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The purpose of this paper is to analyze message features of fear appeals in current British road safety campaigns directed against mobile phone use while driving and to discuss barriers to explicit theory use in campaign message design.

This message-centred research takes a qualitative content analytical approach to analyze nine British web-based road safety campaigns directed against mobile phone use while driving based on the extended parallel process model. Message content and message structure are analyzed.

There still exists a gap between theory and road safety campaign practice. The study reveals that campaigns with fear appeals primarily use threatening messages but neglect efficacy-based contents. Severity messages emerge as the dominant content type while self-efficacy and response efficacy are hardly represented. Fear appeal content in the threat component was mainly communicated through the mention of legal, financial and physical harm, whereas efficacy messages communicated success stories and encouragement. As regards message structure, the threat component always preceded the efficacy component. Within each component, different patterns emerged.

To enhance efficacy in campaigns directed against distracted driving and to reduce the gap between theory and practice, social marketers should include messages that empower recipients to abstain from mobile phone use while driving. Campaigns should show recommended behaviours and highlight their usefulness and effectiveness.

This paper adds to limited research conducted on effect-independent message properties of fear appeals. It enhances understanding of fear appeal message features across the structure and content dimension. By discussing barriers to explicit theory use in social marketing practice and offering practical implications for social marketers, it contributes towards reducing the barriers to explicit theory use in campaign message design.

This paper aims to analyse the prevalence of emotional and rational appeals in social advertising campaigns. There are studies about the effectiveness of these tones of appeals in social marketing, but there is no evidence about their prevalent use in social advertisements.

The authors conducted a content analysis of forty social advertisements promoting attitudes and behaviours regarding social causes. The selected ads were in video format and were extracted from the YouTube channels of Portuguese governmental and non-governmental organisations. The ads were coded according to the characteristics of each tone of appeals and classified as emotional, rational or a mix of both.

The authors classified 25 social ads as rational appeals, 8 as emotional and 7 as a mix of both appeals. The results of the research show that social marketers have preference for the use of rational tone in social advertising campaigns.

This study shows that there is a disruption between theory and practice in social marketing, considering the higher prevalence of rational appeals in contexts where theory recommends emotional appeals for higher effectiveness. This evidence is surprising, considering a previous study that evidenced a higher use of emotional appeals in advertising connected to social causes than in commercial advertisements. This paper focus on how practice may disrupt theory and explores possible reasons for the phenomenon.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

Why are some lean workfloor teams able to improve their already high performance, over time, and others not? By studying teams' and leaders' behaviour-value patterns, this abductive field study uncovers a dynamic capability at the team level.

Various methods were employed over three consecutive years to thoroughly examine five initially high-performing lean workfloor teams, including their leaders. These methods encompassed micro-behavioural coding of 59 h of film footage, surveys, individual and group interviews, participant observation and archival data, involving objective and perceptual team-performance indicators. Two of the five teams continued to improve and perform highly.

Continuously improving high lean team performance is found to be associated with (1) team behaviours such as frequent performance monitoring, information sharing, peer support and process improvement; (2) team leaders who balance, over time, task- and relations-oriented behaviours; (3) higher-level leaders who keep offering the team face-to-face support, strategic clarity and tangible resources; (4) these three actors' endorsement of self-transcendence and openness-to-change work values and alignment, over time, with their behaviours; and (5) coactive vicarious learning-by-doing as a “stable collective activity pattern” among team, team leader, and higher-level leadership.

Since lean has been undertheorised, the authors invoked insights from organisational behaviour and management theories, in combination with various fine- and coarse-grained data, over time. The authors uncovered actors' behaviour-value patterns and a collective learning-by-doing pattern that may explain continuous lean team performance improvement. Four theory-enriching propositions were developed and visualised in a refined model which may already benefit lean practitioners.

This study was conducted in the United States of America to identify what practices virtual mentor-coaches perceived to be effective in virtual mentoring and coaching (VMC) within virtual professional learning communities (VPLCs). The authors also sought to determine the ways in which virtual mentor-coaches provided VMC for school leaders within VPLCs.

The authors used a phenomenological approach in our research, describing the lived experiences of practicing virtual mentor-coaches as they engaged in VMC. Data analysis included video analysis and systematic coding of interview data.

An in-depth analysis of interview and video data showed that virtual mentor-coaches support school leaders in developing and transforming school leaders' leadership for building teachers' instructional capacity. The authors identified a VMC process model within VPLCs, including four steps as follows: (1) presentation, (2) collaboration, (3) reflection and (4) action plan.

VMC for school leaders participating in VPLCs is regarded as a transformative model which provides encouragement, reflection and support for instructional leadership actions.

Key steps and components of an effective VMC highlighted in the current research offer practical guidance for future virtual mentor-coaches in conducting and implementing VMC within VPLCs.

In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.

This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.

The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.

This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

This study aims to present a guide for using grounded theory methods for exploring organizational phenomena of the new online era.

A reflexive account is adopted on how one can build upon the foundations of traditional offline grounded theory for conducting grounded theorizing with online-based data.

Guidelines for conducting grounded theory on online contexts are presented for crafting research questions, gathering online data and using consolidated methods for analyzing online data. This study shows future and present challenges posed by the new online era for grounded theorizing, as well as helpful lessons to be learned from traditional offline grounded theory to mitigate them.

The implications are helpful for established qualitative organizational scholars that are yet to catch-up in the boundary spanning process of using the digital sources of data in grounded theory. They are equally helpful for newcomers on qualitative grounded theory by guiding them on where and how to start these challenging research endeavors of grounded theorizing in this new online era.

Scant attention has been given on applications of grounded theory in the new online era. The differences between online and offline settings have not been clearly defined to this date, and neither do guidelines exist for how qualitative grounded theorists can take advantage of online data to build theory about new organizational phenomena emerging in the online era.