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This paper presents a survey of research into interactive robotic systems for the purpose of identifying the state of the art capabilities as well as the extant gaps in this emerging field. Communication is multimodal. Multimodality is a representation of many modes chosen from rhetorical aspects for its communication potentials. The author seeks to define the available automation capabilities in communication using multimodalities that will support a proposed Interactive Robot System (IRS) as an AI mounted robotic platform to advance the speed and quality of military operational and tactical decision making.

This review will begin by presenting key developments in the robotic interaction field with the objective of identifying essential technological developments that set conditions for robotic platforms to function autonomously. After surveying the key aspects in Human Robot Interaction (HRI), Unmanned Autonomous System (UAS), visualization, Virtual Environment (VE) and prediction, the paper then proceeds to describe the gaps in the application areas that will require extension and integration to enable the prototyping of the IRS. A brief examination of other work in HRI-related fields concludes with a recapitulation of the IRS challenge that will set conditions for future success.

Using insights from a balanced cross section of sources from the government, academic, and commercial entities that contribute to HRI a multimodal IRS in military communication is introduced. Multimodal IRS (MIRS) in military communication has yet to be deployed.

Multimodal robotic interface for the MIRS is an interdisciplinary endeavour. This is not realistic that one can comprehend all expert and related knowledge and skills to design and develop such multimodal interactive robotic interface. In this brief preliminary survey, the author has discussed extant AI, robotics, NLP, CV, VDM, and VE applications that is directly related to multimodal interaction. Each mode of this multimodal communication is an active research area. Multimodal human/military robot communication is the ultimate goal of this research.

A multimodal autonomous robot in military communication using speech, images, gestures, VST and VE has yet to be deployed. Autonomous multimodal communication is expected to open wider possibilities for all armed forces. Given the density of the land domain, the army is in a position to exploit the opportunities for human–machine teaming (HMT) exposure. Naval and air forces will adopt platform specific suites for specially selected operators to integrate with and leverage this emerging technology. The possession of a flexible communications means that readily adapts to virtual training will enhance planning and mission rehearsals tremendously.

Interaction, perception, cognition and visualization based multimodal communication system is yet missing. Options to communicate, express and convey information in HMT setting with multiple options, suggestions and recommendations will certainly enhance military communication, strength, engagement, security, cognition, perception as well as the ability to act confidently for a successful mission.

The objective is to develop a multimodal autonomous interactive robot for military communications. This survey reports the state of the art, what exists and what is missing, what can be done and possibilities of extension that support the military in maintaining effective communication using multimodalities. There are some separate ongoing progresses, such as in machine-enabled speech, image recognition, tracking, visualizations for situational awareness, and virtual environments. At this time, there is no integrated approach for multimodal human robot interaction that proposes a flexible and agile communication. The report briefly introduces the research proposal about multimodal interactive robot in military communication.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

In today's digitalized world, platform leadership is a novel leadership style that facilitates employee innovation. However, the impact mechanism of platform leadership on employee innovation passion has not been explored.

In this study, based on the theory of a self-organizing objective system, 591 new-generation employees were surveyed to explore the impact of platform leadership on the harmonious innovation passion of new-generation employees.

The results showed that platform leadership stimulates the harmonious innovation passion of employees by promoting the integration of organizational and employee objectives. This mechanism was found to be weakened by the internal integrated organizational culture and strengthened by the external adaptive organizational culture.

This study explores the mechanism by which platform leadership style influences the harmonious innovation passion of new-generation employees and provides theoretical guidance and practical insight into ways to improve the innovation capability of new-generation employees.

The study aspires to enhance comprehension of the intricate interplay between supply chain management (SCM) and resilience in family businesses, thereby offering valuable insights to managers and policymakers endeavouring to foster resilience in uncertain environments.

Commencing from the premise that family businesses (FBs) prioritize the preservation of socio-emotional wealth (SEW) when formulating strategic decisions, this study endeavours to advance understanding of supply chain practices adopted by FBs and their direct impact on resilience during crisis situations or economically challenging periods. Through an exploratory case study of nine FBs, the present research reveals four pivotal strategies in SCM that contribute to their resilience: (i) reorganization of inventory management; (ii) cultivating close relationships with suppliers; (iii) emphasizing product quality and customer retention; and (iv) implementing cost reduction measures to bolster resilience. The aim of the study is to provide an in-depth understanding of the intricate interplay between SCM and resilience in FBs, thereby offering valuable insights to managers and policymakers endeavouring to foster resilience in uncertain environments.

Our approach offers a theoretical framework for SCM aligned with prior research on the interplay between characteristics of family businesses and resilience strategies. Furthermore, this paper illustrates how factors such as the emphasis on high-quality products and services by family businesses contribute to achieving non-economic objectives that owners adopt to reconcile family and business needs, creating intrinsic added value for the company. It reveals various challenges in SCM, including inventory organization changes, supplier closures and the significance of customer retention. Family businesses are implementing product and technology enhancements and leveraging digitization to enhance supply chain processes.

This paper contributes significantly to the field of FBs by highlighting the crucial role of SCM in enhancing business resilience during crises. It empirically examines how the SEW characteristics of FBs influence the reconfiguration of their supply chains to enhance resilience, presenting a theoretical model for this context. Our theoretical framework employs an SEW perspective to elucidate how FBs respond to the challenges posed by the COVID-19 pandemic by adapting their SCM processes to safeguard their social and emotional legitimacy, organizational visibility and reputation. These adaptations gain particular relevance during crises or turbulent conditions, potentially leading to alterations in how FBs formulate their supply chain strategies and manage supply chain-related processes.

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

This study investigates into how organizations enable the compatibility between intermediary role of conventional systems with disintermediary potentials of blockchain toward the coordination of multiple actors in operations management.

The data were collected from 31 interviewees of the case organizations. We conduct an in-depth case study of successful BC implementation in operations management, by using affordance-actualization (A-A) theory as the theoretical lens.

This study identifies the incompatibility between the affordances of conventional systems and blockchain in coordination/operations management and offers a process model in which a fusion phase enables the affordances to be compatible and then to be actualized. The fusion phase extends A-A theory by transposing and connecting in the context of operations management. The result also shows that blockchain technology has decentralized potentials to address the issues caused by centralized organizations or information systems, while not to replace the intermediary roles of centralized organizations or information systems.

This study makes important theoretical contributions to the literature on blockchain used in operations management, the roles of blockchain enablement and affordance-actualization theory. The findings can also help IT practitioners to implement BC-based applications effectively.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

The case was written using information and data from secondary sources. It describes real people and the situations experienced by them. It does not use any fictitious names, scenarios or organizations.

The case study “Melanie Perkins: Poised to Redesign Canva from Tech Unicorn to Tech Giant?” describes the entrepreneurship journey of Melanie Perkins (she) (Perkins), the CEO of Australia-based tech unicorn and graphic design company, Canva Pty Ltd. (Canva). The case starts with a brief look into Perkins’ background and documents her entrepreneurial spirit, which, at the age of 19, led her to identify a hitherto unserved market (yearbooks) in the graphic design industry and offer an online design system through her venture, Fusion Books (Fusion). Fusion was completely bootstrapped and became a runaway success within five years. That encouraged her to envision setting up a one-stop-shop design site that would make design accessible to everyone.

However, when she tried to raise funds, Perkins encountered multiple rejections from venture capitalists. She persevered and continually refined her strategy. Eventually, she managed to raise venture capital funding and establish her design startup, Canva, in 2013. Canva then went on to disrupt the graphic design industry. The case describes in detail the reasons for Canva’s success, which went on to be one of the few profitable unicorn start-ups. The case also throws light on how Perkins used Canva as a tool to change society with her two-step plan. Despite its market success, Canva faced heavy competition in the design and publishing space from well-established players. Can Perkins challenge the competition and ultimately make Canva a software giant in the future?

The case is intended for use in teaching the subjects “Entrepreneurship Development,” “Business Strategy,” “Leadership Skills and Change Management” and “Positive Psychology for Managers” in both graduate and post-graduate programs.

For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.

Upon completion of the case study, the students will be able to understand brand differentiation and marketing challenges faced by small businesses in emerging markets; recognize the significance of marketing strategies for a growing business in emerging markets; assimilate paid, owned and earned media to improvise the effectiveness of firm’s communication and digital marketing strategy; analyze the relevance of social media marketing in developing a brand; and create a content marketing strategy.

The case dilemma involved a possible course of action that Fusion Creations faced at the beginning of 2022 about marketing strategies across paid, earned and owned media. “Fusion Creations” was the creation of two sisters who were avid cake bakers since young age. They identified the demand for homemade cakes and the growing number of home bakers in India. It was during the Covid-19 pandemic that they faced challenges in terms of lockdown and scarcity of supply for baking essentials. Moreover, although the pandemic had brought most sections of the society worldwide to a standstill, home bakers were thriving. After the pandemic, these home bakers turned their passion into full-time profession. It was time for the sisters to view this stage as a challenge because of competition from aspiring entrepreneurs and rising home bakers, and convert it into an opportunity. Can Fusion Creation leverage the online social media platforms for their product sales and marketing? With presence established on various social media platforms, were they doing it right, or was there a better way? A few questions lay in front of Chaitali and Kena, owners and bakers of Fusion Creations.

This case is written for use in digital and social media marketing classes for graduation-level courses. The focus of the case aligns well with discussions of digital and social media marketing strategy. The case also has application in discussions regarding implementation of digital marketing strategy. Instructors that choose to emphasize social media strategies could assign this case to explore online marketing and digital communication.

Teaching notes are available for educators only.

CSS8: Marketing.