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AI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect service provision across different digital channels, but with a higher degree of personalization. Hence, the present study investigates the impact of intelligent RA on the PB investment advisory process to derive both process (re)design knowledge and strategic guidance for artificial intelligence (AI) usage for PB investment advisory.

The present study applies an AI process impact analysis approach by decomposing AI-based RA into three AI application types: conversational agent, customer segmentation and predictive analytics. The analysis results along a reference PB investment advisory process reveal sub-process transformations which are applied for process redesign integrating AI.

The study results imply that AI systems (1) enable seamless client journeys, (2) increase advisor flexibility, (3) support the client–advisor relationship by applying an omnichannel approach and (4) demand advisor skills to be augmented with technical and statistical knowledge.

The research study contributes (1) an AI process impact analysis approach, (2) derives process (re)design knowledge for AI deployment and (3) develops strategic guidance for AI usage in PB investment advisory.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

The purpose of this paper is to, which involved 123 students in their last year of an administration course at a private university in the city of São Paulo, Brazil, evaluate the importance of analyzing the pressure from new technologies on the careers of young university students from a career theory perspective.

From the perspective of traditional theories, the authors used “career anchors,” and from the perspective of non-traditional theories, “intelligent careers,” in which people develop three competency groups that are transferable between organizations: knowing why; knowing how; and knowing whom. The hypotheses the authors raised were analyzed using statistical techniques and the following results were obtained: young people do not see new technologies as a threat to their current job; people who see the “Knowing How” competence as being more developed feel less pressure from new technologies; non-traditional theories show a greater potential to analyze technological pressure than traditional theories; and, finally, the nature of people’s jobs produces different impacts on the pressure of new technologies on their careers, since people who occupy positions involving more human interaction with internal or external clients feel less threatened.

It was found that the lowest mean among the constructs analyzed was the pressure from technology on career. The correlations between the competencies of intelligent careers and the perception of the pressure from technology on career were weak, but significant, whereas the “Knowing How” competency was negatively correlated with the pressure caused by technology. There was no significant influence of the anchors on the pressure from technology on career. However, incorporating the competencies of intelligent careers improved the statistical model’s fit. In associating job positions with the pressure from technology on career, administrative and operational positions showed higher averages than sales associate and management positions.

Broadly speaking, it can be noted that traditional career theories, especially the vocational counseling approach, are not sufficient to explain the impact of new technologies on careers. At the same time, one way of coping with the pressure brought about by technological advances may be in using technology itself to develop “useful professional skills,” in a manner consistent with “intelligent careers.”

The purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic university online exam and checked the proctoring result.

The IOPS adopts the B/S (Browser/Server) architecture. The server side is implemented with programming language C and Python and stores the identification data of all examinees and their important behavior change status, including facial expression, eye and mouth movement and speech. The browser side collects and analyzes multimodal data of the examinee writing the online test locally and transfers the examinee’s most important behavior status change data to the server. Real-time face recognition and voice detection are implemented with the support of open-source software.

The system was integrated into a Web-based intelligent tutoring system for school mathematics education. As a pilot application, the system was also used for online proctored exam in an undergraduate seminar in Peking University during the epidemic period in 2020. The recorded log data show that all students concentrated themselves on the exam and did not leave the camera and did not speak.

During the epidemic period of the novel coronavirus outbreak, almost all educational institutions in the world use online learning as the best way to maintain the teaching and learning schedule for all students. However, current online instruction platforms lack the function to prevent the learners from cheating in online exams and cannot guarantee the integrity and equality for all examinees as in traditional classroom exams. The literature review shows that the online proctoring system should become an important component of online exams to tackle the growing online cheating problem. Although such proctoring systems have been developed and put on the market, the practical usage of such systems in authentic exams and its effect have not been reported. Those systems are heavyweight and commercial product and cannot be freely used in education. The light-weight IOPS developed by the authors could meet the need for online exam as a stable and practical approach and could contribute to the growing online learning and distance learning.

Smart industry initiatives focus on intelligent and interconnected cyber-physical systems. These initiatives develop complex technical architectures that integrate heterogenous technologies, causing significant organizational complexity. Tapping into the digital capabilities of distant partners while capturing profit from such innovation is demanding. Furthermore, firms often need to establish and orchestrate inter-organizational collaborations without prior relations or established trust. As a result, smart industry initiatives bring together disparate organizational forms and institutional environments, distinctive knowledge bases, and geographically dispersed organizations. We conceptualize this organizational capability as ‘distant capabilities integration’. This research explores the governance mechanisms that support such integration and their relation to value capture. We analyse 11 IoT case studies organized in three categories (process, product and technologies) of smart industry initiatives. Building on existing literature, we consider different ways to describe distance, including knowledge heterogeneity and organizational, geographical, institutional, cultural and cognitive distance. Finally, we describe the governance mode appropriate for upstream (developing foundational technologies) and downstream (leveraging existing distant technologies) smart industry initiatives.

Models are currently used extensively in the delivery of coaching. These models are used to give structure and form by coaches. The purpose of this paper is to present an alternative viewpoint of the impact of the use of models in the coaching relationship.

The approach taken has been to reflect on recent conversations across professional networks. The cooperative curiosity and questioning of some of our professional assumptions explores using models in coaching to enhances our practice, rather than limiting it. The paper acts as an exploratory prompt to question our practice and the role of the coach in the client/coach relationship.

The paper suggests that models are used, to a greater extent by the more inexperienced coaches to support their early practice. It is suggested that with greater experience, there is less reliance and use of format and recognised models. The paper proposes the more experienced coach provides “freedom without models” creating an alternative type of environment.

The implications of this paper are that if the authors are to grow and develop the practice and profession, there is a need to continue to research what current practice is delivering and offering the clients. The authors need to question if the early career coaches have the skills to meet the needs of the clients who engage them.

Researching our practice intends to will spark new ideas that may enhance the coaching practice and deliver the requirements of clients looking for development in a volatile and challenging corporate business world.

Technology-enabled business-to-business (B2B) services contribute the largest share to GDP growth and are fundamental for an economy’s value creation. This article aims to identify key service- and digital technology-driven B2B innovation modes and proposes a research agenda for further exploration.

This conceptual paper adopts a techno-demarcation view on service innovation, encompassing three core dimensions: service offering (the service product, or the “what”), service process (the “how”) and service ecosystem (the “who/for whom”). It delineates the implications of three digital technologies – the internet-of-things (IoT), intelligent automation (IA) and digital platforms – for service innovation across these core dimensions in B2B markets.

Digital technology has immense potential ramifications for value creation by reshaping all three core dimensions of service innovation. Specifically, IoT can transform physical resources into reconfigurable service products, IA can augment and automate a rapidly expanding array of service processes, while digital platforms provide the technical and organizational infrastructure for the integration of resources and stakeholders within service ecosystems.

This study suggests an agenda with six themes for further research, each linked to one or more of the three service innovation dimensions. They are (1) new recurring revenue models, (2) service innovation in the metaverse, (3) scaling up service innovations, (4) ecosystem innovations, (5) power dependency and lock-in effects and (6) security and responsibility in digital domains.