THE INFLUENCE OF SOLOMO MARKETING ON OFFLINE BUYING

[[abstract]]SoLoMo marketing is emerging as a new marketing campaign to approach to the target consumers and would be widely used to increase consumers’ campaign response. Due to the operational differences in consumer behavior between traditional marketing and mobile marketing, the influences of SoLoMo marketing for retailers and what factors influencing the response of SoLoMo are worthy of study. This study investigates the effects of “Restaurant Locator” APP, which offer local-based push message and word-of-mouth mobile service. Snowball Sampling was used in this survey to locate smartphone user who is also a “Restaurant Locator” APP user. An online survey was adopted and 156 valid questionnaires were collected. Analytical results indicate that consumers would increase their restaurant purchase intension by SoLoMo campaigns and restaurant’s e-WOM. The trust of mobile APP social platform can influence the willing of using LBS discount message and the usage of APP’s Restaurant e-WOM. Furthermore, the characteristics of App social platform perceived by consumers can substantially influence consumer’s response to SoLoMo campaign.[[conferencetype]]國際[[conferencedate]]20150212~20150214[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]雪梨, 澳

Understanding Consumer Sentiments: Exploring the Role of Artificial Intelligence in Marketing

This research article presents a comprehensive review of the industrial opportunities of AI in marketing. The study begins with an introduction highlighting the significance of AI in marketing and its transformative potential. The literature review focuses on three key aspects: understanding consumer sentiments, market insights, and the use of AI in marketing. The review highlights the role of AI in understanding consumer sentiments through sentiment analysis, enabling marketers to gain valuable market insights. It explores the industrial opportunities of AI, including personalized marketing, predictive analytics, and customer segmentation. Additionally, the review discusses the use of AI in enhancing customer satisfaction, improving electronic word-of-mouth insights, and measuring market performance. The research methodology involved a systematic review of academic articles, industry reports, and conference papers. The findings reveal that AI-driven market sentiment analysis uncovers significant patterns, trends, and correlations in consumer sentiment data. This enables marketers to make data-driven decisions and develop effective marketing strategies. The implications of these findings for marketing strategies and decision-making processes are discussed. In conclusion, this research emphasizes the industrial opportunities of AI in marketing and provides insights for practitioners and researchers. Leveraging AI technologies can enhance market insights, improve customer satisfaction, and optimize marketing performance. The study contributes to the understanding of AI's application in marketing and serves as a foundation for future research. Marketers are encouraged to embrace AI to gain a competitive edge in the evolving marketing landscape

Enhancing travel recommendations: Ai-driven personalization through user digital footprints

Esta tesis tiene como objetivo examinar la manera en que la huella digital que dejan los usuarios en internet puede utilizarse para optimizar la personalización de los servicios turísticos, mediante el uso de inteligencia artificial. El documento propone que el auge de la inteligencia artificial ha abierto un mundo de oportunidades para desarrollar nuevas herramientas para mejorar la experiencia de viaje digital. El enfoque se basa en la idea de que las huellas digitales son únicas y particulares de cada individuo y estos valiosos datos pueden dar lugar a sugerencias de viaje más inteligentes y certeras. Se consideran las actitudes de comportamiento del usuario, como la influencia del contenido generado por el usuario en las redes sociales y el boca a boca electrónico en el proceso de planificación del viaje, así como las implicaciones de este rastro de datos en la optimización de los servicios de viaje personalizados. Este modelo describe la relación entre la inteligencia artificial y la hiper personalización de servicios. Como es una tendencia creciente que está alterando nuestra realidad actual, la tesis presentada desarrolla una aplicación de viajes a medida que, con el permiso del usuario, aprovecha los datos recopilados de las redes sociales personales para construir un plan de viaje específico basado en las preferencias individuales.This thesis aims to examine the way the digital footprint users leave behind can be utilized to optimize the personalization of tourism services, through the use of artificial intelligence. The paper proposes that the surge of artificial intelligence has opened a world of opportunities to develop new tools to improve the digital travel experience. The approach is based on the idea that digital footprints are unique and particular to each individual and this valuable data can result in smarter and unerring travel suggestions. Behavioral attitudes of the user, such as the influence of user-generated content in social media and e-word of mouth in the travel planning process, are considered, as well as the implications of this data trail in the optimization of customized travel services. This model describes the relationship between artificial intelligence and hyper-personalization of services. As it is a growing trend that is disrupting our current reality, the presented thesis develops a tailor-made traveling application that, with permission of the user, leverages the data collected from personal social media to build a specific travel plan based on each user’s preferences

All that Glitters is not Gold: Understanding the Impacts of Platform Recommendation Algorithm Changes on Complementors in the Sharing Economy

Sharing platforms often leverage recommendation algorithms to reduce matching costs and improve buyer satisfaction. However, the economic impacts of different recommendation algorithms on the business operations of complementors remains unclear. This study uses natural quasi-experiments and proprietary data from a home-cooked food-sharing platform with two recommendation algorithms: word-of-mouth recommendation (WMR) and botler personalization recommendation (BPR). Results show the WMR negatively affects revenue while BPR has a positive effect. The contrast revenue effects have been attributed to capacity constraints for complementors and matching frictions for consumers. WMR encourages sellers to specialize in high-quality products but limits new product development. BPR promotes innovation to suit diverse customer tastes but may reduce quality. This reflects the exploration-exploitation trade-off: WMR exploits existing competences, while BPR explores new products to satisfy personal preferences. The authors discuss implications for how to utilize recommendation algorithms and artificial intelligence for the prosperity of sharing economy platforms

INDIVIDUALITY OR CONFORMITY: RECOMMENDATION EXPLOITING COMMUNITY-LEVEL SOCIAL INFLUENCE

With the increasing prevalence of online businesses and social networking services, a huge volume of data about transaction records and social connections between users is accumulated at an unprecedented speed, which enables us to take advantage of electronic word-of-mouth effect embedded in social networks for precision marketing and social recommendations. Different from existing works on social recommendations, our research focuses on discriminating the community-level social influence of different friend groups to enhance the quality of recommendation. To this end, we propose a novel probabilistic topic model integrating community detection with topic discovery to model user behaviors. Based on this model, a recommendation method taking both individual interests and conformity influence into consideration is developed. To evaluate the performance of the proposed model and method, experiments are conducted on two real recommendation applications, and the results demonstrate that the proposed recommendation method exhibits superior performance compared with the state-of-art recommendation methods, and the proposed topic model exhibits good explainablibity of topic semantics and community interests. Furthermore, as some people are more individual interest oriented and some are more conformity oriented demonstrated by the experiments, we explore factors that influence each individual’s conformity tendency, and obtain some meaningful findings

DIGITAL WINE: HOW PLATFORMS AND ALGORITHMS WILL RESHAPE THE WINE INDUSTRY

La tesi si propone di analizzare come la digitalizzazione e gli approcci basati sui dati, in particolare quelli che sfruttano l'intelligenza artificiale, stiano impattando il settore vitivinicolo e facendo emergere modelli nuovi di business. Quest'ultimo aspetto sarà approfondito tramite due casi studio di piattaforme digitali che, attraverso approcci diversi, stanno contribuendo a generare un ecosistema digitale virtuoso, con potenziali benefici per tutta la catena del valore a livello di settore.The thesis aims to analyze how digitalization and data-driven approaches, in particular those that leverage artificial intelligence, are impacting the wine industry and generating new business models. The latter aspect will be explored through two case studies of digital platforms which, through different approaches, are helping to generate a virtuous digital ecosystem, with potential benefits for the entire value chain at the industry level

Pal: Building Trust in High Stakes Service Provider Relationships

The rise of on-demand economies has forced consumers to increasingly rely on evaluating service providers through a digital medium. Research has found that Americans’ trust in others has declined over the past several decades, while their trust in strangers from on-demand sharing economies is contrastingly high. In many cases however, the trust goes too far, resulting in negative or even dangerous experiences for both the service provider and customer. One area where digital trust is being widely implemented is in online marketplaces for pet care services. Popular sites and mobile applications like Rover and Wag provide on-demand dog walking and pet sitting services for pet owners. Even with their vetting and background check process for walkers and sitters and review and ratings system, there have been innumerable accounts of pet owner’s negative experiences, such as lost dogs, injured pets, and household theft. This brings into question whether existing digital ranking systems are trustworthy enough for pets, which are important parts of their owners’ lives. This thesis investigates how to more effectively build trust in high stakes service-provider relationships. Through initial research, it was discovered that it is difficult to build trust and credibility when searching digitally for pet care service providers because of a lack of personalized information and social networks of recommendations. In order to solve this problem, several designs were explored in order to determine the best way to present specific aspects of the service provider profile in the form of a mobile application. Specifically, many design explorations focused on the idea of exploring recommendations of user’s real-world connections through the mobile application

Setting the Future of Digital and Social Media Marketing Research: Perspectives and Research Propositions

in pressThe use of the internet and social media have changed consumer behavior and the ways in which companies conduct their business. Social and digital marketing offers significant opportunities to organizations through lower costs, improved brand awareness and increased sales. However, significant challenges exist from negative electronic word-of-mouth as well as intrusive and irritating online brand presence. This article brings together the collective insight from several leading experts on issues relating to digital and social media marketing. The experts' perspectives offer a detailed narrative on key aspects of this important topic as well as perspectives on more specific issues including artificial intelligence, augmented reality marketing, digital content management, mobile marketing and advertising, B2B marketing, electronic word of mouth and ethical issues therein. This research offers a significant and timely contribution to both researchers and practitioners in the form of challenges and opportunities where we highlight the limitations within the current research, outline the research gaps and develop the questions and propositions that can help advance knowledge within the domain of digital and social marketing.Peer reviewe

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

Local Popularity: A Double-edged Tool in Platform Operation

Although displaying local popularity is wildly adopted by major platforms, the actual effect of such information cues on motivating users has not been documented. Findings from a field experiment suggest that local popularity effectively motivates users to invite more friends but surprisingly reduces users’ self-participation. Social conformity theory may account for such effects: local information encourages users to invite their local friends, but such effect is limited to users from small cities since users in a relatively small community are more bonded and less likely to reject the invitation due to social pressure. Meanwhile, local information attenuates the power of popularity (e.g., fewer registered users in the local area) and ultimately discourages users\u27 self-participation. This study deepens our understanding of displaying popularity cue in improving platform operation, based on which we suggest that practitioners should be cautious about the persuasive power of such information cues in location-based marketing