Question: The effects of brand trust, customer satisfaction, and brand loyalty on word-of-mouth communication.

This document delves into the critical aspects of reporting structural equation modeling (SEM) analyses, aiming to provide readers with comprehensive and precise information. It is suggested that each report should thoroughly justify the selected model, consider potential alternative models, and ensure the model's identifiability. Attention should also be given to addressing issues of nonnormality and missing data. Reporting the full set of parameters along with their standard errors is encouraged, and it might be beneficial to include the correlation matrix and discrepancies, in addition to goodness-of-fit indices. This allows readers to independently assess and critically evaluate the model's fit. A review of recent studies demonstrates the current reporting practices in comparison to the recommended guidelines for SEM reporting.

The document emphasizes the importance of transparent reporting in SEM, advocating for detailed explanations of the model's rationale, alternatives, and identifiability. It also highlights the need to address common data issues like nonnormality and missing values. The inclusion of all relevant parameters, standard errors, and fit indices is recommended to facilitate a thorough understanding and evaluation by readers. A comparison of current reporting practices with the proposed guidelines showcases the adherence or deviation from these recommendations in the field.

By considering the problems that commercial saffron companies have faced in international markets, the aim of this study is to investigate the impact of website content, including informational and design dimensions, on the effectiveness of e-marketing and e-trust as mediator variables. These aspects are examined with reference to sales and marketing division managers in a sample of 100 commercial saffron corporations in the Khorasan province. The findings support the ideas that website content has an effect on e-marketing and e-trust and that e-trust plays a mediating role in the relationship between etrust and e-marketing effectiveness. 2013 Elsevier B.V. All rights reserved. * Corresponding author. Tel.: +98 5118811240; fax: +98 5118811243. E-mail addresses: r-nia@ferdowsi.um.ac.ir, frahimnia@hotmail.com (F. Rahimnia).

This analysis explores the impact and nature of reputation as related to e-commerce by looking at the importance of a seller’s reputational rating on the final bid price associated with eBay auctions. Positive reputational ratings emerged as mildly influential in determining final bid price. However, negative reputational ratings emerged as highly influential and detrimental. Thus, we find strong evidence for the importance of reputation when engaging in e-commerce and equally strong evidence concerning the exaggerated influence of negative reputation. © 2001 Elsevier Science Inc. All rights reserved.

Customer relationship management (CRM) is a customer-focused business strategy that dynamically integrates sales, marketing and customer care service in order to create and add value for the company and its customers. This change towards a customer-focused strategy is leading to a strong demand for CRM solutions by companies. However, in spite of companies interest in this new management model, many CRM implementations fail. One of the main reasons for this lack of success is that the existing methodologies being used to approach a CRM project are not adequate, since they do not satisfactorily integrate and complement the strategic and technological aspects of CRM. This paper describes a formal methodology for directing the process of developing and implementing a CRM System that considers and integrates various aspects, such as defining a customer strategy, re-engineering customer-oriented business processes, human resources management, the computer system, management of change and continuous improvement. 2005 Elsevier Inc. All rights reserved.

M oving some Web consumers along to the purchase click is proving to be difficult, despite the impressive recent growth in online shopping. Consumer online shopping revenues and related corporate profits are still meager, though the industry is optimistic, thanks to bullish forecasts of cyberconsumer activity for the new millennium. In 1996, Internet shopping revenues for U.S. users, excluding cars and real estate, were estimated by Jupiter Communications , an e-commerce consulting firm in New York, at approximately $707 million but are expected to How merchants can win back lost consumer trust in the interests of e-commerce sales. reach nearly $37.5 billion by 2002 [1]. Meanwhile, the business-to-business side is taking off with more than $8 billion in revenues for 1997 and $327 billion predicted by 2002 just in the U.S., according to For-rester Research, an information consulting firm in Cambridge, Mass. [4]. On the consumer side, a variety of barriers are invoked to explain the continuing difficulties. There are, to be sure, numerous barriers. Such factors as the lack of standard technologies for secure payment, and the lack of profitable business models play important roles in the relative dearth of commercial activity by businesses and consumers on the Internet compared to what analysts expect in the near future. Granted, the commercial development of the Web is still in its infancy, so few expect these barriers to commercial development to persist. Still, commercial development of the Web faces a far more formidable barrier—consumers' fear of divulging their personal data—to its ultimate com-mercialization. The reason more people have yet to shop online or even provide information to Web providers in exchange for access to information, is the fundamental lack of faith between most businesses and consumers on the Web today. In essence, consumers simply do not trust most Web providers enough to engage in " relationship exchanges " involving money and personal information with them. Our research reveals that this lack of trust arises from the fact that cyberconsumers feel they lack control over the access that Web merchants have to their

In this paper, we empirically investigate an extension of the Technology Acceptance Model (TAM, Davis, 1989) to explain the individual acceptance and usage of websites. Conceptually, we examine perceived ease-of-use, usefulness, enjoyment, and their impact on attitude towards using, intention to use and actual use. The paper also introduces a new construct, “perceived visual attractiveness” of the website and suggest that it influences usefulness, enjoyment, and ease-of-use. For our empirical research we partnered with a Dutch generic portal site with over 300 000 subscribers at the time the research was conducted. The websurvey resulted in sample size of 825 respondents. The results confirmed all of the 12 hypotheses formulated. Three findings are worth mentioning in particular: (1) intention is most dominantly influenced by attitude (β = 0.51), (2) ease-of-use, enjoyment, and usefulness contribute equally to attitude towards using (β = 0.23, 0.23, and 0.17 respectively) and (3) visual attractiveness contributes remarkably well to both ease-of-use, enjoyment, and usefulness (β = 0.41, 0.35, and 0.21). Although this is not the first research to apply TAM to an internet context, we claim three major contributions: (1) a single website as the unit of analysis, (2) the introduction of visual attractiveness, and (3) the use of “real” website visitors rather than student samples. Promising future research lies in the conceptual connection between actual website features and website use, a connection for which the TAM framework provides a meaningful bridge. Factors Influencing the Usage of Websites: The Case of a Generic Portal in the Netherlands

Despite the expected value of online customer reviews as an emerging advertising medium, the manner of enforcing its effectiveness on customers’ purchase behavior is still a question not well answered. To address the question, we propose a new central route cue called “advertising appeal” to examine attitude and intention to purchase a product or service based on the reasoning of the elaboration likelihood model. We also propose that consumers’ consumption goal and attitude certainty separately moderate the advertising appeal effect on purchase intention through a degree of favorable attitude. We test these hypotheses by conducting a laboratory experiment with 50 participants. In this experiment, we manipulate two kinds of advertising appeals and consumption goals. This study demonstrates that attribute-based appeal reviews are more influential than emotion-based appeal reviews in the persuasion process, regardless of the individuals’ consumption goals. However, the advertising appeal effect on purchase intention is more pronounced for hedonic consumers than for utilitarian consumers.

This article proposes in depth comparative study of the most popular, used and analyzed Trust and Reputation System (TRS) according to the trust and reputation literature and in terms of specific trustworthiness criteria. This survey is realized relying on a selection of trustworthiness criteria that analyze and evaluate the maturity and effectiveness of TRS. These criteria describe the utility, the usability, the performance and the effectiveness of the TRS. We also provide a summary table of the compared TRS within a detailed and granular selection of trust and reputation aspects.

Many recent studies of trust and reputation are made in the context of commercial reputation or rating systems for online communities. Most of these systems have been constructed without a formal rating model or much regard for our sociological understanding of these concepts. We first provide a critical overview of the state of research on trust and reputation. We then propose a formal quantitative model for the rating process. Based on this model, we formulate two personalized rating schemes and demonstrate their effectiveness at inferring trust experimentally using a simulated dataset and a real world movie-rating dataset. Our experiments show that the popular global rating scheme widely used in commercial electronic communities is inferior to our personalized rating schemes when sufficient ratings among members are available. The level of sufficiency is then discussed. In comparison with other models of reputation, we quantitatively show that our framework provides significantly better estimations of reputation. "Better" is discussed with respect to a rating process and specific games as defined in this work. Secondly, we propose a mathematical framework for modeling trust and reputation that is rooted in findings from the social sciences. In particular, our framework makes explicit the importance of social information (i.e., indirect channels of inference) in aiding members of a social network choose whom they want to partner with or to avoid. Rating systems that make use of such indirect channels of inference are necessarily personalized in nature, catering to the individual context of the rater. Finally, we have extended our trust and reputation framework toward addressing a fundamental problem for social science and biology: evolution of cooperation. We show that by providing an indirect inference mechanism for the propagation of trust and reputation, cooperation among selfish agents can be explained for a set of game theoretic simulations. For these simulations in particular, our proposal is shown to have provided more cooperative agent communities than existing schemes are able to. Thesis Supervisor: Peter Szolovits Title: Professor of Electrical Engineering and Computer Science

With the rapid change of business competitive environment, enterprise resource integration and innovative issues of business operation have gradually become the most important issues for businesses. Furthermore, many enterprises have implemented novel information technology and developing the innovative e-business applications systems such as enterprise resource planning (ERP), customer relationship management (CRM), knowledge management (KM) and supply chain management (SCM) to enhance their competitive advantages. CRM systems can help organizations to gain the potential new customers, promote the existing customers’ purchase, maintain good relationship with customers as well as to enhance the customer value, thus can improve the enterprise images. Moreover, the development and applications of CRM systems have also been considered as important issues for researchers and practitioners in recent years. For Taiwan’s industry, it has been gradually transferred from manufacturing-oriented to a service-oriented. Thus, the service industry has higher percentage in the GDP and in which the distribution service industry is the biggest one and be a core industry in the whole of service industry. The main purpose of this study is to explore the factors affecting the acceptance and use of CRM systems. Furthermore, the proposed research model was built on the basis of unified theory of acceptance and use of technology (UTAUT) and task-technology fit (TTF) framework as well as technological and managerial theories. The implications of findings for practice will be discussed. 2010 Elsevier Ltd. All rights reserved.

Loyalty programs have become an important component of firms’ relationship management strategies. There are now some industries in which numerous rival loyalty programs are offered, inducing intense competition among these programs. However, existing research on loyalty programs has often studied such programs in a noncompetitive setting and has often focused on a single program in isolation. Addressing this gap, this research examines the effect of a firm’s competitive positioning and market saturation on the performance of the firm’s loyalty program. Based on the analysis of firmand individual-level data from the airline industry, the results indicate that larger firms tend to benefit more from their loyalty program offerings than smaller firms. Moreover, when the product category demand is rigid, the impact of an individual loyalty program decreases as the marketplace becomes more saturated with competing programs. However, when the product category is highly expandable, the saturation effect disappears. Under such situations, loyalty programs can help an industry gain competitive advantage over substitute offerings outside the industry, and multiple programs can effectively coexist even under a high level of market saturation.


Question: The effects of brand trust, customer satisfaction, and brand loyalty on word-of-mouth communication.

In the realm of customer relationship management (CRM) and gaining customer insight, the proliferation of customer data over the past two decades has underscored the necessity for sophisticated data mining techniques. This is particularly true in the telecommunications industry, where customer diversity and varying risk profiles necessitate tailored strategies to optimize shareholder profits and revenue. Segmentation, the process of grouping customers based on similarities in account attributes and behaviors, becomes a pivotal tool for enhancing customer understanding and applying this knowledge to boost profitability, minimize operational expenses, and elevate customer service standards.

The core objective of segmentation is to provide a comprehensive view of the customer, facilitating more precise and effective targeting of treatments. Enhanced comprehension of customer risks and behaviors enables organizations to manage their portfolios more efficiently and proactively apply targeted interventions to enhance account profitability. This paper delineates various segmentation methodologies using SAS Enterprise MinerTM, highlighting its utility in customer analytics.

The rapid evolution of computer technology and the proliferation of data collection systems over the last three decades have intensified the imperative for businesses to intimately understand their clientele. Data-driven analytical strategies are increasingly seen as a source of competitive edge. Industries across the board are leveraging analytical models to deepen their understanding of customers, predict a multitude of outcomes, including behavioral risks, fraud, and response likelihoods. However, a single model may not be universally optimal across the entire target audience due to the presence of distinct segments with unique characteristics. While segmentation can be intuitively derived from experience, it is typically limited to considering a small number of variables. Comprehensive multivariate segmentation, aiming to identify diverse customer segments, is best achieved through cluster analysis.

The clustering and profiling of customer bases can elucidate several key questions:
- Who constitutes our customer base?
- What is the profitability of our customers?
- Who are our least profitable customers?
- Why are customers choosing to leave?
- What characterizes our most valuable customers?

This paper delves into the application of SAS Enterprise Miner for customer segmentation through cluster analysis and decision trees. Emphasis is placed on methodology rather than specific results to uphold customer data confidentiality. Additional statistical strategies for customer intelligence enhancement are also discussed. Cluster analysis, often described as an art form as much as a science, is conducted without the guidance of established statistical criteria, underscoring its reliance on both analytical rigor and intuitive judgment. Data mining and predictive modeling are thus crucial components of modern customer analytics, as demonstrated in this paper.

The 2002 North America Online Report published by eMarketer estimates that almost 24 million pre-college age students (ages 9-17) are already shopping online and gaining valuable e-commerce purchasing experience. Estimates of online shopping usage project steady growth, the number of young adults buying online will increase proportionally. This research seeks to develop a better understanding of the factors motivating young people to select e-commerce vendors for commodity purchases by exploring attitudes, demographic characteristics and purchase decision perceptions (i.e., the product, shopping experience, customer service, and consumer risk). Findings indicate that young adults with a history of e-commerce purchasing experience have a more positive attitude towards online buying than do young adults without e-commerce purchasing experience. In a related finding, a history of e-commerce purchasing experience serves as a good predictor of future e-commerce commodity purchases. Additionally, consumer risk and shopping experience perceptions were found to influence experienced e-commerce shoppers’ commodity purchase decisions more than customer service or consumer risk.

Many recent studies of trust and reputation are made in the context of commercial reputation or rating systems for online communities. Most of these systems have been constructed without a formal rating model or much regard for our sociological understanding of these concepts. We first provide a critical overview of the state of research on trust and reputation. We then propose a formal quantitative model for the rating process. Based on this model, we formulate two personalized rating schemes and demonstrate their effectiveness at inferring trust experimentally using a simulated dataset and a real world movie-rating dataset. Our experiments show that the popular global rating scheme widely used in commercial electronic communities is inferior to our personalized rating schemes when sufficient ratings among members are available. The level of sufficiency is then discussed. In comparison with other models of reputation, we quantitatively show that our framework provides significantly better estimations of reputation. "Better" is discussed with respect to a rating process and specific games as defined in this work. Secondly, we propose a mathematical framework for modeling trust and reputation that is rooted in findings from the social sciences. In particular, our framework makes explicit the importance of social information (i.e., indirect channels of inference) in aiding members of a social network choose whom they want to partner with or to avoid. Rating systems that make use of such indirect channels of inference are necessarily personalized in nature, catering to the individual context of the rater. Finally, we have extended our trust and reputation framework toward addressing a fundamental problem for social science and biology: evolution of cooperation. We show that by providing an indirect inference mechanism for the propagation of trust and reputation, cooperation among selfish agents can be explained for a set of game theoretic simulations. For these simulations in particular, our proposal is shown to have provided more cooperative agent communities than existing schemes are able to. Thesis Supervisor: Peter Szolovits Title: Professor of Electrical Engineering and Computer Science

This study proposed an integrated data mining and customer behavior scoring model to manage existing mobile banking users in an Iranian bank. This segmentation model was developed to identify groups of customers based on transaction history, recency, frequency, monetary background. It classified mobile banking users into six groups. This study demonstrated that identifying customers by a behavioral scoring facilitates marketing strategy assignment. Then the bank can develop its marketing actions. Thus, the bank can attract more customers, maintain its customers, and keep high customers' satisfaction. Keywords—Data mining; mobile data, mobile banking; customer segmentation Introduction Online banking is increasingly common. Financial institutions deliver online services via various electronic channels, subsequently diminishing the importance of conventional branch networks. The newly emerging channels of online banking and rapidly increasing penetration rates of mobile phones motivate this study (C. S. Chen, 2013). The internet has had a significant impact on financial institutions, allowing consumers to access many bank facilities 24 hours a day, while allowing banks to significantly cut their costs. Research has shown that online banking is the cheapest delivery channel for many banking services (KoenigLewis, Palmer, & Moll, 2010; Robinson, 2000). A number of studies have identified advantages to bank customers, including cost and time savings as well as spatial independence benefits (Koenig-Lewis et al., 2010). According to Gartner’s prediction of leading trends of 2012 in mobile applications, mobile commerce (m-commerce) remains the most important one. Gartner further forecasts that mobile devices will replace PCs as the main device to access the internet. As for the third quarter of 2012, IPSOS indicated that “The era of Multi-Screen has come, and smartphones account for the purchasing behavior of 65% of mobile device users.” According to that report, 66 percent of the smartphone holders in Taiwan access the internet via a smartphone at least once daily; approximately 57 percent of the customers perform mobile searches; and 40 percent of the customers shop via mobile phones (IPSOS, 2012). These statistics reflect vigorous growth in the scale of m-commerce. However, mobile banking remains in its infancy, and international adoption rates demonstrate the strong potential of m-commerce (FRB, 2012). Therefore, data mining for mobile banking is of priority concern for further developing mobile banking services (MBSs) (C.

Consumer habit, an important key to repetitive consumption, is an interesting yet puzzling phenomenon. Sometimes this consumption becomes obsessive--consumers will continue to act a certain way even when they feel it is not in their best interests. However, not all consumers develop such addictions. This study uses cyber-game addiction syndrome as an analogue to trace the possible causes of consumer addiction. Results from structure equation modeling show that repetition of favorite activities has a moderate effect upon addiction, which is in line with the assertion of rational addiction theory. However, flow experience--the emotional state embracing perceptional distortion and enjoyment--shows a much stronger impact on addiction. This suggests that consumers who have experienced flow are more likely to be addicted.

In the Internet era, consumers can comment on their hotel living experience directly through the Internet. Thus, consumers online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. The large amount of User-Generated Content (UGC) provided by the consumers has also provided chances for the hospitality industry to consumer behavior statistic inferences. This study applies Kansei engineering combined with text mining to develop guidelines for hotel services to help managers meet consumers Kansei needs. Through understanding consumers online comments by text mining, this study identifies the most important customer Kansei and hotel service characteristics, and then analyzes the relationships between them. The finds can provide suggestions for hotel service improvement and development.

The erosion of service quality throughout the economy is a frequent concern in the popular press. The American Customer Satisfaction Index for services fell in 2000 to 69.4%, down 5 percentage points from 1994. We hypothesize that the characteristics of services— inseparability, intangibility, and labor intensity—interact with management practices to bias service providers toward reducing the level of service they deliver, often locking entire industries into a vicious cycle of eroding service standards. To explore this proposition we develop a formal model that integrates the structural elements of service delivery. We use econometric estimation, interviews, observations, and archival data to calibrate the model for a consumer-lending service center in a major bank in the United Kingdom. We find that temporary imbalances between service capacity and demand interact with decision rules for effort allocation, capacity management, overtime, and quality aspirations to yield permanent erosion of the service standards and loss of revenue. We explore policies to improve performance and implications for organizational design in the service sector. (Organizational Learning; Service Management Performance; Service Operations; Service Quality; Simulation; System Dynamics)

This paper empirically studies consumer choice behavior in regard to store brands in the US, UK and Spain. Store brand market shares differ by country and they are usually much higher in Europe than in the US. However, there is surprisingly little work in marketing that empirically studies the reasons that underlie higher market shares associated with store brands in Europe over the US. In this paper, we empirically study the notion that the differential success of store brands in the US versus in Europe is the higher brand equity that store brands command in Europe over the US. We use a framework based on previous work to conduct our analysis: consumer brand choice under uncertainty, and brands as signals of product positions. More specifically, we examine whether uncertainty about quality (or, the positioning of the brand in the product space), perceived quality of store brands versus national brands, consistency in store brand offerings over time, as well as consumer attitudes towards risk, quality and price underlie the differential success of store brands at least partially in the US versus Europe. We propose and estimate a model that explicitly incorporates the impact of uncertainty on consumer behavior. We compare 1) levels of uncertainty associated with store brands versus national brands, 2) consistency in product positions over time for both national and store brands, 3) relative quality levels of national versus store brands, and 4) consumer sensitivity to price, quality and risk across the three countries we study. The model is estimated on scanner panel data on detergent in the US, UK and Spanish markets, and on toilet paper and margarine in the US and Spain. We find that consumer learning and perceived risk (and the associated brand equity), as well as consumer attitude towards risk, quality and price, play an important role in consumers’ store versus national brand choices and contribute to the differences in relative success of store brands across the countries we study.

Article history: Received August 28, 2015 Received in revised format November 28, 2015 Accepted December 1, 2015 Available online December 1, 2015 Empirical research has identified the phenomenon of destination bonding as a result of summated physical and emotional values associated with the destination. Physical values, namely natural landscape & other physical settings and emotional values, namely the enculturation processes, have a significant role to play in portraying visitors’ cognitive framework for destination preference. The physical values seemed to be the stimulator for bonding that embodies action or behavior tendencies in imagery. The emotional values were the conditions that lead to affective bonding and are reflected in attitudes for a place which were evident in text narratives. Social networking on virtual platforms offers the scope for hybrid cognitive expression using imagery and text to the visitors. Instagram has emerged as an application-window to capture these hybrid cognitions of visitors. This study focuses on assessing the relationship between hybrid cognition of visitors expressed via Instagram and their bond with the destination. Further to this, the study attempts to examine the impact of hybrid cognition of visitors on the behavioral pattern of prospective visitors to the destination. The study revealed that sharing of visual imageries and related text by the visitors is an expression of the physico-emotional bonding with the destination. It was further established that hybrid cognition strongly asserts destination bonding and has been also found to have moderating impact on the link between destination bonding and electronic-word-of-mouth. © 2016 Growing Science Ltd. All rights reserved.

Traditional retailers are facing strong competition from e-commerce. One way to meet this challenge is to follow the marketing movement of focusing on customer experiences. This transformation is based on the notion of engaging customers and one way to drive this engagement is through gamification to support value creation. In this study, we have identified variables affecting intentions to use gamified services and in what ways. For this purpose, we developed an app that generated different levels of gamification by varying the number of game elements. The data from a survey distributed during a field experiment indicates that an increasing level of gamification and technology experience have direct positive associations with intrinsic motivation. Furthermore, intrinsic motivation has a positive direct association with satisfaction, although this is partly mediated by mood. Finally, satisfaction has a positive direct relation with intention to use.

Internet development has fueled e-commerce firms’ globalization efforts, but many have met with only limited success. This often stems from the foreign firms’ limited understanding of a focal country’s local culture and idiosyncrasies. Foreign firms are usually viewed as out-group entities, which lowers consumers’ trust in them. The extent of such a phenomenon varies. In locations where people are more skeptical of out-groups, a critical question is whether it is possible to transform such foreign out-group firms into in-groups, specifically with the support of popular social networking media. Based on Social Identity Theory and Trust Transference Process, five strategies leveraging social grouping and social ties to build trust for foreign electronic commerce firms were proposed. A survey was conducted to examine their effectiveness. The results suggest that social-grouping strategies are useful for in-grouping foreign out-group entities to build trust, and the effectiveness of strategies is determined by the social similarity and psychological distance between the consumer and the endorser. This has important implications for scholars and practitioners, both local and abroad, to leverage social grouping to boost Internet sales. © 2013 Elsevier Ltd. All rights reserved.


Question: The effects of brand trust, customer satisfaction, and brand loyalty on word-of-mouth communication.

This text delves into the intricacies of causality, exploring its models, reasoning, and inference, a subject of paramount importance to researchers in the expansive fields of probabilistic graphical models, belief networks, and causal influence. Dr. Pearl, a distinguished figure and recipient of the ACM Turing award, is widely recognized for his seminal contributions to the understanding of causality. The book, "Causality: Models, Reasoning, and Inference," authored by Dr. Pearl, who is credited with the invention of Bayesian belief networks, offers a comprehensive treatment of his extensive research on causality. It not only revisits his previous work on reasoning with cause and effect but also delves into various facets of causal inference in statistics, including Simpson's paradox, causal diagrams for empirical research, the robustness of causal claims, causes and explanations, and probabilities of causation, including bounds and identification. This book serves as a critical resource for anyone seeking to grasp the complex interplay between causes and effects in various scientific and statistical contexts. However, it should be noted that this text does not directly address the impacts of brand trust, customer satisfaction, and brand loyalty on word-of-mouth, which is a distinct area of marketing and consumer behavior research.

Internet self-efficacy (ISE), or the beliefs in one’s capabilities to organize and execute courses of Internet actions required to produce given attainments, is a potentially important factor to explain the consumers’ decisions in e-commerce use, such as eservice. In this study, we introduce two types of ISE (i.e., general Internet self-efficacy and Web-specific self-efficacy) as new factors that reflect the user’s behavioral control beliefs in e-service acceptance. Using these two constructs as behavioral control factors, we extend and empirically validate the Theory of Planned Behavior (TPB) for the World Wide Web (WWW) context. D 2003 Elsevier B.V. All rights reserved.

Many of the instruments to measure information and system quality were developed in the context of mainframe and PC-based technologies of yesteryears. With the proliferation of the Internet and World Wide Web applications, users are increasingly interfacing and interacting with web-based applications. It is, therefore, important to develop new instruments and scales, which are directly targeted to these new interfaces and applications. In this article, we report on the development of an instrument that captures key characteristics of web site quality from the userâ€TMs perspective. The 25item instrument measures four dimensions of web quality: specific content, content quality, appearance and technical adequacy. While improvements are possible, the instrument exhibits excellent psychometric properties. The instrument would be useful to organizations and web designers as it provides an aggregate measure of web quality, and to researchers in related web research. a 1, b Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.

The author compares the multi-attribute and subjective expected utility (SEU) models popular in research on consumer behavior to the approach offered by the theory of planed behavior (TPB). Unlike the multi-attribute and SEU models, the TPB relies not on revealed preferences to infer the underlying decision process but instead on direct assessment of its theoretical constructs. According to the theory, the consumer’s behavior is a function of intention to perform the behavior in question; the intention is based on attitude, subjective norm, and perceived behavioral control with respect to the behavior; and these factors are determined, respectively, by behavioral, normative, and control beliefs. The theory allows us to predict intentions and behavior with respect to the purchase or use of a single brand or product as well as in relation to choice among different brands or products.

Vol. XLIII (February 2006), 1–10 1 © 2006, American Marketing Association ISSN: 0022-2437 (print), 1547-7193 (electronic) *Tamar Avnet is Assistant Professor of Business, Rotman School of Management, University of Toronto (e-mail: tavnet@rotman.utoronto.ca). E. Tory Higgins is Professor of Psychology, Department of Psychology, and Professor of Management, Columbia School of Business, Columbia University (e-mail: tory@psych.columbia.edu). The research reported in this article was supported by a grant (No. 39429) from the National Institute of Mental Health to E. Tory Higgins. TAMAR AVNET and E. TORY HIGGINS*

0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.08.005 ⇑ Corresponding author. Tel.: +886 2 2737 6764; fa E-mail address: melodyps@gmail.com (P.-S. Wei). The goal of this study is to compare the influence of celebrity endorsements to online customer reviews on female shopping behavior. Based on AIDMA and AISAS models, we design an experiment to investigate consumer responses to search good and experience good respectively. The results revealed that search good (shoes) endorsed by a celebrity in an advertisement evoked significantly more attention, desire, and action from the consumer than did an online customer review. We also found that online customer reviews emerged higher than the celebrity endorsement on the scale of participants’ memory, search and share attitudes toward the experience good (toner). Implications for marketers as well as suggestions for future research are discussed. 2012 Elsevier Ltd. All rights reserved.

User satisfaction with general information system (IS) and certain types of information technology (IT) applications has been thoroughly studied in IS research. With the widespread and increasing use of portal technology, however, there is a need to conduct a user satisfaction study on portal use -in particular, the business-to-employee (b2e) portal. In this paper, we propose a conceptual model for determining b2e portal user satisfaction, which has been derived from an extensive literature review of user satisfaction scales and the b2e portal. Nine dimensions of b2e portal user satisfaction are identified and modeled: information content, ease of use, convenience of access, timeliness, efficiency, security, confidentiality, communication, and layout.

U recently, brand identities were built by firms via brand image advertising. However, the flourishing consumer communication weakened the firms’ grip on their brands. The interaction between advertising and consumer communications and their joint impact on brand identity is the focal point of this paper. We present a model in which consumer preference for functional attributes may correlate with the identity they desire to project of themselves. This correlation is known to the firm but not to the consumers. Both the firm and the consumers can communicate their desired brand identity, although the actual brand identity is determined endogeneously by the composition of consumers who purchase it (i.e., what types of people consume the brand). We find that sometimes the firm can strengthen the identity of its brand by refraining from advertising. This result is based on the following intermediate finding: advertising can diminish the endogeneous informativeness of consumer communications by making it one-sided. Furthermore, it turns out that refraining from brand image advertising may be optimal for the firm when the product is especially well positioned to create a strong identity—i.e., when consumer preferences for functional and self-expressive attributes are highly correlated.

The emergence of the WeChat has brought some new changes to people's ways of life and reading. Thanks to the WeChat Public Platform, many people from all walks of life have become We Media operators, creating their own brands and products. In a case study of WeChat Public Platform, this paper uses NLPIR, a tool for Chinese word segmentation, and SPSS, a data analysis software, to conduct an analysis of actual data captured by Web Spider about WeChat posts and the platform itself, exploring the effects of post content and platform attributes on users' reading and dissemination behaviors. Our regression analysis found that some content attributes, including parts of speech like adverb, punctuation marks such as question mark, and headline length and position, as well as platform attributes, such as the platform seniority, have significant impacts on page views; the genres of advertisement, joke and warning, among other content attributes, and follower count as a platform attribute, have a very significant effect on the dissemination index. This paper is helpful for operators to gain a better understanding of factors influencing the spread of information published by WeChat Official Accounts and providing valuable revelations on achieving fast and efficient dissemination of posts.

Citizen confidence in the competence of the government and the reliability of the technology used to implement egovernment initiatives is imperative to the wide-spread adoption of e-government. This study analyzes how citizens’ trust in technology and government affect their willingness to engage in e-government transactions. We proposes a model of egovernment trust composed of disposition to trust, institution-based trust (IBT), characteristic-based trust (CBT) and perceived risk. Data were collected via a survey of 214 citizens ranging in age from 14 to 83 old. The model was tested using Structural Equation Modeling techniques. Results indicate that disposition to trust positively affects IBT and CBT trusts, which in turn affect intentions to use an e-government service. CBT trust also affects negatively perceived risk, which affects use intentions as well. Implications for practice and research are discussed.

Using the means-end framework as a theoretical foundation, this article conceptualizes, constructs, refines, and tests a multiple-item scale (E-S-QUAL) for measuring the service quality delivered by Web sites on which customers shop online. Two stages of empirical data collection revealed that two different scales were necessary for capturing electronic service quality. The basic E-S-QUAL scale developed in the research is a 22-item scale of four dimensions: efficiency, fulfillment, system availability, and privacy. The second scale, E-RecS-QUAL, is salient only to customers who had nonroutine encounters with the sites and contains 11 items in three dimensions: responsiveness, compensation, and contact. Both scales demonstrate good psychometric properties based on findings from a variety of reliability and validity tests and build on the research already conducted on the topic. Directions for further research on electronic service quality are offered. Managerial implications stemming from the empirical findings about E-S-QUAL are also discussed.


Question: The effects of brand trust, customer satisfaction, and brand loyalty on word-of-mouth communication.

In the realm of information systems (IS) research, a significant body of work has been dedicated to understanding the factors that contribute to IS success. Yet, defining the dependent variable—IS success—has proven challenging, with various studies focusing on distinct aspects, leading to a fragmented research landscape. To address this issue, a comprehensive taxonomy has been proposed to categorize and organize the diverse research findings. This taxonomy identifies six key dimensions of IS success: SYSTEM QUALITY, INFORMATION QUALITY, USE, USER SATISFACTION, INDIVIDUAL IMPACT, and ORGANIZATIONAL IMPACT. By consolidating these dimensions, a total of 180 articles are analyzed, providing a more integrated perspective on IS success. A descriptive model is then constructed to encapsulate the multifaceted nature of IS success, offering valuable insights for future research endeavors in the field.

**Question**: The role of customer engagement in the adoption of digital banking services

**Documents**: The effect of mobile banking on customer satisfaction and loyalty in the banking industry
The banking industry has witnessed a significant transformation due to the widespread adoption of mobile banking services. This study aims to investigate the impact of mobile banking on customer satisfaction and loyalty in the banking sector. The research employs a quantitative approach, utilizing a survey method to collect data from a sample of 500 bank customers who use mobile banking services. The findings reveal that mobile banking significantly influences customer satisfaction and loyalty. Customers who frequently use mobile banking services are more satisfied and exhibit higher loyalty towards their banks. The study also highlights the importance of user interface design, transaction speed, and security measures in enhancing customer satisfaction and loyalty. Banks are advised to continuously improve their mobile banking platforms to meet the evolving needs of customers and foster long-term loyalty.

**Similar Documents**: The banking landscape has undergone a profound change with the integration of digital services, particularly mobile banking. This research focuses on assessing the effects of mobile banking on customer satisfaction and loyalty within the banking sector. Adopting a quantitative methodology, the study surveyed 500 mobile banking users to gather insights on their experiences. Results indicate a strong correlation between mobile banking usage and heightened customer satisfaction and loyalty. Regular users of mobile banking services display greater satisfaction and a stronger commitment to their financial institutions. Moreover, the study underscores the critical role of user-friendly interfaces, swift transaction processing, and robust security protocols in driving customer satisfaction and loyalty. Banks are encouraged to refine and upgrade their mobile banking platforms continuously to align with customer expectations and cultivate enduring loyalty.

Purpose – The purpose of this research is to examine the effect of flow experience on mobile social networking service (SNS) users’ loyalty. Design/methodology/approach – Based on 305 valid responses collected from a survey questionnaire, structural equation modeling (SEM) technology was employed to examine the research model. Findings – The results show that both information quality and system quality significantly affect users’ trust and flow experiences, which further determine their loyalty. The results indicate that flow experience is the strongest determinant of users’ loyalty. Practical implications – Mobile SNS providers need to consider user experience when seeking users’ loyalty. They should enhance information quality and system quality in order to improve user trust and flow experience. Originality/value – Although much research has been conducted to explore the effects of extrinsic motivations, such as perceived usefulness, on mobile commerce user behavior, the effect of intrinsic motivations, such as flow experience, is seldom tested. This research found the effect of flow experience on mobile SNS users’ loyalty to be significant.

Trust models are widely used in various computer science disciplines. The primary purpose of a trust model is to continuously measure the trustworthiness of a set of entities based on their behaviors. In this article, the novel notion of rational trust modeling is introduced by bridging trust management and game theory. Note that trust models/reputation systems have been used in game theory (e.g., repeated games) for a long time, however, game theory has not been utilized in the process of trust model construction; this is the novelty of our approach. In our proposed setting, the designer of a trust model assumes that the players who intend to utilize the model are rational/selfish, i.e., they decide to become trustworthy or untrustworthy based on the utility that they can gain. In other words, the players are incentivized (or penalized) by the model itself to act properly. The problem of trust management can be then approached by game theoretical analyses and solution concepts such as Nash equilibrium. Although rationality might be built-in in some existing trust models, we intend to formalize the notion of rational trust modeling from the designer’s perspective. This approach will result in two fascinating outcomes. First of all, the designer of a trust model can incentivize trustworthiness in the first place by incorporating proper parameters into the trust function, which can be later utilized among selfish players in strategic trust-based interactions (e.g., e-commerce scenarios). Furthermore, using a rational trust model, we can prevent many well-known attacks on trust models. These two prominent properties also help us to predict the behavior of the players in subsequent steps by game theoretical analyses.

The authors report results from 5 experiments that describe the influence of emotional states on trust. They found that incidental emotions significantly influence trust in unrelated settings. Happiness and gratitude--emotions with positive valence--increase trust, and anger--an emotion with negative valence--decreases trust. Specifically, they found that emotions characterized by other-person control (anger and gratitude) and weak control appraisals (happiness) influence trust significantly more than emotions characterized by personal control (pride and guilt) or situational control (sadness). These findings suggest that emotions are more likely to be misattributed when the appraisals of the emotion are consistent with the judgment task than when the appraisals of the emotion are inconsistent with the judgment task. Emotions do not influence trust when individuals are aware of the source of their emotions or when individuals are very familiar with the trustee.

Despite the significant influence of citizens’ trust towards successful adoption of e-government, the majority of research studies investigating trust in e-government focus only on technology and government agencies. This research provides a deep understanding of the concept of trust in e-government by investigating the influence of technology, government agencies, risk, citizens’ characteristic. Structure Equation Modelling PLS-SEM is utilized to analyse the collected data and to test the proposed hypotheses. The findings of the study reveals the positive and significant impacts of both technical factors and citizens’ characteristic while factors related to government agencies and risk provide negative impacts in trust in egovernment. Also, this paper identifies effective strategies that government need to develop citizens’ trust on their online services. Research contributions and limitations are also discussed in the end of the paper.

Review helpfulness is attracting increasing attention of practitioners and academics. It helps in reducing risks and uncertainty faced by users in online shopping. This study examines uninvestigated variables by looking at not only the review characteristics but also important indicators of reviewers. Several significant review content and two reviewer variables are proposed and an effective review helpfulness prediction model is built using stochastic gradient boosting learning method. This study derived a mechanism to extract novel review content variables from review text. Six popular machine learning models and three real-life Amazon review data sets are used for analysis. Our results are robust to several product categories and along three Amazon review data sets. The results show that review content variables deliver the best performance as compared to the reviewer and state-of-the-art baseline as a standalone model. This study finds that reviewer helpfulness per day and syllables in review text strongly relates to review helpfulness. Moreover, the number of space, aux verb, drives words in review text and productivity score of a reviewer are also effective predictors of review helpfulness. The findings will help customers to write better reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.

Evidence exists that service quality delivery through Web sites is an essential strategy to success, possibly more important than low price and Web presence. To deliver superior service quality, managers of companies with Web presences must first understand how customers perceive and evaluate online customer service. Information on this topic is beginning to emerge from both academic and practitioner sources, but this information has not yet been examined as a whole. The goals of this article are to review and synthesize the literature about service quality delivery through Web sites, describe what is known about the topic, and develop an agenda for needed research.

Information overload has been studied extensively by decision science researchers, particularly in the context of task-based optimization decisions. Media selection research has similarly investigated the extent to which task characteristics influence media choice and use. This paper outlines a proposed study, which would compare the effectiveness of web-based online product review systems in facilitation trust and purchase intention to those of mobile product review systems. We propose that since web-based systems are more effective in fostering focus and are less prone to navigation frustration, information overload is less likely influence the extent to which a consumer trusts an online product review.

This paper examines what motivates employees in the retail industry, and examines their level of job satisfaction, using Herzberg's hygiene factors and motivators. In this study, convenience sampling was used to select sales personnel from women's clothing stores in Bandar Sunway shopping mall in the state of Selangor. The results show that hygiene factors were the dominant motivators of sales personnel job satisfaction. Working conditions were the most significant in motivating sales personnel. Recognition was second, followed by company policy and salary. There is a need to delve more deeply into why salespeople place such a high importance on money. Further analysis was performed to assess how much the love of money mediates the relationship between salary and job satisfaction. Based on the general test for mediation, the love of money could explain the relationship between salary and job satisfaction. The main implication of this study is that sales personnel who value money highly are satisfied with their salary and job when they receive a raise.

A number of both national and international customer satisfaction barometers or indices have been introduced in the last decade. For the most part, these satisfaction indices are embedded within a system of cause and effect relationships or satisfaction model. Yet there has been little in the way of model development. Of critical importance to the validity and reliability of such indices is that the models and methods used to measure customer satisfaction and related constructs continue to learn, adapt, and improve over time. The primary goal of this research is to propose and test a number of modifications and improvements to the national index models. Using survey data from the Norwegian Customer Satisfaction Barometer (NCSB), we find general support for the proposed modifications.

U recently, brand identities were built by firms via brand image advertising. However, the flourishing consumer communication weakened the firms’ grip on their brands. The interaction between advertising and consumer communications and their joint impact on brand identity is the focal point of this paper. We present a model in which consumer preference for functional attributes may correlate with the identity they desire to project of themselves. This correlation is known to the firm but not to the consumers. Both the firm and the consumers can communicate their desired brand identity, although the actual brand identity is determined endogeneously by the composition of consumers who purchase it (i.e., what types of people consume the brand). We find that sometimes the firm can strengthen the identity of its brand by refraining from advertising. This result is based on the following intermediate finding: advertising can diminish the endogeneous informativeness of consumer communications by making it one-sided. Furthermore, it turns out that refraining from brand image advertising may be optimal for the firm when the product is especially well positioned to create a strong identity—i.e., when consumer preferences for functional and self-expressive attributes are highly correlated.


Question: Acquiring mixed initiative dialog tactics by applying reinforcement learning to both dialogue participants.

Enhancing Dialogue Management Through Reinforcement Learning: The Case Study of the NJFun System

Developing an effective dialogue policy for a spoken dialogue system presents numerous complex decisions. This paper explores a reinforcement learning technique for the automatic optimization of dialogue policy, tackling the practical difficulties associated with implementing reinforcement learning in a real-world dialogue system that interacts with human users. We detail the design, implementation, and empirical testing of the NJFun system, an experimental spoken dialogue platform that offers users information about leisure activities in New Jersey. Our findings reveal that by fine-tuning its performance through reinforcement learning, the NJFun system significantly enhances overall system efficiency and user experience.

Mobile robots are increasingly populating our human environments. To interact with humans in a socially compliant way, these robots need to understand and comply with mutually accepted rules. In this paper, we present a novel approach to model the cooperative navigation behavior of humans. We model their behavior in terms of a mixture distribution that captures both the discrete navigation decisions, such as going left or going right, as well as the natural variance of human trajectories. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the resulting high-dimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. Furthermore, we rely on a Voronoi graph of the environment to efficiently explore the space of trajectories from the robot’s current position to its target position. Using the proposed model, our method is able to imitate the behavior of pedestrians or, alternatively, to replicate a specific behavior that was taught by tele-operation in the target environment of the robot. We implemented our approach on a real mobile robot and demonstrate that it is able to successfully navigate in an office environment in the presence of humans. An extensive set of experiments suggests that our technique outperforms state-of-the-art methods to model the behavior of pedestrians, which makes it also applicable to fields such as behavioral science or computer graphics.

A long-lived autonomous agent should be able to respond online to novel instances of tasks from a familiar domain. Acting online requires ‘fast’ responses, in terms of rapid convergence, especially when the task instance has a short duration such as in applications involving interactions with humans. These requirements can be problematic for many established methods for learning to act. In domains where the agent knows that the task instance is drawn from a family of related tasks, albeit without access to the label of any given instance, it can choose to act through a process of policy reuse from a library in contrast to policy learning. In policy reuse, the agent has prior experience from the class of tasks in the form of a library of policies that were learnt from sample task instances during an offline training phase. We formalise the problem of policy reuse and present an algorithm for efficiently responding to a novel task instance by reusing a policy from this library of existing policies, where the choice is based on observed ‘signals’ which correlate to policy performance. We achieve this by posing the problem as a Bayesian choice problem with a corresponding notion of an optimal response, but the computation of that response is in many cases intractable. Therefore, to reduce the computation cost of the posterior, we follow a Bayesian optimisation approach and define a set of policy selection functions, which balance exploration in the policy library against exploitation of previously tried policies, together with a model of expected performance of the policy library on their corresponding task instances. We validate our method in several simulated domains of interactive, short-duration episodic tasks, showing rapid convergence in unknown task variations.

The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.

Techniques for both exploratory and known item search tend to direct only to more specific subtopics or individual documents, as opposed to allowing directing the exploration of the information space. We present an interactive information retrieval system that combines Reinforcement Learning techniques along with a novel user interface design to allow active engagement of users in directing the search. Users can directly manipulate document features (keywords) to indicate their interests and Reinforcement Learning is used to model the user by allowing the system to trade off between exploration and exploitation. This gives users the opportunity to more effectively direct their search nearer, further and following a direction. A task-based user study conducted with 20 participants comparing our system to a traditional query-based baseline indicates that our system significantly improves the effectiveness of information retrieval by providing access to more relevant and novel information without having to spend more time acquiring the information.

Review of Semantic-Free Utterances in Social Human–Robot Interaction Selma Yilmazyildiz, Robin Read, Tony Belpeame & Werner Verhelst To cite this article: Selma Yilmazyildiz, Robin Read, Tony Belpeame & Werner Verhelst (2016) Review of Semantic-Free Utterances in Social Human–Robot Interaction, International Journal of Human–Computer Interaction, 32:1, 63-85, DOI: 10.1080/10447318.2015.1093856 To link to this article: http://dx.doi.org/10.1080/10447318.2015.1093856

Despite deep reinforcement learning has recently achieved great successes, however in multiagent environments, a number of challenges still remain. Multiagent reinforcement learning (MARL) is commonly considered to suffer from the problem of non-stationary environments and exponentially increasing policy space. It would be even more challenging to learn effective policies in circumstances where the rewards are sparse and delayed over long trajectories. In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning (hierarchical deep MARL) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed. We decompose the original MARL problem into hierarchies and investigate how effective policies can be learned hierarchically in synchronous/asynchronous hierarchical MARL frameworks. Several hierarchical deep MARL architectures, i.e., Ind-hDQN, hCom and hQmix, are introduced for different learning paradigms. Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER). We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.

The Turing Test Track of the Mario AI Championship focused on developing human-like controllers for a clone of the popular game Super Mario Bros. Competitors participated by submitting AI agents that imitate human playing style. This paper presents the rules of the competition, the software used, the voting interface, the scoring procedure, the submitted controllers and the recent results of the competition for the year 2012. We also discuss what can be learnt from this competition in terms of believability in platform games. The discussion is supported by a statistical analysis of behavioural similarities and differences among the agents, and between agents and humans. The paper is co-authored by the organizers of the competition (the first three authors) and the competitors.

Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. It is generally presumed that humans should take the role to lead the conversation and introduce new content when a stalemate occurs, and that the computer only needs to “respond.” In this paper, we propose STALEMATEBREAKER, a conversation system that can proactively introduce new content when appropriate. We design a pipeline to determine when, what, and how to introduce new content during human-computer conversation. We further propose a novel reranking algorithm BiPageRank-HITS to enable rich interaction between conversation context and candidate replies. Experiments show that both the content-introducing approach and the reranking algorithm are effective. Our full STALEMATEBREAKER model outperforms a state-of-the-practice conversation system by +14.4% p@1 when a stalemate occurs.

The robot soccer system is being used as a test bed to develop the next generation of field robots. In the multiagent system, action selection is important for the cooperation and coordination among agents. There are many techniques in choosing a proper action of the agent. As the environment is dynamic, reinforcement learning is more suitable than supervised learning. Reinforcement learning is based on trial and error method through experience. Because of this, reinforcement learning is applied to many practical problems. However, straightforward application of reinforcement learning algorithm may not successfully scale up to more complex multi-agent learning problems. To solve this problem, modular Q-learning is employed for multi-agent learning in reinforcement methods. A modified uni-vector field is used for robot navigation. This paper discusses the Y2K2 NaroSot(Nano-Robot World Cup Soccer Tournament) robot soccer system.

One reason why Distributed AI (DAI) technology has been deployed in relatively few real-size applications is that it lacks a clear and implementable model of cooperative problem solving which specifies how agents should operate and interact in complex, dynamic and unpredictable environments. As a consequence of the experience gained whilst building a number of DAI systems for industrial applications, a new principled model of cooperation has been developed. This model, called Joint Responsibility, has the notion of joint intentions at its core. It specifies pre-conditions which must be attained before collaboration can commence and prescribes how individuals should behave both when joint activity is progressing satisfactorily and also when it runs into difficulty. The theoretical model has been used to guide the implementation of a general-purpose cooperation framework and the qualitative and quantitative benefits of this implementation have been assessed through a series of comparative experiments in the real-world domain of electricity transportation management. Finally, the success of the approach of building a system with an explicit and grounded representation of cooperative problem solving is used to outline a proposal for the next generation of multi-agent systems.


Question: Applying reinforcement learning to both conversants for the acquisition of mixed initiative dialog strategies.

This paper introduces a probabilistic framework for human-machine interaction aimed at learning dialog strategies. We argue that the design of dialog systems can be conceptualized as an optimization challenge, with an objective function that encapsulates various dialog dimensions pertinent to the specific application. Furthermore, we assert that any dialog system can be abstractly represented as a sequential decision-making process, delineating its state space, action set, and strategy. By incorporating assumptions about state transition probabilities and cost assignments, a dialog system can be aligned with a stochastic model, specifically a Markov Decision Process (MDP). Within this MDP framework, a multitude of data-driven algorithms exist for identifying the optimal strategy, which maximizes the defined criterion, utilizing reinforcement learning techniques. To leverage training data efficiently, we advocate for a hybrid approach combining supervised and reinforcement learning. Supervised learning is employed to construct a model of the user, quantifying their behavior through the estimation of MDP parameters. Subsequently, a reinforcement learning algorithm is utilized to determine the optimal strategy as the system interacts with a simulated user. This methodology is evaluated through the learning of dialog strategies in an air travel information system (ATIS) scenario. The empirical outcomes detailed herein affirm the feasibility of automatically acquiring a sophisticated dialog behavior, akin to that developed heuristically by expert teams, by defining a straightforward criterion, state space representation, and user simulation parameters.

We propose detecting and responding to humor in spoken dialogue by extracting language and audio cues and subsequently feeding these features into a combined recurrent neural network (RNN) and logistic regression model. In this paper, we parse Switchboard phone conversations to build a corpus of punchlines and unfunny lines where punchlines precede laughter tokens in Switchboard transcripts. We create a combined RNN and logistic regression model that uses both acoustic and language cues to predict whether a conversational agent should respond to an utterance with laughter. Our model achieves an F1-score of 63.2 and accuracy of 73.9. This model outperforms our logistic language model (F1-score 56.6) and RNN acoustic model (59.4) as well as the final RNN model of D. Bertero, 2016 (52.9). Using our final model, we create a “laughbot” that audibly responds to a user with laughter when their utterance is classified as a punchline. A conversational agent outfitted with a humorrecognition system such as the one we present in this paper would be valuable as these agents gain utility in everyday life. Keywords—Chatbots; spoken natural language processing; deep learning; machine learning

This paper summarizes our ongoing project on developing an architecture for a robot that can acquire new words and their meanings while engaging in multidomain dialogues. These two functions are crucial in making conversational service robots work in real tasks in the real world. Household robots and office robots need to be able to work in multiple task domains and they also need to engage in dialogues in multiple domains corresponding to those task domains. Lexical acquisition is necessary because speech understanding cannot be done without enough knowledge on words that are possibly spoken in the task domain. Our architecture is based on a multi-expert model in which multiple domain experts are employed and one of them is selected based on the user utterance and the situation to engage in the control of the dialogue and physical behaviors. We incorporate experts that have an ability to acquire new lexical entries and their meanings grounded on the physical world through spoken interactions. By appropriately selecting those experts, lexical acquisition in multi-domain dialogues becomes possible. An example robotic system based on this architecture that can acquire object names and location names demonstrates the viability of the architecture.

Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine’s policy will prioritize each player’s interests over time. Assuming the players have reached common knowledge of their situation, this paper derives a recursion that any Pareto optimal policy must satisfy. Two qualitative observations can be made from the recursion: the machine must (1) use each player’s own beliefs in evaluating how well an action will serve that player’s utility function, and (2) shift the relative priority it assigns to each player’s expected utilities over time, by a factor proportional to how well that player’s beliefs predict the machine’s inputs. Observation (2) represents a substantial divergence from näıve linear utility aggregation (as in Harsanyi’s utilitarian theorem, and existing MORL algorithms), which is shown here to be inadequate for Pareto optimal sequential decision-making on behalf of players with different beliefs.

Exploration is a difficult challenge in reinforcement learning and is of prime importance in sparse reward environments. However, many of the state of the art deep reinforcement learning algorithms, that reply on epsilon-greedy exploration, fail on these environments. In such environments, empowerment can serve as an intrinsic reward signal to enable the agent to explore by maximizing the influence it has over the near future. We formulate empowerment as the channel capacity between actions and states and is calculated by estimating the mutual information between the actions and the following states. The mutual information is estimated using a Mutual Information Neural Estimator and a forward dynamics model. We demonstrate that an empowerment driven agent is able to improve significantly the score of a baseline DQN agent on the game of Montezuma’s Revenge.

We present methods for optimizing portfolios, asset allocations, and trading systems based on direct reinforcement (DR). In this approach, investment decision-making is viewed as a stochastic control problem, and strategies are discovered directly. We present an adaptive algorithm called recurrent reinforcement learning (RRL) for discovering investment policies. The need to build forecasting models is eliminated, and better trading performance is obtained. The direct reinforcement approach differs from dynamic programming and reinforcement algorithms such as TD-learning and Q-learning, which attempt to estimate a value function for the control problem. We find that the RRL direct reinforcement framework enables a simpler problem representation, avoids Bellman's curse of dimensionality and offers compelling advantages in efficiency. We demonstrate how direct reinforcement can be used to optimize risk-adjusted investment returns (including the differential Sharpe ratio), while accounting for the effects of transaction costs. In extensive simulation work using real financial data, we find that our approach based on RRL produces better trading strategies than systems utilizing Q-learning (a value function method). Real-world applications include an intra-daily currency trader and a monthly asset allocation system for the S&P 500 Stock Index and T-Bills.

Reinforcement learning offers one of the most general framework to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like humanoid robots remains an unsolved problem. In this paper, we discuss different approaches of reinforcement learning in terms of their applicability in humanoid robotics. Methods can be coarsely classified into three different categories, i.e., greedy methods, ‘vanilla’ policy gradient methods, and natural gradient methods. We discuss that greedy methods are not likely to scale into the domain humanoid robotics as they are problematic when used with function approximation. ‘Vanilla’ policy gradient methods on the other hand have been successfully applied on real-world robots including at least one humanoid robot [3]. We demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. A derivation of the natural policy gradient is provided, proving that the average policy gradient of Kakade [10] is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges to the nearest local minimum of the cost function with respect to the Fisher information metric under suitable conditions. The algorithm outperforms non-natural policy gradients by far in a cart-pole balancing evaluation, and for learning nonlinear dynamic motor primitives for humanoid robot control. It offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems.

Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete nite-state Markovian problems, which is too restrictive for many real-world environments. Therefore, it is desirable to extend these methods to high dimensional continuous state-spaces, which requires the use of function approximation to generalise the information learnt by the system. In this report, the use of back-propagation neural networks (Rumelhart, Hinton and Williams 1986) is considered in this context. We consider a number of di erent algorithms based around Q-Learning (Watkins 1989) combined with the Temporal Di erence algorithm (Sutton 1988), including a new algorithm (Modi ed Connectionist Q-Learning), and Q( ) (Peng and Williams 1994). In addition, we present algorithms for applying these updates on-line during trials, unlike backward replay used by Lin (1993) that requires waiting until the end of each trial before updating can occur. On-line updating is found to be more robust to the choice of training parameters than backward replay, and also enables the algorithms to be used in continuously operating systems where no end of trial conditions occur. We compare the performance of these algorithms on a realistic robot navigation problem, where a simulated mobile robot is trained to guide itself to a goal position in the presence of obstacles. The robot must rely on limited sensory feedback from its surroundings, and make decisions that can be generalised to arbitrary layouts of obstacles. These simulations show that on-line learning algorithms are less sensitive to the choice of training parameters than backward replay, and that the alternative update rules of MCQ-L and Q( ) are more robust than standard Q-learning updates. 1

Creating animation of a character putting on clothing is challenging due to the complex interactions between the character and the simulated garment. We take a model-free deep reinforcement learning (deepRL) approach to automatically discovering robust dressing control policies represented by neural networks. While deepRL has demonstrated several successes in learning complex motor skills, the data-demanding nature of the learning algorithms is at odds with the computationally costly cloth simulation required by the dressing task. This paper is the first to demonstrate that, with an appropriately designed input state space and a reward function, it is possible to incorporate cloth simulation in the deepRL framework to learn a robust dressing control policy. We introduce a salient representation of haptic information to guide the dressing process and utilize it in the reward function to provide learning signals during training. In order to learn a prolonged sequence of motion involving a diverse set of manipulation skills, such as grasping the edge of the shirt or pulling on a sleeve, we find it necessary to separate the dressing task into several subtasks and learn a control policy for each subtask. We introduce a policy sequencing algorithm that matches the distribution of output states from one task to the input distribution for the next task in the sequence. We have used this approach to produce character controllers for several dressing tasks: putting on a t-shirt, putting on a jacket, and robot-assisted dressing of a sleeve.

The Tactical Language and Culture Training System (TLCTS) helps people quickly acquire communicative skills in foreign languages and cultures. More than 20,00 0 learners worldwide have used TLCTS courses. TLCTS utili zes artificial intelligence technologies in multiple wa ys: during the authoring process, and at run time to process l arner speech, interpret learner actions, control the resp on e of non-player characters, and evaluate and assess lear ner performance and proficiency. This paper describes the architecture of TLCTS and the artificial intelligence techn ologies that it employs, and presents results from multiple evaluation studies that demonstrate the benefits of learn ing foreign language and culture using this approach.

This paper describes quasi-online reinforcement learning: while a robot is exploring its environment, in the background a probabilistic model of the environment is built on the fly as new experiences arrive; the policy is trained concurrently based on this model using an anytime algorithm. Prioritized sweeping, directed exploration, and transformed reward functions provide additional speed-ups. The robot quickly learns goal-directed policies from scratch, requiring few interactions with the environment and making efficient use of available computation time. From an outside perspective it learns the behavior online and in real time. We describe comparisons with standard methods and show the individual utility of each of the proposed techniques


Question: Applying reinforcement learning to both conversants for the purpose of learning mixed initiative dialog strategies.

Exploring Mixed-Initiative Dialogue Dynamics: A Study on Discourse Control and Segmentation

Dialogue between individuals often exhibits a Mixed-Initiative pattern, characterized by the fluid transfer of conversational control between participants. This study investigates the discourse segmentation within four distinct dialogue sets, comprising a total of 1862 conversational turns, by applying a set of predefined rules for control transfer. Through the application of these control rules, we are able to extract domain-independent discourse structures, highlighting the significant role of initiative in shaping the discourse.

To further probe the interplay between control, initiative, and discourse processes, we analyze the distribution of four types of anaphora across two data sets. Our findings suggest a hierarchical relationship between certain control segments, reinforcing the notion that discourse participants frequently reach a mutual agreement on topic transitions. Moreover, we compare the dynamics of initiative in Task-Oriented and Advice-Giving dialogues, revealing stark differences in both the allocation of control and the method of control transfer between these dialogue types. These observed disparities can be elucidated through the lens of collaborative planning principles.

This analysis not only illuminates the intricate dynamics of mixed-initiative dialogues but also underscores the importance of understanding control transfer mechanisms in natural conversation. By recognizing these patterns, we can enhance the development of more sophisticated dialogue systems capable of engaging in nuanced, human-like conversations.

There have been several recent efforts to build behavior-based autonomous creatures. While competent autonomous action is highly desirable, there is an important need to integrate autonomy with “directability”. In this paper we discuss the problem of building autonomous animated creatures for interactive virtual environments which are also capable of being directed at multiple levels. We present an approach to control which allows an external entity to “direct” an autonomous creature at the motivational level, the task level, and the direct motor level. We also detail a layered architecture and a general behavioral model for perception and action-selection which incorporates explicit support for multi-level direction. These ideas have been implemented and used to develop several autonomous animated creatures.

Current dialogue systems generally operate in a pipelined, modular fashion on one complete utterance at a time. Evidence from human language understanding shows that human understanding operates incrementally and makes use of multiple sources of information during the parsing process, including traditionally “later” components such as pragmatics. In this paper we describe a spoken dialogue system that understands language incrementally, provides visual feedback on possible referents during the course of the user’s utterance, and allows for overlapping speech and actions. We further present findings from an empirical study showing that the resulting dialogue system is faster overall than its nonincremental counterpart. Furthermore, the incremental system is preferred to its nonincremental counterpart – beyond what is accounted for by factors such as speed and accuracy. These results indicate that successful incremental understanding systems will improve both performance and usability.

In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL). Despite the wide-spread application of deep RL techniques, learning generalized policy representations that work across domains remains a challenging problem. We demonstrate that textual descriptions of environments provide a compact intermediate channel to facilitate effective policy transfer. We employ a model-based RL approach consisting of a differentiable planning module, a model-free component and a factorized representation to effectively utilize entity descriptions. Our model outperforms prior work on both transfer and multi-task scenarios in a variety of different environments.

We discsus Hinton’s (1989) relative payoff procedure (RPP), a static reinforcement learning algorithm whose foundation is not stochastic gradient ascent. We show circumstances under which applying the RPP is guaranteed to increase the mean return, even though it can make large changes in the values of the parameters. The proof is based on a mapping between the RPP and a form of the expectation-maximisation procedure of Dempster, Laird & Rubin (1976).

This system demonstration paper presents IRIS (Informal Response Interactive System), a chat-oriented dialogue system based on the vector space model framework. The system belongs to the class of examplebased dialogue systems and builds its chat capabilities on a dual search strategy over a large collection of dialogue samples. Additional strategies allowing for system adaptation and learning implemented over the same vector model space framework are also described and discussed.

The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.

It has been proven that automatic conversational agents can be built up using the Endto-End Neural Response Generation (NRG) framework, and such a data-driven methodology requires a large number of dialog pairs for model training and reasonable evaluation metrics for testing. This paper proposes a Large Scale Domain-Specific Conversational Corpus (LSDSCC) composed of high-quality queryresponse pairs extracted from the domainspecific online forum, with thorough preprocessing and cleansing procedures. Also, a testing set, including multiple diverse responses annotated for each query, is constructed, and on this basis, the metrics for measuring the diversity of generated results are further presented. We evaluate the performances of neural dialog models with the widely applied diversity boosting strategies on the proposed dataset. The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably.

In this paper we discuss the recent evolution of spoken dialog systems in commercial deployments. Yet based on a simple finite state machine design paradigm, dialog systems reached today a higher level of complexity. The availability of massive amounts of data during deployment led to the development of continuous optimization strategy pushing the design and development of spoken dialog applications from an art to science. At the same time new methods for evaluating the subjective caller experience are available. Finally we describe the inevitable evolution for spoken dialog applications from speech only to multimodal interaction.

One of the key steps in creating quality interactive drama is the ability to create quality interactive characters (or believable agents). Two important aspects of such characters will be that they appear emotional and that they can engage in social interactions. My basic approach to these problems has been to use a broad agent architecture and minimal amounts of modeling of other agent in the environment. This approach is based on an understanding of the artistic nature of the problem. To enable agent-builders (artists) to create emotional agents, I provide a general framework for building emotional agents, default emotion-processing rules, and discussion about how to create quality, emotional characters. My framework gets a lot of its power from being part of a broad agent architecture. The concept is simple: the agent will be emotionally richer if there are more things to have emotions about and more ways to express them. This reliance on breadth has also meant that I have been able to create simple emotion models that rely on perception and motivation instead of deep modeling of other agents and complex cognitive processing. To enable agent builders to create social behaviors for believable agents, I have designed a methodology that provides heuristics for incorporating personality into social behaviors and suggests how to model other agents in the environment. I propose an approach to modeling other agents that calls for limiting the amount of modeling of other agents to that which is sufficient to create the desired behavior. Using this technique, I have been able to build robust social behaviors that use surprisingly little representation. I have used this methodology to build a number of social behaviors, like negotiation and making friends. I have built three simulations containing seven agents to drive and test this work. I have also conducted user studies to demonstrate that these agents appear to be emotional and can engage in non-trivial social interactions while also being good characters with distinct personalities.

Automated intelligent agents inhabiting a shared environment must coordinate their activities Cooperation not merely coordination may improve the performance of the individual agents or the overall behavior of the system they form Research in Distributed Arti cial Intelligence DAI addresses the problem of designing automated intelligent systems which interact e ectively DAI is not the only eld to take on the challenge of understanding cooperation and coordination There are a variety of other multi entity environments in which the entities coordinate their activity and cooperate Among them are groups of people animals particles and computers We argue that in order to address the challenge of building coordinated and collaborated intelligent agents it is bene cial to combine AI techniques with methods and techniques from a range of multi entity elds such as game theory operations research physics and philosophy To support this claim we describe some of our projects where we have successfully taken an interdisciplinary approach We demonstrate the bene ts in applying multi entity methodologies and show the adaptations modi cations and extensions necessary for solving the DAI problems This is an extended version of a lecture presented upon receipt of the Computers and Thought Award at the th International Joint Conference on Arti cial Intelligence in Montreal Canada August