Some Observations on Multi-Agent Based Negotiation in B2C E-Commerce

Multi-agent based negotiation is the emergent functionality of E-Commerce. There are several approach deployed by various researcher in the B2C, E-Commerce model. In this research paper we provide some observations on various negotiation mechanism which are deployed in various E-Commerce mode

The Agent Pattern Driven Business Engineering (APBDE) approach enabled business-based systems

Agent design patterns form a new methodology used to improve the development of software agents. Agent design patterns can help by capturing solutions to common problems in agent design [Lange and Oshima, 1998].Agent design patterns are applied in different systems such as knowledge management systems, real-time systems, and network management systems. Agent design patterns for business-based systems, aim to support different ecommerce paradigms business-to-business (B2B) and business-to-consumer (B2C).In this paper, we developed an approach for extracting agent-based design patterns for B2C e-commerce to improve business-based processes.This approach is called an Agent Pattern Driven Business Engineering (APDBE).Based on this approach, we derived two agent-based commerce design patterns namely, the De-coupler Design Pattern (DecDP), and the Dynamic Design Pattern (DynDP). These design patterns are used to support selling/buying-based processes in e-commerce domain

Reaching inter-institutional business processes in e-Society

Each business enterprise strives to achieve the most efficient organization of its operations. While business enterprises can influence internal factors of organization, external factors are more rigid. Public organizations have less of an incentive to be efficient. Furthermore, their organization is less favorable since the decision making is centralized and highly formal (i.e. legislative). Adoption of business process orientation (BPO) paradigm,with an emphasis on the management of internal factors of organization, has provided business organizations with substantial savings and improvements in efficiency. However, external factors also have a high potential for improvement of efficiency. For instance, development of supply chains or value chains has proven that external factors can be harnessed to provide additional sources of competitiveness. Other external factors can also beused to improve the performance of individual organizations, an entire industry or economy as a whole. These synergic effects can be achieved through a unified and virtualized communication infrastructure, document exchange and conduct of business transactions. The goal of this paper is to present business environment properties in an e-Society that can be further developed to enhance integration between organizations and public institutions, which in turn can be used to create and manage inter-institutional business processes. This typeof processes can promote e-business and e-business models to a new level of efficiency, making a whole industry or national economy comparatively more competitivein international markets.business processes; public administration; e-business; e-society; interactions

Do we bank on regulation or reputation? A meta-analysis and meta-regression of organizational trust in the financial services sector

Purpose: – Trust in financial institutions has been eroded through the collapse of mortgage-related securities, with confidence further denuded through well publicized cases of rogue traders and rate fixing cases, such as with the Lehman brothers, the Libor rate-fixing scandals, and the hypo real estate breakdown. In response to these events, governments have introduced a range of distinct policy initiatives designed to restore trust in this sector. Thus, the question arises: are these regulations and control mechanisms sufficient in isolation, or are there other elements that this sector needs to pay attention to in efforts to build and sustain customers’ trust? The paper aims to discuss these issues. Design/methodology/approach: – There is a compelling agenda for both financial organizations and academics to understand better organizational trust in this context especially the role and impact of regulatory mechanisms in its development and repair. The paper therefore examines the special facets of the financial services sector in comparison to other sectors, such as manufacturing, to consider whether trust is fundamentally different in this context than others, and thus address how far there are special challenges concerning trust and the banking industry. The paper analyses, by using a meta-analytical design, 93 studies (N=38,631), of which 20 empirically investigate organizational trust in the financial sector with a combined N of 11,224 respondents. Findings: – The paper shows that the banking sector is heavily affected by two distinct forces: first, customers’ perception of an organization's level of compliance and conformity with laws and regulations is a necessity for banks’ sociopolitical legitimization, and second it is also related to how non-compliance is dealt with. Importantly, this meta-analysis indicates that regulation is just one of a suite of devices that organizations need to deploy in their efforts to restore trust. The paper identified two further elements of significance: customers require direct evidence, derived either from their own or others’ satisfaction with the goods or services provided, and customers do take note of the external endorsement of the firm, especially in Asia, where customers place huge emphasis on the firm's reputation. Research limitations/implications: – First, meta-analysis is inherently reliant on the earlier studies and therefore retains their weaknesses. Some of the relationships included self-report variables collected at the same point in time and therefore may be inflated by common method bias. Second, due to the focus and because of the limited number of studies in this sector, and a paucity of attention on some key topics, such as perceptions of regulation, second-order sampling error may also be a limitation. Third, some relationships were not investigated frequently enough in studies to enable us to include them in the review, such as cooperation, opportunistic behaviour or quality. Finally, despite calls for trust scholars to include propensity to trust measures within their studies, many of these studies do not include this measure and therefore it is more difficult to identify and control individual difference factors. Practical implications: – The results show the merit of multi-strand trust development strategies. There is a striking paucity of financial institutions, which have examined how far their trust deficit may be related to their internal culture, and whether recent corporate corruption could be the product of bonuses and the internal short-term individualized reward systems. The analysis reveals that although external regulations and controls are an effective and powerful devise for organizational trust, over the last two periods of significant crisis, their impact appears to be warning; Yet reassuring customers of their expectations of the other party's future behaviour is central to trust. Alternative remedies need to be considered, such as the establishment of a more effective regulator, or board of governors who oversee and assure compliance. Monitoring and surveillance offer a further external means of reducing the possibility of future misbehaviours. However, as the analysis indicates, other strands are required to build trust, including greater attention by firms on customers’ direct experiences, which in turn would enhance the third part endorsement of their competence and goodwill intentions of organizations. Social implications: – Significantly, the results indicate the potentially partial erosion of credence factors, and thus confidence, in this sector over the last 20 years, during what has been a period of repeated exposure to trust breaches. The paper shows that single strand solutions, such as improvements to customer communication, are no longer sufficient, nor, more importantly, do they have the same impact. Instead, the paper shows the necessity to utilize more effectively and target attention towards three distinct antecedents: external regulations and their enforcement; third party and expert endorsements, and therefore external reputations; and customer satisfaction in terms of the effective delivery of customer expectations. Originality/value: – Organizational trust has been shown as critical in positively affecting and repairing broken relationships through uncertainty reduction and confidence enhancement. In the past, different meta-analyses of trust have been undertaken, but this, to the authors knowledge, is the first meta-analytic study measuring trust on an organizational level in the context of the financial services sector and its regulatory environment. This meta-analysis indicates that regulation is just one of a suite of devices that organizations need to deploy in their efforts to restore trust. The paper identified two further elements: customers require direct evidence, and do take note of the external endorsement of the firm

Research Framework, Strategies, And Applications Of Intelligent Agent Technologies (IATs) In Marketing

In this digital era, marketing theory and practice are being transformed by increasing complexity due to information availability, higher reach and interactions, and faster speeds of transactions. These have led to the adoption of intelligent agent technologies (IATs) by many companies. As IATs are relatively new and technologically complex, several definitions are evolving, and the theory in this area is not yet fully developed. There is a need to provide structure and guidance to marketers to further this emerging stream of research. As a first step, this paper proposes a marketing-centric definition and a systematic taxonomy and framework. The authors, using a grounded theory approach, conduct an extensive literature review and a qualitative study in which interviews with managers from 50 companies in 22 industries reveal the importance of understanding IAT applications and adopting them. Further, the authors propose an integrated conceptual framework with several propositions regarding IAT adoption. This research identifies the gaps in the literature and the need for adoption of IATs in the future of marketing given changing consumer behavior and product and industry characteristics

Research Framework, Strategies, And Applications Of Intelligent Agent Technologies (IATs) In Marketing

In this digital era, marketing theory and practice are being transformed by increasing complexity due to information availability, higher reach and interactions, and faster speeds of transactions. These have led to the adoption of intelligent agent technologies (IATs) by many companies. As IATs are relatively new and technologically complex, several definitions are evolving, and the theory in this area is not yet fully developed. There is a need to provide structure and guidance to marketers to further this emerging stream of research. As a first step, this paper proposes a marketing-centric definition and a systematic taxonomy and framework. The authors, using a grounded theory approach, conduct an extensive literature review and a qualitative study in which interviews with managers from 50 companies in 22 industries reveal the importance of understanding IAT applications and adopting them. Further, the authors propose an integrated conceptual framework with several propositions regarding IAT adoption. This research identifies the gaps in the literature and the need for adoption of IATs in the future of marketing given changing consumer behavior and product and industry characteristics

E-COMMERCE: A NEW BUSINESS MODEL FOR THE FOOD SUPPLY/DEMAND CHAIN

The use of electronic commerce for quality control and cost cutting efficiencies by the food and agricultural industries in the United States is the focus of this paper. The food industry engages in e-commerce through 1.) Internet shopping for consumers called business-to-consumer (B2C) e-commerce 2.) Business-to-business (B2B) Internet market discovery exchanges used by food suppliers at any point in the supply chain, and 3.) Business-to-business (B2B) relationships that reduce costs and increase efficiencies in the procurement, storage and delivery of food to retail stores or distribution centers. This third use of e-commerce is the most highly developed and widely adopted. It allows retailers to share information about consumers' purchases and preferences with food manufacturers and farmers and for tracking food products' characteristics, source, and movement from production to consumer. This circle of information allows high quality and consistent products to be consumed at lower prices. This paper is about the development of e-commerce in the food industry, the economic concepts and goals that it meets, and the changes it brings to the industry. E-commerce both fosters and demands vertical coordination. It favors consolidation of firms. It changes the business culture from one of adversarial relationships to one of cooperation and trust. It changes the historical supply chain into a supply/demand loop while it lowers the cost of food. Policy issues arise around monopoly power, privacy, a diminution of variety, and the demise of small, undercapitalized firms.Industrial Organization, Marketing,

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

[EN] In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can navigate and interact between each other in a more safe way.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis. Applied Sciences. 10(19):1-29. https://doi.org/10.3390/app10196746S1291019Vanderhoven, E., Schellens, T., Vanderlinde, R., & Valcke, M. (2015). Developing educational materials about risks on social network sites: a design based research approach. Educational Technology Research and Development, 64(3), 459-480. doi:10.1007/s11423-015-9415-4Teens and ICT: Risks and Opportunities. Belgium: TIRO http://www.belspo.be/belspo/fedra/proj.asp?l=en&COD=TA/00/08Risks and Safety on the Internet: The Perspective of European Children: Full Findings and Policy Implications From the EU Kids Online Survey of 9–16 Year Olds and Their Parents in 25 Countries http://eprints.lse.ac.uk/33731/Vanderhoven, E., Schellens, T., & Valcke, M. (2014). Educating teens about the risks on social network sites. An intervention study in Secondary Education. Comunicar, 22(43), 123-132. doi:10.3916/c43-2014-12Christofides, E., Muise, A., & Desmarais, S. (2012). Risky Disclosures on Facebook. Journal of Adolescent Research, 27(6), 714-731. doi:10.1177/0743558411432635George, J. M., & Dane, E. (2016). Affect, emotion, and decision making. Organizational Behavior and Human Decision Processes, 136, 47-55. doi:10.1016/j.obhdp.2016.06.004Thelwall, M. (2017). TensiStrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1), 106-121. doi:10.1016/j.ipm.2016.06.009Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558. doi:10.1002/asi.21416Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020). Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals. Journal of Network and Computer Applications, 149, 102447. doi:10.1016/j.jnca.2019.102447Zhang, C., Zeng, D., Li, J., Wang, F.-Y., & Zuo, W. (2009). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 60(12), 2474-2487. doi:10.1002/asi.21206Lu, B., Ott, M., Cardie, C., & Tsou, B. K. (2011). Multi-aspect Sentiment Analysis with Topic Models. 2011 IEEE 11th International Conference on Data Mining Workshops. doi:10.1109/icdmw.2011.125Nasukawa, T., & Yi, J. (2003). Sentiment analysis. Proceedings of the international conference on Knowledge capture - K-CAP ’03. doi:10.1145/945645.945658Borth, D., Ji, R., Chen, T., Breuel, T., & Chang, S.-F. (2013). Large-scale visual sentiment ontology and detectors using adjective noun pairs. Proceedings of the 21st ACM international conference on Multimedia - MM ’13. doi:10.1145/2502081.2502282Deb, S., & Dandapat, S. (2019). Emotion Classification Using Segmentation of Vowel-Like and Non-Vowel-Like Regions. IEEE Transactions on Affective Computing, 10(3), 360-373. doi:10.1109/taffc.2017.2730187Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013). Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. doi:10.1109/acii.2013.90Nicolaou, M. A., Gunes, H., & Pantic, M. (2011). Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space. IEEE Transactions on Affective Computing, 2(2), 92-105. doi:10.1109/t-affc.2011.9Hossain, M. S., Muhammad, G., Alhamid, M. F., Song, B., & Al-Mutib, K. (2016). Audio-Visual Emotion Recognition Using Big Data Towards 5G. Mobile Networks and Applications, 21(5), 753-763. doi:10.1007/s11036-016-0685-9Zhou, F., Jianxin Jiao, R., & Linsey, J. S. (2015). Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews. Journal of Mechanical Design, 137(7). doi:10.1115/1.4030159Ceci, F., Goncalves, A. L., & Weber, R. (2016). A model for sentiment analysis based on ontology and cases. IEEE Latin America Transactions, 14(11), 4560-4566. doi:10.1109/tla.2016.7795829Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886. doi:10.1016/j.ijhcs.2009.07.005Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. doi:10.1145/2436256.2436274Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. doi:10.1109/tkde.2015.2485209Ji, R., Cao, D., Zhou, Y., & Chen, F. (2016). Survey of visual sentiment prediction for social media analysis. Frontiers of Computer Science, 10(4), 602-611. doi:10.1007/s11704-016-5453-2Li, L., Cao, D., Li, S., & Ji, R. (2015). Sentiment analysis of Chinese micro-blog based on multi-modal correlation model. 2015 IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip.2015.7351718Lee, P.-M., Tsui, W.-H., & Hsiao, T.-C. (2015). The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. PLOS ONE, 10(6), e0129056. doi:10.1371/journal.pone.0129056Matsiola, M., Dimoulas, C., Kalliris, G., & Veglis, A. A. (2018). Augmenting User Interaction Experience Through Embedded Multimodal Media Agents in Social Networks. Information Retrieval and Management, 1972-1993. doi:10.4018/978-1-5225-5191-1.ch088Rosaci, D. (2007). CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents. Information Systems, 32(6), 793-825. doi:10.1016/j.is.2006.06.003Buccafurri, F., Comi, A., Lax, G., & Rosaci, D. (2016). Experimenting with Certified Reputation in a Competitive Multi-Agent Scenario. IEEE Intelligent Systems, 31(1), 48-55. doi:10.1109/mis.2015.98Rosaci, D., & Sarnè, G. M. L. (2014). Multi-agent technology and ontologies to support personalization in B2C E-Commerce. Electronic Commerce Research and Applications, 13(1), 13-23. doi:10.1016/j.elerap.2013.07.003Singh, A., & Sharma, A. (2017). MAICBR: A Multi-agent Intelligent Content-Based Recommendation System. Lecture Notes in Networks and Systems, 399-411. doi:10.1007/978-981-10-3920-1_41Villavicencio, C., Schiaffino, S., Diaz-Pace, J. A., Monteserin, A., Demazeau, Y., & Adam, C. (2016). A MAS Approach for Group Recommendation Based on Negotiation Techniques. Lecture Notes in Computer Science, 219-231. doi:10.1007/978-3-319-39324-7_19Rincon, J. A., de la Prieta, F., Zanardini, D., Julian, V., & Carrascosa, C. (2017). Influencing over people with a social emotional model. Neurocomputing, 231, 47-54. doi:10.1016/j.neucom.2016.03.107Aguado, G., Julian, V., Garcia-Fornes, A., & Espinosa, A. (2020). A Multi-Agent System for guiding users in on-line social environments. Engineering Applications of Artificial Intelligence, 94, 103740. doi:10.1016/j.engappai.2020.103740Aguado, G., Julián, V., García-Fornes, A., & Espinosa, A. (2020). Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks. Applied Sciences, 10(11), 3754. doi:10.3390/app10113754Camara, M., Bonham-Carter, O., & Jumadinova, J. (2015). A multi-agent system with reinforcement learning agents for biomedical text mining. Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics. doi:10.1145/2808719.2812596Lombardo, G., Fornacciari, P., Mordonini, M., Tomaiuolo, M., & Poggi, A. (2019). A Multi-Agent Architecture for Data Analysis. Future Internet, 11(2), 49. doi:10.3390/fi11020049Schweitzer, F., & Garcia, D. (2010). An agent-based model of collective emotions in online communities. The European Physical Journal B, 77(4), 533-545. doi:10.1140/epjb/e2010-00292-

The Evolution of Social Commerce: The People, Management, Technology, and Information Dimensions

Social commerce is a form of commerce mediated by social media and is converging both online and offline environments. As a relatively new phenomenon, social commerce has evolved quickly in practice, yet has gained little attention in the IS discipline. With its pervasiveness in businesses and people’s lives, social commerce presents ample research opportunities that can have both theoretical and practical significance and implications. This article aims to capture researchers’ attention by describing the characteristics of social commerce and its potential future directions. We trace the evolutionary patterns of social commerce chronologically, based on trade articles and academic publications from 2005 to 2011. A framework that combines people, management, technology, and information dimensions is used to provide a systematic analysis of social commerce development. Our examination shows that since 2005, the year the term social commerce was incepted, assumptions and understanding of people in social commerce move from a simple and general description of human social nature to a rich exploration with different angles from social psychology, social heuristics, national culture, and economic situations. On the management dimension, business strategies and models evolve from the short-tail to long-tail thinking, with invented concepts such as branded social networks/communities, niche social networks/communities, niche brands, co-creating, team-buying, and multichannel social networks. Technologically, IT platforms and capabilities for social commerce evolve from blogs, to social networking sites, to media-sharing sites, and to smartphones. While Facebook becomes a profit-generating platform, creating the notion of f-commerce, Google and Twitter become strong competitors with great potentials. Information in social commerce evolves from peer-generated, to community-generated (crowdsourcing), to consumer and marketer co-created, and to global crowdsourced. Our examination identifies various conceptualizations, terminologies, views, and perspectives about social commerce and its relation to other well-known concepts such as e-commerce. In light of the evolution of social commerce, we provide possible future directions for research and practice