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The study investigates the antecedents and consequences of digital ad avoidance in the e-commerce industry. This study aims to investigate how digital ad avoidance relates to internet usage over time, using a two-wave longitudinal research design. This study also explores how real-world advertising clutter (clutter and non-clutter) affects these relationships.

The data were collected from 192 online consumers in Türkiye, and a two-wave longitudinal research design and structural equation modeling were conducted to test the hypotheses of the conceptual model.

The results indicated that perceived goal impediment and prior negative experience had positive effects on affective avoidance in both ad-clutter and non-clutter periods. Further, affective avoidance had a significant positive effect on both cognitive and behavioral avoidance in either period. In addition, ad clutter moderated the effects of cognitive and behavioral avoidance on online purchase intention.

This research provides new evidence of the crucial role perceived goal impediment, prior negative experience and perceived control each play in driving digital ad avoidance. This paper contributes to the body of knowledge in the field by exploring how advertising clutter (both cluttered and non-cluttered settings) moderates these relationships through a two-wave longitudinal study.

This study aimed to test some dynamics related to YouTube advertising clutter and viewers' attitudes towards YouTube channels operated by traditional television (TV) companies (These YouTube channels tend to have a greater ad clutter since their videos have embedded ads of programme sponsors in addition to regular YouTube advertisements).

Employing a positivistic, quantitative methodology, the study collected data through an online survey from a sample of 295 YouTube viewers in Sri Lanka. Data were analysed using the regression-based PROCESS macro, utilising the SPSS software.

Ad clutter has a negative impact on viewers' attitudes towards the YouTube channel, but only indirectly, through irritation. Skippability of advertisements does not moderate the relationship between ad clutter and ad irritation, but non-skippability increases ad irritation (direct relationship).

YouTube channel owners should pay careful attention to having an optimum level of advertising because clutter resulting in high ad irritation leading to a negative attitude towards the channel could reduce subscriptions. Offering an ad skippability option is unlikely to reduce irritation if there is high clutter.

Although ad clutter is well-researched, its impact on viewer responses to online media channels is uncommon. With YouTube poised to become a top ad revenue-generating medium, this study contributes by examining some dynamics related to ad clutter in a possibly high-clutter context.

Using the stimulus-organism-response (SOR) theory, this study explores the effect of religiosity, resource sharing and environmental attitudes on minimalist behaviour in sharing economy-based services and its subsequent influence on customer citizenship behaviour.

This research employed a descriptive, quantitative and cross-sectional design, examining data from 530 Indian tourists who used sharing economy-based services. The proposed conceptual model was evaluated through partial least squares-structural equation modelling (PLS-SEM), and the impact of control variables was examined via PLS-multi-group analysis (PLS-MGA).

The study findings validated that religiosity, resource sharing and environmental attitudes significantly foster minimalist behaviour in sharing economy-based services. This minimalist behaviour, in turn, positively impacts customer citizenship behaviour, affecting aspects such as tolerance, feedback, advocacy and assistance to others. The model's outcomes also indicated the substantial moderating roles of age and gender.

This study is novel in its exploration of how religiosity, resource sharing and environmental attitudes as stimuli can shape minimalist behaviour within sharing economy-based services and further influence customer citizenship behaviour. By uncovering the significant roles of age and gender in this context, the research offers unique insights into understanding the dynamics of consumer behaviour within the burgeoning sharing economy. The findings provide valuable direction for stakeholders in the sharing economy and policymakers aiming to cultivate positive consumer behaviours and advance the sector's sustainability.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.

A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.

The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.

Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

Surgical services are settings where resilient performance (RP) is necessary to cope with a wide range of variabilities. Although RP can benefit from a supportive built environment (BE), prior studies have focused on the operating room, giving scant attention to support areas. This study takes a broader perspective, aiming at developing BE design knowledge supportive of RP at the surgical service as a whole.

Seven BE design prescriptions developed in a previous work in the context of internal logistics of hospitals, and thus addressing interactions between workspaces, were used as a point of departure. The prescriptions were used as a data analysis framework in a case study of the surgical service of a medium-sized private hospital. The scope of the study included surgical and support areas, in addition to workflows involving patients and family members, staff, equipment, sterile instruments and materials, supplies, and waste. Data collection included document analysis, observations, interviews, and meetings with hospital staff.

Results identified 60 examples of using the prescriptions, 77% of which were related to areas other than the operating rooms. The developed design knowledge is framed as a set of prescriptions, examples, and their association to workflows and areas, indicating where it should be applied.

The design knowledge is new in surgical services and offers guidance to both BE and logistics designers.

The purpose of this study is to comprehensively analyse and compare equity crowdfunding (ECF) regulations across 26 countries, shedding light on the diverse regulatory frameworks, investor and issuer limits and the evolution of ECF globally. By addressing this research gap and providing consolidated insights, the study aims to inform policymakers, researchers and entrepreneurs about the regulatory landscape of ECF, fostering a deeper understanding of its potential and challenges in various economies. Ultimately, the study contributes to the advancement of ECF as an alternative financing method for small and medium enterprises (SMEs) and startups, empowering them to access much-needed capital for growth.

The study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model for a systematic literature review on global ECF regulations. Starting with 74 initial articles from Web of Sciences and Scopus databases, duplicates were removed and language criteria applied, leaving 42 articles. After a thorough full-text screening, 20 articles were excluded, resulting in the review of 22 papers from 2016 to 2022. PRISMA’s structured framework enhances the quality of systematic reviews, ensuring transparency and accessibility of findings for various stakeholders, including researchers, practitioners and policymakers, in the field of ECF regulations.

This study examines ECF regulations across various countries. Notably, the UK has advanced regulations, while the USA adopted them later through the Jumpstart Our Business Startups Act. Canada regulates at the provincial level. Malaysia and China were early adopters in Asia, but Hong Kong, Japan, Israel and India have bans. Turkey introduced regulations in 2019. New Zealand and Australia enacted laws, with Australia referring to it as “crowd-sourced equity funding”. Italy, Austria, France, Germany and Belgium have established regulations in Europe. These regulations vary in investor and issuer limits, disclosure requirements and anti-corruption measures, impacting the growth of ECF markets.

This study’s findings underscore the diverse regulatory landscape governing ECF worldwide. It reveals that regulatory approaches vary from liberal to protectionist, reflecting each country’s unique economic and political context. The implications of this research highlight the need for cross-country analysis to inform practical implementation and the effectiveness of emerging ECF ecosystems. This knowledge can inspire regulatory adjustments, support startups and foster entrepreneurial growth in emerging economies, ultimately reshaping early-stage funding for new-age startups and SMEs on a global scale.

This study’s originality lies in its comprehensive analysis of ECF regulations across 26 diverse countries, shedding light on the intricate interplay between regulatory frameworks and a nation’s political-economic landscape. By delving into the nuanced variations in investor limits, investment types and regulatory strategies, it unveils the multifaceted nature of ECF regulation globally. Furthermore, this research adds value by comparing divergent perspectives on investment constraints and offering an understanding of their impact on ECF efficacy. Ultimately, the study’s unique contribution lies in its potential to inform practical implementation, shape legislative frameworks and catalyse entrepreneurial ecosystems in emerging economies, propelling the evolution of early-stage funding practices.

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

This study examined the factors impeding the implementation of micro-credentials and accepting it as a credible source of earning professional qualifications and certifications necessary for pursuing higher education or other career goals.

The factors were identified by reflecting on the recent literature and Internet resources coupled with in-depth brainstorming with experts in the field of micro-credentials including educators, learners and employers. Two ranking methods, namely Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE) and multi-objective optimization based on ratio analysis (MOORA), are used together to rank the major challenges.

The results of this study present that lack of clear definitions, ambiguous course descriptions, lack of accreditation and quality assurance, unclear remuneration policies, lack of coordination between learning hours and learning outcomes, the inadequate volume of learning, and lack of acceptance by individuals and organizations are the top-ranked and the most significant barriers in the implementation of micro-credentials.

The findings can be used by educational institutions, organizations and policymakers to better understand the issues and develop strategies to address them, making micro-credentials a more recognized form of education and qualifications.

The novelty of this study is to identify the primary factors influencing the implementation of micro-credentials from the educators', students' and employers' perspectives and to prioritize those using ranking methods such as PROMETHEE and MOORA.

Social media has become an inescapable part of our lives. However, recent research suggests that excessive use of social media may lead to fatigue and users’ disengagement. This study aims to examine which brand-related factors contribute to social media fatigue (SMF) and its subsequent role on driving lurking behaviors, particularly among young consumers.

Based on survey data from 282 young users of social media, a holistic model of brand-related drivers and outcomes of SMF was tested, emphasizing the contribution of brands’ social media presence to users’ disengagement.

Research shows that branded content overload and irrelevance, as well as branded ads intrusiveness significantly impact SMF, which in turn plays a mediating role between brand-related drivers and lurking behaviors. The authors further conclude that the impact of SMF on lurking is stronger for users who follow a larger set of brands.

The study contributes to social media research by addressing its “dark side” and empirically validating the role of brands’ social media presence in developing young users’ fatigue and disengagement. The study further adds to the scant literature on SMF, which was mostly developed outside the branding field. Research also provides valuable insights to brands on how to improve their social media performance.