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After the dilemma at the crossroad of selling the family business and retire or take over from the shareholders in 2005, Andy Makmur was again thinking about his retirement in 2011. Now 70 years old, he was over the average retirement age and still commuting between Indonesia and Singapore for business operations and family. Glopac Chemical had been gradually improving financially since the takeover in 2005, with the help of Andy's two offsprings, John and Victoria. The joint force of John looking after the business development and Victoria on management control systems was a perfect combination in turning Glopac Chemical from making loss to profiting. However, recent developments had led to misunderstanding between John and Victoria, where they were on non-talking terms except communicating on work matters. This was a major headache for Andy, as he couldn't leave his life time's work to his offsprings who would not talk to each other.

This paper aims to address third actor introductions to interaction episodes aiming at fast-forwarding the continuous development of business relationships of new firms.

The study is qualitative, collecting data from 30 interviews from 28 informants associated with creation of new ventures and business network development in the context of a novel type of third actor called venture builder. Venture builders are privately owned organizations devoted to new firm creation in a factory-like mode, collaborating with individual entrepreneurs.

The findings suggest that interaction episodes, central to the development of new relationships, may be triggered by introductions managed by third actors using different types of involvement depending on the location and focus of the potential relationship. A framework is presented including four types of introductions to interaction episodes, aiming at saving time by removing the perceived distance between new firms and their counterparts in the initiation of business relationships. The framework describes four types of introductions of interaction episodes: Managed, Advised, Facilitated and Monitored.

Triggers and introductions of interaction episodes for new firms has previously been sparsely addressed. This paper presents how third actor involvement, by the introductions of interaction episodes with internal and external counterparts is managed with an aim of fast-forwarding relationship development.

Artificial intelligence (AI) can assist in the worldwide shortage of cybersecurity workers in technical and managerial roles. Thus, the purpose of this study was to investigate the role of AI in automating many of the routine tasks associated with cybersecurity. As such, AI enables cybersecurity personnel to reduce their workloads and focus on more strategic aspects of their work.

This study is an exploratory field study. The authors started by conducting a literature review to assess the possibility that AI tools can provide and how they can improve cybersecurity efficacy. Following this, the authors identified the specific core tasks for two cybersecurity work roles (technical and managerial) and searched for specific commercial tools that can perform each of the tasks. Then, the authors used the free ChatGPT 3.5 to list the current cybersecurity systems that use AI for the associated tasks, which the authors then reviewed with the tools’ documentation and websites to confirm these tasks were conducted or assisted by AI.

Results indicated that all 14 cybersecurity tasks of the technical work role are currently noted to be performed by commercial cybersecurity systems with AI-integrated capabilities, while only 11 of the 17 managerial work role tasks currently appear to be performed by AI.

The rapid integration of AI capabilities into commercial cybersecurity systems may suggest that the cybersecurity workforce must be currently trained on how to use AI tools in their daily operations, especially as it pertains to technical cybersecurity work roles.

The cybersecurity workforce shortage is reported to exceed four million cybersecurity workers worldwide in 2023. Thus, further understanding of the role of AI in improving the efficiency of technical and managerial cybersecurity tasks is significant.

The value of this research lies in the initial assessment of the current AI capabilities of commercial cybersecurity systems, which will ultimately provide the “super-human” performances resulting from human-AI teaming.

Food marketing has long been recognized to influence food preferences, consumption and health, yet little is known about the nature and extent of food marketing to young adults – especially with respect to their real-world encounters with food marketing and the appeals they find persuasive. This study aims to engage young adults to explore the persuasive power of food marketing and its platforms of exposure.

Participatory research with 45 young adults, who used a specially designed mobile app to capture the food marketing they encountered for seven days, including information on brand, product, platform and “power” (i.e. the specific techniques that made the advertisement persuasive).

A total of 618 ads were captured for analysis. Results revealed the dominance of digital platforms (especially Instagram, comprising 43% of ads), fast food and beverage brands (48% of ads) and the top persuasive techniques of visual style, special offer and theme.

This study uniquely draws from framing theory to advance the notions of selection and salience to understand food marketing power. It is the first study of its kind to provide a comprehensive look at the platforms and persuasive techniques of food marketing to adults as selected, captured and tagged by participants. It provides timely insights into young adults and food marketing to adults, including where it is encountered, the (generally unhealthy) brands and products promoted and how it is made meaningful.

Autonomous vehicles (AVs) have the potential to transform the infrastructure, mobility and social well-being paradigms in New Zealand (NZ) amid its unprecedented population and road safety challenges. But, public acceptance, co-evolution of regulations and AV technology based on interpersonal and institutional trust perspectives pose significant challenges. Previous theories and models need to be more comprehensive to address trust influencing autonomous driving (AD) factors in natural settings. Therefore, this study aims to find key AD factors corresponding to the chain of human-machine interaction (HMI) events happening in real time and formulate a guiding framework for the successful deployment of AVs in NZ.

This study utilized a comprehensive literature review complemented by an AV users’ study with 15 participants. AV driving sprints were conducted on low, medium and high-density roads in Auckland, followed by 15 ideation workshops to gather data about the users’ observations, feelings and attitudes towards the AVs during HMI.

This research study determined nine essential trust-influencing AD determinants in HMI and legal readiness domains. These AD determinants were analyzed, corresponding to eight AV events in three phases. Subsequently, a guiding framework was developed based on these factors, i.e. human-machine interaction autonomous driving events relationship identification framework (HMI-ADERIF) for the deployment of AVs in New Zealand.

This study was conducted only in specific Auckland areas.

This study is significant for advanced design research and provides valuable insights, guidelines and deployment pathways for designers, practitioners and regulators when developing HMI Systems for AD vehicles.

This study is the first-ever AV user study in New Zealand in live traffic conditions. This user study also claimed its novelty due to AV trials in congested and fast-moving traffic on the four-lane motorway in New Zealand. Previously, none of the studies conducted AV user study on SUV BMW vehicle and motorway in real-time traffic conditions; all operations were completely autonomous without any input from the driver. Thus, it explored the essential autonomous driving (AD) trust influencing variables in human factors and legal readiness domains. This research is also unique in identifying critical AD determinants that affect the user trust, acceptance and adoption of AVs in New Zealand by bridging the socio-technical gap with futuristic research insights.

Organizational scholars studying compensation often place an emphasis on certain employee groups (e.g., executives). Missing from this discussion is research on the compensation systems for low-wage jobs. In this review, the authors argue that workers in low-wage jobs represent a unique employment group in their understanding of rent allocation in organizations. The authors address the design of compensation strategies in organizations that lead to different outcomes for workers in low-wage jobs versus other workers. Drawing on and integrating human resource management (HRM), inequality, and worker literatures with compensation literature, the authors describe and explain compensation systems for low-wage work. The authors start by examining workers in low-wage work to identify aspects of these workers’ jobs and lives that can influence their health, performance, and other organizationally relevant outcomes. Next, the authors explore the compensation systems common for this type of work, building on the compensation literature, by identifying the low-wage work compensation designs, proposing the likely explanations for why organizations craft these designs, and describing the worker and organizational outcomes of these designs. The authors conclude with suggestions for future research in this growing field and explore how organizations may benefit by rethinking their approach to compensation for low-wage work. In sum, the authors hope that this review will be a foundational work for those interested in investigating organizational compensation issues at the intersection of inequality and worker and organizational outcomes.

This study aims to compare traffic sign (TS) and obstacle detection for autonomous vehicles using different methods. The review will be performed based on the various methods, and the analysis will be done based on the metrics and datasets.

In this study, different papers were analyzed about the issues of obstacle detection (OD) and sign detection. This survey reviewed the information from different journals, along with their advantages and disadvantages and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of autonomous vehicles and is obliged to accurately identify various TS.

The review of different approaches based on deep learning (DL), machine learning (ML) and other hybrid models that are utilized in the modern era. Datasets in the review are described clearly, and cited references are detailed in the tabulation. For dataset and model analysis, the information search process utilized datasets, performance measures and achievements based on reviewed papers in this survey.

Various techniques, search procedures, used databases and achievement metrics are surveyed and characterized below for traffic signal detection and obstacle avoidance.

This paper aims to propose a robust kinematic controller based on sliding mode theory designed to solve the trajectory tracking problem and also the formation control using the leader–follower strategy for nonholonomic differential-drive wheeled mobile robots with a PD dynamic controller.

To deal with classical sliding mode control shortcomings, such as the chattering and the requirement of a priori knowledge of the limits of the effects of disturbances, an immune regulation mechanism-inspired approach is proposed to adjust the control effort magnitude adaptively. A simple fuzzy boundary layer method and an adaptation law for the immune portion gain online adjustment are also considered. An obstacle avoidance reactive strategy is proposed for the leader robot, given the importance of the leader in the formation control structure.

To verify the adaptability of the controller, obstacles are distributed along the reference trajectory, and the simulation and experimental results show the effectiveness of the proposed controller, which was capable of generating control signals avoiding chattering, compensating for disturbances and avoiding the obstacles.

The proposed design stands out for the ability to adapt in a case involving obstacle avoidance, trajectory tracking and leader–follower formation control by nonholonomic robots under the incidence of uncertainties and disturbances and also considering that the immune-based control provided chattering mitigation by adjusting the magnitude of the control effort, with adaptability improved by a simple integral-type adaptive law derived by Lyapunov stability analysis.