Fast-paced advancements in technology demand swift adaptation and presents new opportunities and challenges for the optimization of communication, especially for advertisers. Digitalization and new developments in ICT have brought significant changes to the ways in which information, especially promotional messages, is disseminated to consumers. Additionally, with explosive interests in anticipation of fully autonomous vehicles, this study identifies and addresses the potential to optimize communication in an under examined digital media environment – in-vehicle infotainment system. Therefore, this study proposes a text-image embedding method recommender system for the personalization of multimedia contents and advertisements for in-vehicle infotainment systems. Unlike most previous research, which focuses on textual-only or image-only analyses, the current study explores the understanding, development and application of text embedding models and image feature extraction methods simultaneously in the context of target advertisement research. Overall, this study highlights the need to adapt to the ever-evolving technological landscape to optimize communication in various digital media environments. With the proposed text-image embedding method, this study offers a unique approach to personalizing multimedia content and advertisements in the under-explored digital media environment of in-vehicle infotainment systems.

This research intends to examine whether these types of data – behavioral versus social networking data – affect consumer response to personalized ads. With the advancement in technology, marketers have access to various types of personal data, including their online/offline behaviors and social networking activities, and use those data to retarget consumers. Moreover, this research examines the moderating role of SNS privacy concerns on consumer response to different types of retargeted ads. The findings of this research may offer theoretical and practical implications to understand consumers' responses to retargeted ads that use social networking information.

Consumers may not be fully aware of the level of personalization used in brand placements in digital advertising. Personalized brand placements use customer data to select and insert preferred brands into digital media content, such as having Coke appear in a YouTube video for someone who favors Coca-Cola. This personalization can enhance the consumer experience (Trifts & Aghakhani, 2018) but may also backfire if placements don't account for changes in brand preference or when personalized placements are co-consumed with a person whose preferences strongly diverge from one’s own (e.g., one’s romantic partner; Brick, Fitzsimons, Chartrand, & Fitzsimons, 2018). Brands have a significant impact on how people perceive each other. Brands convey social information and symbolic meaning (Keller, 1993; Levy, 1959) and can influence behavior (e.g., Fitzsimons, Chartrand, & Fitzsimons 2008) and provide a source of affiliation (Escalas & Bettman, 2003). However, less is known about how brands affect basic social connections like empathy. This notable gap is relevant to the evaluation of the impact of brand placements in digital advertising as brand logos are quite visible in brand placements and can be the first piece of information someone observes when forming an attitude towards another person. For example, imagine an Instagram post of a person wearing a Harley Davidson baseball cap. What kind of person did you imagine? Does your impression change if, instead, the brand was People for the Ethical Treatment of Animals, or the Trump Organization? Moreover, how likely would you be to engage with media showcasing a person supporting this brand. In the current research, we propose that personally disliked brands create a negative bias in person perception and consumer judgment, a phenomenon we coin “brand negativity bias.” Our results show that disliking a brand can reduce empathy for others and substantially bias attitudes toward a digital product associated with but not created by the brand. Specifically, we show process evidence that suggests disliked brand placements reduce (1) empathy toward others through reductions in perceived similarity and (2) attitudes toward a digital product through perceived similarity and empathy, as serial processes. In addition, although we do not empirically test an overall mechanism that explains the negativity bias in its entirety, we speculate several reasons that underlie the broad influence of personally disliked brands (e.g., symbolic, ideological, or moral). From a societal point of view, our findings inform the public to be mindful of how simple consumption cues can bias people’s decision making, which holds implications beyond consumer judgment in domains involving first impressions.

Personalizing banner ads or embedding ads with specific data signals or triggers, such as – personal characteristics, past behaviours, etc. is believed to improve customer response or Click Through Rate (CTR) since, embedding ads with viewers/recipients’ personal data or characteristics make ads more appealing and relevant to users (Lambrecht & Tucker, 2013). However, evidence also exists in literature that personalization can be ineffective as the usage of customers’ personal data can trigger off privacy concerns causing them to ignore such ads (van Doorn & Hoekstra, 2013). Investigations exploring suitability and effectiveness of ad personalization report that factors such as advertised product, data used for personalizing may influence the effectiveness of personalized ads (De Keyzer, Dens, & De Pelsmacker, 2015; Goldfarb & Tucker, 2011; van Doorn & Hoekstra, 2013). In this research we examine the impact of personalization triggers (PTs) on click through rate (CTR) of online banner ads across cultures. CTR data for 1345 unique ad copies (personalized) of an international hotel group screened in Japan and Middle East countries was used for this study. Data analyses revealed significant impact of PTs on CTRs. Analyses further revealed that – 1) usage of past purchase data impacts the CTR negatively, implying that customers respond negatively to ads showing hotel properties that they have previously visited/stayed in; 2) usage of search history data has a significant positive impact on CTR, suggesting that customers respond favourably towards ads showing hotel properties in destinations revealed from their search history. Interestingly, culture specific data such as local language elicit different responses in different cultures. While in Japan, language personalised ads i.e., ads in Japanese language fared poorly (negative impact on CTR) as compared to ads in English language (positive impact on CTR); in the Middle East it was the ads in English language that fared poorly (negative impact on CTR) compared to ads in Arabic. These results strongly suggest that the knowledge of PTs influence CTR and combining them with the right creative elements would help advertisers in improving customer engagement with ads, have a positive impact on CTR and even improve customer conversion. This would imply better returns for the resources spent on digital advertising. Findings from the study are true and reflective of the PTs (membership, brand affinity, destination, language) used in the ad campaign under study and limited to the cultures investigated. Future studies exploring other PTs in online hotel ads would help marketers in making a more informed decision while selecting data signals or PTs for personalizing digital banner ads for hotel brands.

In this research, we explore the role of interpersonal personalization in brand offerings in engendering the representations of ‘which dimension’ of brand personality (Aaker, 1997) and the subsequent effect on consumer attitude toward and connection with the brand (Fournier, 1998). Experiment 1 reveals that interpersonal personalization is different from customization with unique benefit in developing sincerity in brand personality. It demonstrates the positive effect of personalization on brand attitude through formulation of a sincere brand. Experiment 2 provides empirical evidence that personalization encouraging consumer’s involvement in the product design process allows consumer’s greater engagement with and investment towards the brand, which then manifests favorable brand attitude. The findings of this research offer insights on how brands can take advantage of personalization practice in their product or service offerings to engage consumers, and subsequently develop a close relationship with them. This research makes an important contribution to the brand personality literature through identifying a specific marketing approach (i.e., interpersonal personalization) as an effective strategy in developing a specific type of brand personality (i.e., sincerity). In sum, this research offers new view on how to manage close relationship with consumers, which is a significant practical implication for marketing managers.

21세기 식품산업에서 소비자들의 개인화 (Personalization)와 주문 제작(Customization)에 대한 요구는 계속 증가하고 있다. 특히, millennial 세대가 경제 주체로서 활동하는 비율이 증가함에 따라, ‘나를 위하여 제작된 개인화 제품’을 찾는 경향은 더욱 늘어날 것이며, 이 경향은 nutrigenetics와 nutrigenomics에 기반한 개인화 영양 (personalized nutrition)의 영역에서도 증가하는 추세를 보이고 있다. 반면 현재의 대형식품회사들은 여전히 전통적인 대량 생산/ 대량 유통 시스템에 머물러 있고 효율적인 massive customization 을 구현하지 못하고 있어 시대의 변화에 매우 제한적인 대응밖에는 하고 있지 못한 상황이다.. Millennial 세대의 구매 트렌드 변화를 놓치지 않고 소비자의 기대에 제대로 부응하기 위하여 현재 활발한 연구가 진행 중인 4차 산업혁명 관련 미래기술과 융복합 기술들의 상업화 적용 속도가 빨라져야만 할 것이다.

수많은 정보의 홍수 속에서 소비자는 자신의 감성에 맞는 스타일을 선택하기 원하므로 개인화 서비스에 대한 효용과 필요성이 꾸준히 증가하고 있다. 본 연구에서는 적극적 개인화 쇼핑몰의 샘플 동영상을 직접 제작하여 인터넷 쇼핑몰 이용자 대학생 170명에 대하여 이를 경험하도록 하였고 이에 따라 소비자 반응이 변하는 것을 측정하였다. 또 소비자의 개인화의 수준에 따라 소비자의 웹사이트 평가, 만족도/인터넷 행동, 제품품질평가 등 반응도 조사하였다. 또한 이를 선호쇼핑몰의 종류, 인터넷 쇼핑몰 접속 횟수에 따라 차이가 존재하는지를 조사하였다. 결과는 첫째, 적극적 개인화 쇼핑몰을 이미 경험해본 대학생 소비자의 경우 적극적 개인화 수준이 높게 나타났다. 선호 쇼핑몰의 종류에 따라서 소극적 개인화 수준이 달랐는데, 일반의류 쇼핑몰을 선호하는 사람들이 낮고 복합대형쇼핑몰과 소셜커머스를 선호하는 사람들은 소극적 개인화 점수가 높았다. 둘째, 대학생 소비자의 적극적 개인화 동영상 경험 전후에 따라서 만족도/인터 넷행동, 제품품질 평가는 변화하지 않는 것으로 나타났으나, 소극적 개인화 점수는 감소하고 적극적 개인화 점수와 웹사이트 평가는 증가하는 것으로 나타났다. 셋째, 인터넷 쇼핑몰 접속 횟수는 만족도/인터넷행동, 웹사이트 평가와 정적 상관이 있는 반면에, 적극적 개인화 요소는 만족도/인터넷 행동, 웹사이트 평가와 부적 상관이 있었다.

본 연구는 다른 분야에서 성공적으로 활용되고 있는 다양한 추천 기법들을 비교하는 사례 연구를 통해 더욱 효과적인 디자인 개인화 서비스 개발의 기회를 모색하고자 하였다. 우선, 문헌연구를 통하여 '컨텐츠 기반 기법', '협력적 필터링 기법', 그리고 '인구통계적 필터링 기법'과 같은 대표적인 추천 기법들의 특징과 장단점을 고찰하였다. 다음으로 이러한 기법들이 디자인과 같은 컨텐츠를 대상으로 적용되었을 때 예상되는 추천 정확성을 분석하기 위해 실험을 실시하였다. 그 결과, 인구통계적 필터링 기법은 나머지 기법에 비해서 비교적 낮은 정확성을 보였으며 컨텐츠 기반 기법이 가장 좋은 높은 추천 정확성을 나타내었다. 아울러 협력적 필터링 기법은 참여자들의 수가 증가할수록 좀 더 높은 추천 정확성을 나타냄을 알 수 있었다. 결론적으로 디자인 추천 서비스는 컨텐츠 기반 기법이나 협력적 필터링 기법의 적용을 통해 그 추천 정확성을 향상시킬 수 있으며 대상 사용자의 수가 일정 수준 이상으로 증가된다면 협력적 필터링 기법이 가장 우수한 효율을 나타낼 가능성이 높음을 제시하였다.

Expectation and interest about e-CRM are rising for more efficient customer management in on-line including electronic commerce. The decision-making tree can be used usefully as the data mining technology for e-CRM. In this paper, the representative decision making techniques, CART, C4.5, CHAID analyzed the differences in personalization point of view with actuality customer data through an experiment. With these analysis data, it is proposed a new decision-making tree system that has big advantage in personalization techniques. Through new system, it can get following advantage. First, it can form superior model more qualitatively in personalization by adding individual's weight value. Second it can supply information personalized more to customer. Third, it can have high position about customer's loyalty than other site of similar types of business. Fourth, it can reduce expense that cost marketing and decision-making. Fifth, it becomes possible that know that customer through smooth communication with customer who use personalized service wants and make from goods or service's quality to more worth thing.

온라인 기업의 경우 쉽게 가질 수 있는 고객데이터는 고객이 자사의 홈페이지에 접속하여 남기고 간 흔적(Web Log)이나, 고객이 직접 제공하는 데이터 일 것이다. 현재 많은 온라인 기업이 가장 기본적인 분석으로 웹 로그 분석을 시행하고 있으나, 그 양이 너무 많아 수시로 처리하는데는 문제가 있기도 하지만, 가장 쉽고, 기본적인 분석임은 피할 수 없는 사실이다. 기존의 웹 로그 분석에 관한 연구들이 웹 로그 분석을 통하여 사용자패턴 분석에 그친대 비하여