์‚ฌ์šฉํ•œ ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. 3์žฅ์—์„œ๋Š” AI personal assistant ๊ด€๋ จ ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ํ™œ์šฉ๋œ ํ‰๊ฐ€ ํ•ญ๋ชฉ์„ ๊ฒ€ํ† ํ•˜์˜€๋‹ค. ์ด 40๊ฐœ์˜ ์—ฐ๊ตฌ๋ฅผ ๋ฆฌ๋ทฐํ•˜์—ฌ, ๊ธฐ์กด์— ํ™œ์šฉ๋˜๊ณ  ์žˆ๋Š” ํ‰๊ฐ€ ํ•ญ๋ชฉ์˜ ์ข…๋ฅ˜ ๋ฐ ํ•œ๊ณ„์ ์„ ์•Œ์•„๋ณด์•˜๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ํ”„๋กœํ† ํƒ€์ž… ๊ฐœ๋ฐœ์ด ์‰ฝ์ง€ ์•Š๊ธฐ์— ์ด๋ฏธ ์ƒ์šฉํ™”๋œ ์ œํ’ˆ๋“ค์„ ์ตœ๋Œ€ํ•œ ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ์ œํ’ˆ ์ „๋ฐ˜์„ ํ‰๊ฐ€ํ•œ ์‚ฌ๋ก€๋Š” ๋ถ€์กฑํ•จ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์ด ์‚ฌ์šฉํ•œ ํ‰๊ฐ€ ํ•ญ๋ชฉ์„ ๋ชจ๋‘ ์ˆ˜์ง‘ ๋ฐ ์ •๋ฆฌํ•˜์—ฌ ์ดํ›„ ์ œ์•ˆํ•  ํ‰๊ฐ€ ๋ชจ๋ธ์˜ ๊ธฐ๋ฐ˜ ์ž๋ฃŒ๋กœ ํ™œ์šฉํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, social AI personal assistant์˜ ๋ชฉ์ ์„ ๊ณ ๋ คํ•ด๋ณด์•˜์„ ๋•Œ, ์‚ฌ์šฉ์ž์™€์˜ ์‚ฌํšŒ์  ์ธํ„ฐ๋ž™์…˜์„ ํ†ตํ•ด ์‚ฌ์šฉ์ž์˜ ๊ฐ์ •์ ์ธ ๋ฉด์„ ์ฑ„์›Œ์ฃผ๋Š” ์—ญํ• ์ด ์ค‘์š”ํ•˜์ง€๋งŒ, ๊ณตํ†ต์ ์œผ๋กœ ํ™œ์šฉํ•˜๊ณ  ์žˆ๋Š” ๊ฐ์ •์  ๊ฐ€์น˜ ๊ด€๋ จ ํ‰๊ฐ€ ํ•ญ๋ชฉ์ด ๋ถ€์กฑํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. 4์žฅ์—์„œ๋Š” social AI personal assistant ์ œํ’ˆ ๊ฐœ๋ฐœ ์ดˆ๊ธฐ ๋‹จ๊ณ„์—์„œ ํ™œ์šฉ ๊ฐ€๋Šฅํ•œ ํ‰๊ฐ€ ํ•ญ๋ชฉ์„ ์ˆ˜์ง‘ ๋ฐ ์ œ์•ˆํ•˜๊ณ , ํ‰๊ฐ€ ํ•ญ๋ชฉ์„ ํ™œ์šฉํ•˜์—ฌ social robots์„ ํ‰๊ฐ€ํ•œ ๋’ค ์ด๋ฅผ ํ†ตํ•ด ํ‰๊ฐ€ ํ•ญ๋ชฉ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. Social robots ๊ด€๋ จ ์˜๊ฒฌ์„ ๋‹ค์–‘ํ•˜๊ฒŒ ์ฒญ์ทจํ•˜๊ณ  ํ‰๊ฐ€ ํ•ญ๋ชฉ์„ ๋„์ถœํ•˜๋Š” ํ”„๋กœ์„ธ์Šค๋ฅผ ์ œ์•ˆํ•˜์˜€์œผ๋ฉฐ, ๋ณธ ํ”„๋กœ์„ธ์Šค๋ฅผ ํ†ตํ•ด ์ตœ์ข… ์„ ์ •๋œ ํ‰๊ฐ€ ํ•ญ๋ชฉ์„ ์ด์šฉํ•˜์—ฌ, ์ด 230๋ช…์ด ์„ธ ๊ฐ€์ง€ social robots ์ปจ์…‰ ์˜์ƒ์„ ํ‰๊ฐ€ํ•˜๋Š” ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ํ‰๊ฐ€ ๊ฒฐ๊ณผ, ์ œํ’ˆ์— ๋Œ€ํ•œ ์†Œ๋น„์ž ํƒœ๋„๋Š” Utilitarian dimension๊ณผ Hedonic dimension์„ ํ†ตํ•ด ํ˜•์„ฑ๋˜์—ˆ๊ณ , Utilitarian dimension ๋‚ด ์‚ฌ์šฉ์„ฑ ๋ฐ ์ œํ’ˆ ํšจ์šฉ์„ฑ, Hedonic dimension์— ํฌํ•จ๋˜๋Š” ์‹ฌ๋ฏธ์  ๋งŒ์กฑ๋„, ์„ฑ๊ฒฉ์˜ ๋งค๋ ฅ๋„, ๊ฐ์„ฑ์  ๊ฐ€์น˜ ๊ฐ๊ฐ์€ ์„œ๋กœ ๊ธ์ •์ ์ธ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ์ง€๋‹˜์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ๊ธฐ์กด์— ์ œ์•ˆ๋œ ๊ธฐ์ˆ  ์ˆ˜์šฉ ๋ชจ๋ธ ๋Œ€๋น„ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋„์ถœํ•œ ํ‰๊ฐ€ ๋ชจ๋ธ์ด ์šฐ์ˆ˜ํ•œ ์„ค๋ช…๋ ฅ์„ ๋ณด์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. 5์žฅ์—์„œ๋Š” 4์žฅ์—์„œ ๋„์ถœ๋œ ํ‰๊ฐ€ ๋ชจ๋ธ์„ ํƒ€ ์ œํ’ˆ์— ์ ์šฉํ•˜์—ฌ ๋ชจ๋ธ์„ ๋‹ค์‹œ ํ•œ๋ฒˆ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ํ•ด๋‹น ๋ถ„์•ผ์— ์ „๋ฌธ์„ฑ์„ ์ง€๋‹Œ UX ์ „๋ฌธ๊ฐ€ 100๋ช… ๋ฐ ์Œ์„ฑ ๋น„์„œ ์„œ๋น„์Šค๋ฅผ ์‹ค์ œ ์‚ฌ์šฉํ•˜๋Š” ์‹ค์‚ฌ์šฉ์ž 100๋ช…์ด, ํœด๋Œ€ํฐ ์˜จ๋ณด๋”ฉ ์ƒํ™ฉ์—์„œ ์‚ฌ์šฉ์ž๋ฅผ ๋„์™€์ฃผ๋Š” ์Œ์„ฑ ๋น„์„œ ์„œ๋น„์Šค์˜ ์ปจ์…‰ ์˜์ƒ ๋‘ ๊ฐ€์ง€๋ฅผ ๋ณด๊ณ  ์ปจ์…‰์— ๋Œ€ํ•œ ํ‰๊ฐ€๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ํ‰๊ฐ€ ๊ฒฐ๊ณผ UX ์ „๋ฌธ๊ฐ€์™€ ์‹ค์‚ฌ์šฉ์ž ๊ทธ๋ฃน ๊ฐ„์—๋Š” ํ‰๊ฐ€ ๊ฒฐ๊ณผ์— ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๊ธฐ ๋•Œ๋ฌธ์—, UX ์ „๋ฌธ๊ฐ€์™€ ์‹ค์‚ฌ์šฉ์ž ๊ทธ๋ฃน์—์„œ ์–ป์€ ๋ฐ์ดํ„ฐ ์ „์ฒด๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ตฌ์กฐ ๋ฐฉ์ •์‹ ๋ชจ๋ธ ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ 5์žฅ๊ณผ ์œ ์‚ฌํ•œ ์ˆ˜์ค€์˜ ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ๊ณ , ์ถ”ํ›„ ํ•ด๋‹น ๋ชจ๋ธ์„ social AI personal assistant ์ œํ’ˆ์— ์ผ๋ฐ˜ํ™”ํ•˜์—ฌ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ํŒ๋‹จํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ social AI personal assistant ๊ด€๋ จ ์ œํ’ˆ ๋ฐ ์„œ๋น„์Šค์˜ ๊ฐœ๋ฐœ ์ดˆ๊ธฐ ๋‹จ๊ณ„์—์„œ ์‚ฌ์šฉ์ž ํ‰๊ฐ€๋ฅผ ์ง„ํ–‰ํ•  ๋•Œ ํ™œ์šฉ ๊ฐ€๋Šฅํ•œ ํ‰๊ฐ€ ํ•ญ๋ชฉ ๋ฐ ํ‰๊ฐ€ ํ•ญ๋ชฉ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ด๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ social AI personal assistant ์ œํ’ˆ ๋ฐ ์„œ๋น„์Šค๋ฅผ ํ™œ์šฉํ•œ ์‚ฌ๋ก€์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ถ”ํ›„ ์ œํ’ˆ ๊ฐœ๋ฐœ ์ดˆ๊ธฐ ๋‹จ๊ณ„์—์„œ ์ œํ’ˆ์˜ ์ปจ์…‰์„ ๋ช…ํ™•ํžˆ ํ•˜๊ธฐ ์œ„ํ•œ ์‚ฌ์šฉ์ž ํ‰๊ฐ€๋ฅผ ์‹ค์‹œํ•ด์•ผ ํ•˜๋Š” ์—ฐ๊ตฌ์ง„์ด ํšจ์œจ์ ์œผ๋กœ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ์ถ”ํ›„ ์ด ๋ถ€๋ถ„์˜ ๊ฒ€์ฆ์„ ์œ„ํ•ด, social AI personal assistants์˜ ์™„์ œํ’ˆ๊ณผ ๊ฐœ๋ฐœ ์ดˆ๊ธฐ ๋‹จ๊ณ„์˜ video type stimulus๋ฅผ ๋น„๊ตํ•˜๋Š” ์ถ”๊ฐ€ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์ง„๋‹ค๋ฉด ๋ณธ ์—ฐ๊ตฌ์˜ ์˜๋ฏธ๋ฅผ ๋ณด๋‹ค ๋ช…ํ™•ํ•˜๊ฒŒ ์ œ์‹œํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.1 Research objectives 5 1.2 Dissertation outline 7 Chapter 2 Literature review 9 2.1 Social AI personal assistant 9 2.2 User centered design process 13 2.3 Technology acceptance models 16 2.4 Evaluation measures for social AI personal assistant 22 2.5 Existing evaluation methodologies for social AI personal assistant 27 Chapter 3 Collection of existing evaluation measures for social AI personal assistants 40 3.1 Background 40 3.2 Methodology 43 3.3 Result 51 3.4 Discussion 60 Chapter 4 Development of an evaluation model for social AI personal assistants 63 4.1 Background 63 4.2 Methodology 66 4.2.1 Developing evaluation measures for social AI personal assistants 68 4.2.2 Conducting user evaluation for social robots 74 4.3 Result 77 4.3.1 Descriptive statistics 77 4.3.2 Hypothesis development and testing 80 4.3.3 Comparison with existing technology acceptance models 88 4.4 Discussion 93 Chapter 5 Verification of an evaluation model with voice assistant services 95 5.1 Background 95 5.2 Methodology 98 5.2.1 Design of evaluation questionnaires for voice assistant services 99 5.2.2 Validation of relationship among evaluation factors 103 5.3 Result 108 5.3.1 Descriptive statistics 108 5.3.2 Hypothesis development and testing 111 5.3.3 Comparison with existing technology acceptance models 118 5.4 Discussion 121 Chapter 6 Conclusion 124 6.1 Summary of this study 124 6.2 Contribution of this study 126 6.3 Limitation and future work 128 Bibliography 129 Appendix A. Evaluation measures for social AI personal assistant collected in Chapter 4 146 Appendix B. Questionnaires for evaluation of social robots 154 Appendix C. Questionnaires for evaluation of voice assistant service 166๋ฐ•

    An Investigation of the Applicability of the Uses and Gratifications Theory for Providing Insight into e-Touristsโ€™ Use of Smartphones

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    Despite the previous smartphone research in the context of travel and tourism, there is limited research based on a strong theoretical background that seeks to understand how tourists are motivated and satisfied via smartphone use. This study extended previous studies by systematically investigating and quantitatively measuring how and to what extent tourists are gratified (satisfied) by the use of smartphones during their trips based on the Uses and Gratifications Theory. According to this theory, individuals choose a media platform with the anticipation that it will aid them in realizing a specific intention, the satisfaction of this need being referred to as gratification (Green 2014; Logan, 2017; Stacks & Salwen, 2009). This study investigated four constructs in terms of antecedents (i.e., motivations of using smartphones by tourists) and consequences (i.e., satisfaction with smartphones use by tourists, satisfaction referred to as gratifications). This study adopted the Uses and Gratifications Theory as a theoretical framework to explore the use of smartphones by tourists and to measure quantitatively their touristic satisfaction. U&G motivations (Social Interaction, Entertainment, Convenience, and Information) and hypotheses were developed. The respondents of the main study were tourists traveling in downtown Greenville, South Carolina, who have experiences using smartphones at the destination. To test the model for the study, a multilevel analysis (multilevel SEM) was employed to avoid statistical biases caused by common traits within group tourists and to measure potential group effects. This study also analyzed multilevel mediation in the structural equation model. It was hypothesized that the attitude construct mediates the relationship between motivations of using smartphones by tourists (independent variable or predictors) and satisfactions with smartphones use by tourists (dependent variable) in the structural model. Moreover, the relationships among constructs were tested and examined based on the theoretical background developed through a review of the literature. This study provides a classification of motivations of using smartphone use by tourists (U&G motivations) and a newly developed scale to measure satisfaction with smartphone use by tourists and their experiences, and thus it may enhance deeper our understanding of motivations of using smartphone by tourists, attitude toward the smartphone use by tourists and satisfactions with smartphone use by tourists. This study addressed specific aspects of tourism experiences. The results suggest that U&G motivations have a significant effect on touristsโ€™ attitude toward smartphone use, which, in turn, significantly affects e-tourist satisfaction at the individual level. However, there was no group effect among U&G motivations, the attitude toward smartphone use and e-tourist satisfaction. Based on the results from this study, the most important reason that tourists used their smartphones was to obtain information during their trips to Greenville, SC. The results of this study provide practical and theoretical implications for e- tourism communication and tourism marketing

    Assessing the Lead Market Potential of Countries for Innovation Projects

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    This paper presents an approach to assessing the potential of countries to increase the likelihood that locally preferred innovation designs become successful in other countries, too. The concept suggests that for many innovations lead markets exist that initiate the international diffusion of a specific design of an innovation. Once a specific innovation design has been adopted by users in the lead market chances are that it subsequently becomes adopted by users in other countries as well. Lead markets can be utilised for the development of global innovation designs. By focusing on the design of the innovation which responds to the preferences within the lead market, a company can leverage the success experienced in the lead market for global market launch. In order to follow a lead market strategy of new product development, it is necessary to assess the lead market potential of countries before an innovation is developed and tested in the market. This paper presents an indicator-based methodology that approximates the lead market attributes of countries. This assessment methodology was applied to two innovation projects at the truck division of DaimlerChrysler AG. The method produces information that is of importance for the development phase and the market launch of globally standardised innovations.Innovation, Global Diffusion, Market Entry

    External evaluation of mobile phone technology-based nutrition and agriculture advisory services in Africa and South Asia

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    The GSM Association (GSMA), working with a wide range of mobile network operators and civil society organisations, is launching a series of nutrition-focused m-health and m-agriculture initiatives in South Asia and sub-Saharan Africa. GSMA refers to nutrition-enhanced initiatives collectively as โ€˜m-nutritionโ€™. This report summarises the plans for an impact evaluation of two of these nutrition-enhanced initiatives: mHealth in Tanzania and mAgri in Ghana. The evaluation consists of three integrated components: a quantitative impact evaluation, a qualitative evaluation focusing on implementation fidelity, pathways of impact and external validity, and an evaluation of the sustainability of the business model behind the mNutrition initiative. The business model evaluation compares the two initiatives described above with a third, mHealth in Ghana, which is closer to the GSMA core commercial model, and additionally, possibly to retain a view on Bangladesh, mAgri to generate more heterogeneity in conclusions.Department for International Development (DFID
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