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The study aims to explore the buildup of consumer-based brand equity (CBBE) from positive derived experiences. Rewarding experiences with products make a user feel good about their decision to buy and use them. Those feelings get accrued as strong consumer–brand relationship, measured comprehensively by CBBE in marketing literature.

The study is conducted in two phases – exploratory and validation. The exploratory phase involved conceiving a theoretical framework from in-depth literature review. The framework is then validated through a survey-based empirical phase. Smartphones form the context of the work.

The three consumption values used in the study are usability, social value and pleasure in use. Brand equity has been conceptualized and measured as brand association, perceived value, brand trust and brand loyalty. The moderating role of user expertise, as well as lifestyle, was also tested on pleasure derived. Most of the hypothesized relationships between different constructs of experience and brand equity were found significant. Significant evidence for hierarchical formation of brand equity was also established.

Lack of evidence of moderation of lifestyle may be ascribed to the validity of the scale used to measure it in the current context and needs to be updated. The study contributes by conceiving experience as a multidimensional framework based on Holbrook’s typology, besides validating its relationship to CBBE. Hierarchical formation of brand equity is also a novel contribution.

This study provides an indicative guide to marketers with design cues that can provide relevant consumption values in the quest for a positive brand impression. It also provides directions for segmenting the smartphone market based on user expertise for better branding.

The study is innovative by relating experience, conceptualized with Holbrook’s framework and CBBE – something yet to be seen in the literature.

The average employee spends a total of 18.6 h every two months on password-related activities, including password retries and resets. The problem is caused by the user forgetting or mistyping the password (usually because of character switching). The source of this issue is that while a password containing combinations of lowercase characters, uppercase characters, digits and special characters (LUDS) offers a reasonable level of security, it is complex to type and/or memorise, which prolongs the user authentication process. This results in much time being spent for no benefit (as perceived by users), as the user authentication process is merely a prerequisite for whatever a user intends to accomplish. This study aims to address this issue, passphrases that exclude the LUDS guidelines are proposed.

To discover constructs that create security and to investigate usability concerns relating to the memory and typing issues concerning passphrases, this study was guided by three theories as follows: Shannon’s entropy theory was used to assess security, chunking theory to analyse memory issues and the keystroke level model to assess typing issues. These three constructs were then evaluated against passwords and passphrases to determine whether passphrases better address the security and usability issues related to text-based user authentication. A content analysis was performed to identify common password compositions currently used. A login assessment experiment was used to collect data on user authentication and user – system interaction with passwords and passphrases in line with the constructs that have an impact on user authentication issues related to security, memory and typing. User–system interaction data was collected from a purposeful sample size of 112 participants, logging in at least once a day for 10 days. An expert review, which comprised usability and security experts with specific years of industry and/or academic experience, was also used to validate results and conclusions. All the experts were given questions and content to ensure sufficient context was provided and relevant feedback was obtained. A pilot study involving 10 participants (experts in security and/or usability) was performed on the login assessment website and the content was given to the experts beforehand. Both the website and the expert review content was refined after feedback was received from the pilot study.

It was concluded that, overall, passphrases better support the user during the user authentication process in terms of security, memory issues and typing issues.

This research aims at promoting the use of a specific type of passphrase instead of complex passwords. Three core aspects need to be assessed in conjunction with each other (security, memorisation and typing) to determine whether user-friendly passphrases can support user authentication better than passwords.

Wearable health devices (WHDs) have demonstrated significant potential in assisting elderly adults with proactive health management by utilizing sensors to record and monitor various aspects of their health, including physical activity, heart rate, etc. However, limited research has systematically explored older adults’ continued usage intention toward WHD. By utilizing the extended unified theory of acceptance and use of technology (UTAUT2), this paper aims to probe the precursors of elderly adults’ continuance intention to use WHD from an enabler–inhibitor perspective.

The research model was developed based on UTAUT2 and examined utilizing the partial least squares technique (PLS). The research data were collected through in-person meetings with older people (n = 272) in four cities in China.

Results reveal that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic values and perceived complexity are the positive predictors of elderly adults’ continuance intention to use WHDs. Technology-related anxiety and usage cost negatively influence the formation of older people’s continuance intention.

This work is an original empirical investigation that draws on several theories as guiding frameworks. It adds to the existing literature on the usage of wearable technologies and offers insights into how the elderly’s intentions to continue using WHDs can be developed. This study broadens the scope of the UTAUT2 application and presents an alternative theoretical framework that can be utilized in future research on the usage behavior of wearable devices by individuals.

For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of crowdfunding by comparing prediction models.

With 1,368 features extracted from 15,195 Kickstarter campaigns in the USA, the authors compare base models such as logistic regression (LR) with tree-based homogeneous ensembles such as eXtreme gradient boosting (XGBoost) and heterogeneous ensembles such as XGBoost + LR.

XGBoost shows higher prediction accuracy than LR (82% vs 69%), in contrast to the findings of a previous relevant study. Regarding important content features, humans (e.g. founders) are more important than visual objects (e.g. products). In both spoken and written language, words related to experience (e.g. eat) or perception (e.g. hear) are more important than cognitive (e.g. causation) words. In addition, a focus on the future is more important than a present or past time orientation. Speech aids (see and compare) to complement visual content are also effective and positive tone matters in speech.

This research makes theoretical contributions by finding more important visuals (human) and language features (experience, perception and future time). Also, in a multimodal context, complementary cues (e.g. speech aids) across different modalities help. Furthermore, the noncontent parts of speech such as positive “tone” or pace of speech are important.

Founders are encouraged to assess and revise the content of their video or text ads as well as their basic campaign features (e.g. goal, duration and reward) before they launch their campaigns. Next, overly complex ensembles may suffer from overfitting problems. In practice, model validation using unseen data is recommended.

Rather than reducing the number of content feature dimensions (Kaminski and Hopp, 2020), by enabling advanced prediction models to accommodate many contents features, prediction accuracy rises substantially.