This paper presents the success story of the intuitive vision of an Information and Communication Technology (ICT) high school educator in South Africa. The growth and evolution of a Community of Practice towards a full-fledged living lab is investigated. A grounded theory study analyses the living lab concept and highlights some of the current challenges secondary high school ICT education face within the South African educational landscape. Some of the concepts, ideas, best practices, and lessons learned in the establishment and running of two web based technologies to support secondary school ICT subjects is discussed. The researchers present a motivation for the use of living labs to address some of the issues identified and highlights how the existing platforms fits into bigger design.

Aim/Purpose: This paper aims to describe how various Kanban elements can help alleviate two prominent types of challenges, communication and collaboration in Global Software Development (GSD). Background: Iterative and Lean development methodologies like Kanban have gained significance in the software development industry, both in the co-located and globally distributed contexts. However, little is known on how such methodologies can help mitigate various challenges in that occur in a globally distributed software development context. Methodology: The study was conducted using a single-case study based on a general inductive approach to analysis and theory development. Through the literature review, collaboration and communication challenges that GSD teams face were identified. Data collected through semi-structured interviews was then inductively analyzed to describe how the case-study teams employed various Kanban elements to mitigate communication and collaboration challenges they face during GSD. Findings: The study found that some Kanban elements, when properly employed, can help alleviate collaboration and communication challenges that occur within GSD teams. These relate to Inclusion Criteria, Reverse Items, Kanban Board, Policies, Avatars, and Backlog. Contribution: The paper contributes to knowledge by proposing two simple concept maps that detail the specific types of communication and collaboration challenges which can be alleviated by the aforementioned Kanban elements in GSD. Recommendations for Practitioners: This paper is relevant to GSD teams who are seeking ways to enhance their team collaboration and communication as these are the most important elements that contribute to GSD project success. It is recommended that relevant Kanban elements be used to that effect, depending on the challenges that they aim to alleviate. Future Research: Future research can investigate the same research questions (or similar ones) using a quantitative approach.

Aim/Purpose: Governments, private business, and academia have become increasingly aware of the importance of collaboration in multi-stakeholder, multicultural environments. This is due to the globalization and (developing) mutual relationships with other global partners, due to the often varying visions and goals between the respective organizations in managing projects that span those environments. Background: This research conducts a survey of literature pertaining to organizational collaboration in multi-stakeholder, multicultural environments in government, private business, and academic sectors, conducting an analysis to identify the gaps in the basic questions thus far explored in the literature. The gap analysis will expose the opportunities for greater collaboration in these environments. Methodology: The author conducted a literature review to identify existing research gaps to focus interviews that will develop multiple case studies in future research. Contribution/Findings: This literature review has determined gaps in understanding how contributing factors to cultural communication impact collaboration in multi-cultural, multi-stakeholder organizations, encouraging additional research in this area. Recommendations for Practitioners: Practitioners have the opportunity to develop their use of cultural communication contributing factors, potentially increasing their collaboration efficiency. Recommendation for Researchers: Researchers have opportunity to gather empirical evidence that factors of cultural communication may influence collaboration in the multi-cultural, multi-stakeholder environment. Impact on Society Improved understanding of how cultural communication factors influence collaboration in multi-cultural, multi-stakeholder organizations can improve organizational efficiency. Future Research: Gather empirical evidence that factors of cultural communication may influence collaboration in the multi-cultural, multi-stakeholder environment.

Collaboration technologies enable people to communicate and use information to make organizational decisions. The United States Navy refers to this concept as information dominance. Various collaboration technologies are used by the Navy to achieve this mission. This qualitative descriptive study objectively examined how a matrix oriented Navy activity perceived an implemented collaboration technology. These insights were used to determine whether a specific collaboration technology achieved a mission of information dominance. The study used six collaboration themes as a foundation to include: (a) Cultural intelligence, (b) Communication, (c) Capability, (d) Coordination, (e) Cooperation, and (f) Convergence. It was concluded that collaboration technology was mostly perceived well and helped to achieve some levels of information dominance. Collaboration technology improvement areas included bringing greater awareness to the collaboration technology, revamping the look and feel of the user interface, centrally paying for user and storage fees, incorporating more process management tools, strategically considering a Continuity of Operations, and incorporating additional industry best practices for data structures. Emerging themes of collaboration were collected to examine common patterns identified in the collected data. Emerging themes included acceptance, awareness, search, scope, content, value, tools, system performance, implementation, training, support, usage, structure, complexity, approach, governance/configuration management/policy, and resourcing.

The purpose of the study is to identify influential factors in the use of collaborative networks within the context of manufacturing. The study aims to investigate factors that influence employees’ learning, and to bridge the gap between theory and praxis in collaborative networks in manufacturing. The study further extends the boundary of a collaborative network beyond enterprises to include suppliers, customers, and external stakeholders. It provides a holistic perspective of collaborative networks within the complexity of the manufacturing environment, based on empirical evidence from a questionnaire survey of 246 respondents from diverse manufacturing industries. Drawing upon the socio-technical systems (STS) theory, the study presents the theoretical context and interpretations through the lens of manufacturing. The results show significant influences of organizational support, promotive interactions, positive interdependence, internal-external learning, perceived effectiveness, and perceived usefulness on the use of collaborative networks among manufacturing employees. The study offers a basis of empirical validity for measuring collaborative networks in organizational learning and knowledge/information sharing in manufacturing.

A central premise for the creation of Electronic Health Records (EHR) is ensuring the portability of patient health records across various clinical, insurance, and regulatory entities. From portability standards such as International Classification of Diseases (ICD) to data sharing across institutions, a lack of portability of health data can jeopardize optimal care and reduce meaningful use. This research empirically investigates the relationship between health records availability and portability. Using data collected from 168 medical providers and patients, we confirm the positive relationship between user perceptions of expected satisfaction with EHR availability and the expected satisfaction with portability. Our findings contribute to more informed practice by understanding how ensuring the availability of patient data by virtue of enhanced data sharing standards, device independence, and better EHR data integration can subsequently drive perceptions of portability across a multitude of stakeholders.

Aim/Purpose: In this work, the authors propose an augmented model for human-centered Unified Communications & Collaboration (UC&C) product design and evaluation, which is supported by previous theoretical work. Background: Although the goal of implementing UC&C in an organization is to promote and mediate group dynamics, increasing overall productivity and collaboration; it does not seem to provide a solution for effective communication. It is clear that there is still a lack of consideration for human communication processes in the development of such products. Methodology: This paper is sustained by existing research to propose and test the application of an augmented model capable of supporting the design, development and evaluation of UC&C services that can be driven by the human communication process. To test the application of the augmented model in UC&C service development, a proof-of-concept mobile prototype was elaborated upon and evaluated, making use of User Experience (UX) and user-centred methods and techniques. A total of nine testing sessions were carried out in an organizational communication setup and recorded with eye tracking technology. Contribution: The authors argue that UC&C services should look at the user’s (human) natural processes to improve effective infocommunication and thus enhance collaboration. Authors believe this augmented version of the model will pave the way improving the research and development of useful and practical infocommunication products, capable of truly serving users’ needs. Findings: On evaluation of the prototype, qualitative data analysis uncovered structural problems in the proposed prototype which hindered the augmented model’s elements and subsequently, the user experience. Five out of eighteen identified interaction issues are highlighted in this paper to demonstrate the proposed augmented model’s validity, applied in UC&C services evaluation. Recommendations for Practitioners: Considering and respecting the user’s natural communication processes, practitioners should be able to propose and develop innovative solutions that truly enable and empower effective organizational collaboration. UC&C functionalities should be designed, taking the augmented model’s proposed elements and their pertinence in representing the human interpersonal communication phenomena into consideration, namely: Social Presence; Immediacy of Communication; Concurrency and Synchronicity. Recommendation for Researchers: This paper intends to demonstrate that the adoption and use of UTAUT technology characteristics, in conjunction with Synchronicity proposition, can be considered as a reference for human-centric design and the evaluation of UC&C systems. Impact on Society: To highlight the need to develop further research on this important topic of human collaboration mediated by technology inside organizations. Future Research: This research focused its attention on communication functionalities. However, collaboration can potentially be affected by other services that may be included in a UC&C system, such as scheduling, meetings or task management. Future research could consider employing this augmented model to evaluate such systems or proof-of-concept prototypes.

Aim/Purpose: This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background: Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology: One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution: Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings: Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners: Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendation for Researchers: Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society: Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the da-taset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research: The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India.

The proposed work aims to discover the international technology competition and development of human-artificial intelligence (AI) collaboration on content creation in the film and animation industries to support the strategic planning, decision-making of R&D, and soft innovation. The study demonstrates a hybrid approach that combines technology life cycle (TLC) and latent Dirichlet allocation (LDA) topic modelling. We analyse 1,982 patents of AI collaborating on creating film and animation in the primary patent application countries (i.e., patents applied to the intellectual property offices of the USA, China, Korea, Japan, and European Patent Office, EPO) from 2010 to 2020. The TLC results show growing trends in the international technology competition. The major topic trends corresponding to TLC phases denote strong potential or future stagnation signals in different countries. The study provides the future R&D signals and suggests stimulating soft innovation with human-AI collaboration to face growing competition.

Prediction of route based on historical trip observation of users is widely employed in location-based services. This work concentrates on building a route prediction system using Lempel-Ziv technique applied to a historical corpus of user travel data. Huge continuous logs of historical GPS traces representing the user's location in past are decomposed into smaller logical units known as trips. User trips are converted into sequences of road network edges using a process known as map matching. Lempel-Ziv is applied on road network edges to build the prediction model that captures the user's travel pattern in the past. A two-phased model is proposed using a map reduce framework without losing accuracy and efficiency. Model is then used to predict the user's end-to-end route given a partial route travelled by the user at any point in time. The objective of the proposed work is to build a Route Prediction system in which model building and prediction both are horizontally scalable.