A number of prior studies have suggested that personalization is more efficacious than non-personalization. However, the existing literature is somewhat ambiguous on whether the test of ...personalization effects should be based on a message sender's actual personalization process or a message recipient's perception of the message. It is argued in this article that an actual personalization process does not automatically yield more favorable effects because people's perceptions of personalized messages tend to be biased. Through three experiments, it is demonstrated that testing personalization effects based on a message sender's actual personalization process can be problematic and produce misleading results. Specifically, a personalized message can be perceived as non-personalized and a non-personalized message can be perceived as personalized. The key finding is that perceived personalization, instead of actual personalization, is the underlying psychological mechanism of message effectiveness. A message will show superior effects when it is perceived to be personalized by a message recipient, regardless of whether it is actually personalized or not.
•Personalized messages are not always more effective than generic messages.•Actual personalization and perceived personalization are two distinct constructs.•A personalized message may be accidentally perceived as non-personalized.•A non-personalized message may be accidentally perceived as personalized.•Perceived personalization is the real driver of favorable personalization effects.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Retailers develop personalized websites with the aim of improving customer experience. However, we still have limited knowledge about the effect of personalization on customer experience and the ...underlying processes. With a lab experiment, this research specifically examines the effect of actual personalization and perceived personalization on playful customer experience using both subjective and objective measures, with the support of eye-tracking techniques. We show that personalization, regardless of whether it is perceived or not, enhance the playful customer experience of a retailing website. In addition, we highlight the presence of two concomitant processes. Content needs to be perceived as personalized to influence the subjective playful customer experience, but actual personalization does influence objective playful customer experience. Although customers spend the same time on the website, they focus more of their attention on their favorite products when content is personalized. Such focused attention leads them to select their favorite products for purchase.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study empirically explored consumers’ response to the personalization–privacy paradox arising from the use of location-based mobile commerce (LBMC) and investigated the factors affecting ...consumers’ psychological and behavioral reactions to the paradox. A self-administered online consumer survey was conducted using a South Korean sample comprising those with experience using LBMC, and data from 517 respondents were analyzed. Using cluster analysis, consumers were categorized into four groups according to their responses regarding perceived personalization benefits and privacy risks: indifferent (n = 87), personalization oriented (n = 113), privacy oriented (n = 152), and ambivalent (n = 165). The results revealed significant differences across consumer groups in the antecedents and outcomes of the personalization–privacy paradox. Multiple regression analysis showed that factors influence the two outcome variables of the personalization–privacy paradox: internal conflict (psychological outcome) and continued use intention of LBMC (behavioral outcome). In conclusion, this study showed that consumer involvement, self-efficacy, and technology optimism significantly affected both outcome variables, whereas technology insecurity influenced internal conflict, and consumer trust influenced continued use intention. This study contributes to the current literature and provides practical implications for marketers and retailers aiming to succeed in the mobile commerce environment.
•Explores psychological and behavioral outcomes of personalization-privacy paradox.•Identifies clusters based on privacy risk and personalization benefit perceptions.•The ambivalent group was the largest implying consumer dilemma in LBMC use.•The ambivalent group had the highest level of internal conflict and use intention.•Self-efficacy, involvement, and trust are key factors for stable LBMC use.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Although marketing managers are relying increasingly on customer data, insight into the best approaches for resolving the personalization–privacy paradox remains limited. Specifically, we argue for ...the success of a personalization involving the integration of two stages: the self-disclosure stage and the personalization stage. Using a conceptual framework grounded in the foot-in-the-door effect, we argue that compliance with commitment to self-disclosure as the initial small request induces greater compliance with the later target request. The results of a large-scale two-stage field experiment based on a combined propensity score matching and difference-in-difference model show positive causal effects of the act of self-disclosure and the positive effect of the intensity of self-disclosure on purchase responses to personalized promotions. The results also indicate that a combination of privacy assurance and personalization declaration drives customers’ act of self-disclosure and increases the intensity of self-disclosure. Findings empower managers to capitalize on new opportunities in personalization.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
With the development of information technology, the concept of smart healthcare has gradually come to the fore. Smart healthcare uses a new generation of information technologies, such as the ...internet of things (loT), big data, cloud computing, and artificial intelligence, to transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalized. With the aim of introducing the concept of smart healthcare, in this review, we first list the key technologies that support smart healthcare and introduce the current status of smart healthcare in several important fields. Then we expound the existing problems with smart healthcare and try to propose solutions to them. Finally, we look ahead and evaluate the future prospects of smart healthcare.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e.g., clicks, item ...consumption, ratings). They allow users to express intent, preferences, constraints, and contexts in a richer fashion, often using natural language (including faceted search and dialogue). Yet more research is needed to find the most effective ways to use this feedback. One challenge is inferring a user’s semantic intent from the open-ended terms or attributes often used to describe a desired item. This is critical for recommender systems that wish to support users in their everyday, intuitive use of natural language to refine recommendation results. Leveraging concept activation vectors (CAVs) 26, a recently developed approach for model interpretability in machine learning, we develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in recommender systems. One novel feature of our approach is its ability to distinguish objective and subjective attributes (both subjectivity of degree and of sense) and associate different senses of subjective attributes with different users. We demonstrate on both synthetic and real-world datasets that our CAV representation not only accurately interprets users’ subjective semantics but also can be used to improve recommendations through interactive item critiquing.
Use of conversational artificial intelligence (AI), such as humanlike social chatbots, is increasing. While a growing number of people is expected to engage in intimate relationships with social ...chatbots, theories and knowledge of human–AI friendship remain limited. As friendships with AI may alter our understanding of friendship itself, this study aims to explore the meaning of human–AI friendship through a developed conceptual framework. We conducted 19 in-depth interviews with people who have a human–AI friendship with the social chatbot Replika to uncover how they understand and perceive this friendship and how it compares to human friendship. Our results indicate that while human–AI friendship may be understood in similar ways to human–human friendship, the artificial nature of the chatbot also alters the notion of friendship in multiple ways, such as allowing for a more personalized friendship tailored to the user’s needs.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Despite the vast opportunities offered by location-aware marketing (LAM), mobile customers' privacy concerns appear to be a major inhibiting factor in their acceptance of LAM. This study extends the ...privacy calculus model to explore the personalization–privacy paradox in LAM, with considerations of personal characteristics and two personalization approaches (covert and overt). Through an experimental study, we empirically validated the proposed model. Results suggest that the influences of personalization on the privacy risk/benefit beliefs vary upon the type of personalization systems (covert and overt), and that personal characteristics moderate the parameters and path structure of the privacy calculus model.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•One-size-fits-all treatment approaches are not sufficient and data-data driven approaches to personalize treatment are necessary.•Individual characteristics may predict better outcomes with one ...treatment rather than another.•Personalized skill sequencing influences treatment outcome.
Clinicians in practice routinely make decisions that, in effect, tailor the care they provide to each individual patient. For example, clinicians must decide which treatment package to administer, or what specific skills to introduce. Providers also determine when to change approaches, the frequency of sessions, and when to terminate treatment. In this review, we describe the empirical literature for personalizing the delivery of psychological care that corresponds to the inflection points in treatment that clinicians face. In addition, we provide data-based recommendations to clinicians for personalizing patient care at each of these decision points and suggest areas for future study.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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