Many higher education institutions are moving towards blended learning environments that seek to move towards a student-centred ethos, where students are stakeholders in the learning process. This often involves multi-modal learner-support technologies capable of operating in a range of time and place settings. This article considers the impact of an Audience Response System (ARS) upon the ongoing development of an Information Systems Professional course at the Masters level in the College of Business at Victoria University in Melbourne, Australia. The course allows students to consider ethical issues faced by an Information Systems Professional. Given the sensitivity of some of the topics explored within this area, an ARS offers an ideal vehicle for allowing students to respond to potentially contentious questions without revealing their identity to the rest of the group. The paper reports the findings of a pilot scheme designed to explore the efficacy of the technology. Use of a blended learning framework to frame the discussion allowed the authors to consider the readiness of institution, lecturers, and students to use ARS. From a usage viewpoint, multiple choice questions lead to further discussion of student responses related to important issues in the unit. From an impact viewpoint the use of ARS in the class appeared to be successful, but some limitations were reported.

Aim/Purpose: Authentic assessments offer students the opportunity to develop skills that implement the formal learning they receive in the classroom. Although there is a need for accounting graduates to possess a plethora of skills to equip them for success, there is a shortage of literature that focuses on authentic assessment design for accounting courses. This paper aims to address this gap by compiling a toolkit for accounting educators aspiring to design and implement authentic assessments. Background: This paper reviews examples of authentic assessments that are available and have been used by accounting educators and educators in general. It highlights the skills that might be developed with each assessment Methodology: A review of 182 articles on authentic assessment design and examples of authentic assessments like portfolios, reflective journals, presentations, reports, peer and self-assessment was conducted. Contribution: A toolkit with examples of authentic assessment to ease the task of authentic assessment design for those new to authentic assessment and seasoned authentic assessment practitioners alike. Findings: Authentic assessments are a form of learning. They help graduates develop skills and attributes that will make them work-ready and capable of handling a lot of real life practical work situations. Rubrics are an important part of authentic assessment implementation and their use is mandated by business school accrediting bodies like AACSB. Recommendations for Practitioners: Accounting educators may find guidelines in this paper which will help them understand authentic assessments and enlighten them on the challenges they may encounter when implementing the assessments. Recommendation for Researchers: There is a need for future researchers to contribute more to this topic so as to increase the variety and amount of literature available for those wishing to engage with authentic curriculum design in accounting. Future researchers could also focus on the perceptions of authentic assessments of accounting educators, students and employers. Impact on Society: This paper may also be of use to prospective employers wishing to gain a clear understanding of the skills inherent in prospective graduates who may have been exposed to authentic assessments. Accounting students and graduates may find this paper useful as it will help them comprehend the importance of some the assessments with the backing and assurance from the literature. Future Research: Future research may focus on the challenges in implementing authentic assessments. It would also be great to see more research addressing the perceptions of educators towards authentic assessments.

Aim/Purpose: The aim of this study was to develop a prototype of an information-generating computer tool designed to automatically map the dental restorations in a panoramic radiograph. Background: A panoramic radiograph is an external dental radiograph of the oro-maxillofacial region, obtained with minimal discomfort and significantly lower radiation dose compared to full mouth intra-oral radiographs or cone-beam computed tomography (CBCT) imaging. Currently, however, a radiologic informative report is not regularly designed for a panoramic radiograph, and the referring doctor needs to interpret the panoramic radiograph manually, according to his own judgment. Methodology: An algorithm, based on techniques of computer vision and machine learning, was developed to automatically detect and classify dental restorations in a panoramic radiograph, such as fillings, crowns, root canal treatments and implants. An experienced dentist evaluated 63 panoramic anonymized images and marked on them, manually, 316 various restorations. The images were automatically cropped to obtain a region of interest (ROI) containing only the upper and lower alveolar ridges. The algorithm automatically segmented the restorations using a local adaptive threshold. In order to improve detection of the dental restorations, morphological operations such as opening, closing and hole-filling were employed. Since each restoration is characterized by a unique shape and unique gray level distribution, 20 numerical features describing the contour and the texture were extracted in order to classify the restorations. Twenty-two different machine learning models were evaluated, using a cross-validation approach, to automatically classify the dental restorations into 9 categories. Contribution: The computer tool will provide automatic detection and classification of dental restorations, as an initial step toward automatic detection of oral pathologies in a panoramic radiograph. The use of this algorithm will aid in generating a radiologic report which includes all the information required to improve patient management and treatment outcome. Findings: The automatic cropping of the ROI in the panoramic radiographs, in order to include only the alveolar ridges, was successful in 97% of the cases. The developed algorithm for detection and classification of the dental restorations correctly detected 95% of the restorations. ‘Weighted k-NN’ was the machine-learning model that yielded the best classification rate of the dental restorations - 92%. Impact on Society: Information that will be extracted automatically from the panoramic image will provide a reliable, reproducible radiographic report, currently unavailable, which will assist the clinician as well as improve patients’ reliance on the diagnosis. Future Research: The algorithm for automatic detection and classification of dental restorations in panoramic imaging must be trained on a larger dataset to improve the results. This algorithm will then be used as a preliminary stage for automatically detecting incidental oral pathologies exhibited in the panoramic images.

Aim/Purpose: Little is known of the cultural competence or leadership styles of a minority owned newspaper. This autoethnography serves to benchmark one early 1990s example. Background: I focused on a series of flashbacks to observe an African American weekly newspaper editor-in-chief for whom I reported to 25 years ago. In my reflections I sought to answer these questions: How do minorities in entrepreneurial organizations view their own identity, their cultural competence? What degree of this perception is conveyed fairly and equitably in the community they serve? Methodology: Autoethnography using both flashbacks and article artifacts applied to the leadership of an early 1990s African American weekly newspaper. Contribution: Since a literature gap of minority newspaper cultural competence examples is apparent, this observation can serve as a benchmark to springboard off older studies like that of Barbarin (1978) and that by examining the leadership styles and editorial authenticity as noted by The Chicago School of Media Theory (2018), these results can be used for comparison to other such minority owned publications. Findings: By bringing people together, mixing them up, and conducting business any other way than routine helped the Afro-American Gazette, Grand Rapids, proudly display a confidence sense of cultural competence. The result was a potentiating leadership style, and this style positively changed the perception of culture, a social theory change example. Recommendations for Practitioners: For the minority leaders of such publications, this example demonstrates effective use of potentiating leadership to positively change the perception of the quality of such minority owned newspapers. Recommendations for Researchers: Such an autoethnography could be used by others to help document other examples of cultural competence in other minority owned newspapers. Impact on Society: The overall impact shows that leadership at such minority owned publications can influence the community into a positive social change example. Future Research: Research in the areas of culture competence, leadership, within minority owned newspapers as well as other minority alternative publications and websites can be observed with a focus on what works right as well as examples that might show little social change model influence. The suggestion is to conduct the research while employed if possible, instead of relying on flashbacks.

Aim/Purpose: Providing both formative and summative assessment that allows students to learn from their mistakes is difficult in large classes. This paper describes an automated assessment system suitable for courses with even 100 or more students. Background: Assessment is a vital part of any course of study. Ideally students should be given formative assessment with feedback during the course so students and tutors can identify weaknesses and focus on what needs improvement before summative assessment, which results in a grade. This paper describes and automated assessment system that lessens the burden of providing formative assessment in large classes. Methodology: We used Checkpoint, a web-based automated assessment system, to grade assignments in a number of different computer science courses. Contribution: The students come from diverse backgrounds, with a wide range of ages, previous qualifications and technical skills, and our approach allows the students to work at their own pace according to their individual needs, submitting their solutions as many times as they wish up to a deadline, using feedback provided by the system to help identify and correct their mistakes before trying again. Findings: Use of automated assessment allows us to achieve the goals of both summative and formative assessment: we allow students to learn from their mistakes without incurring a penalty, while at the same time awarding them a grade to validate their efforts. The students have an overwhelmingly positive view about our use of automated assessment, and their comments support our views on the assessment process. Recommendations for Practitioners: Because of the increasing number of students in today’s courses, we recommend using automated assessment wherever possible.

Aim/Purpose: The need for Higher Education Institutions (HEIs) to maximize the use of their intellectual property and strategic resources for research and teaching has become ever more evident in recent years. Furthermore, little attention is paid in developing an enabling system that will facilitate knowledge transfer in the Research-Teaching Nexus (RTN). Hence, this study assesses the current state of practice in knowledge management of the nexus in higher education in Oman. It also explores the context of how Knowledge Management System (KMS) for the nexus can be designed and utilized by HEIs and challenges them to rethink their traditional approaches in managing their knowledge as-sets to boost individual and organizational learning. Background: This study provides a Knowledge Management-based framework and design of a knowledge management system that support the academic community towards the improvement of the nexus. This study sets out ideas from various academic and professional experts on how academic stakeholders in the higher education can improve and promote knowledge transfer and make better use of its knowledge and research assets for teaching and learning. It stressed the importance of having the knowledge assets or resources that can easily be pooled, accessed, and made available to its intended stakeholders. Methodology: Data were gathered from 29 out of 49 institutional quality audit reports of all HEIs in Oman. The panel comments were coded and analysed to extract valuable insights regarding the management of knowledge assets in research. Additionally, data were gathered from the institutional accreditation outcomes page of the same website. Manifest and latent content analyses were used in reporting the findings of the panel. Contribution: The study will contribute to a greater understanding and acceptance of Knowledge Management (KM) in higher education and extended the body of knowledge concerning knowledge management for the RTN. Findings: The reports revealed a very limited practice of the nexus in terms of people and culture, structure ad processes, and computing and web technologies. A few staff are involved in RTN work, there is an uneven understanding of the RTN among staff, limited joint research between staff and students are some of the reasons for this. Significantly, there is no explicit research framework or policy for the RTN, and systems and/or mechanisms are limited. Further-more, the reports did not account any use of computing and web technologies for the nexus. These limitations can lead to students with less academic, research, and graduate skills. Hence, this study presents a feature design of a KMS that incorporates various RTN best practices, as informed by the reports and literature. The design will allow the staff to utilize the research assets in the classroom, at the same time, engages students in research and scholarly under-takings. Recommendations for Practitioners: All HEIs must have a innovative system that integrates a formal agenda and approach, and set initiatives, strategies, policies, and procedures for knowledge management in utilizing research assets for teaching and learning. It must be designed so that RTN practices remain up-to-date, relevant, and responsive to the needs of the stakeholders, as well as, address academic accreditation challenges. Recommendation for Researchers: Researchers can evaluate the knowledge management of RTN practices of other HEIs outside of Oman to effectively recommend the proper course of action for teaching and learning improvement. Impact on Society: This study will redefine the role and contribution of HEIs, which are key players in advancing a knowledge economy. HEIs are expected to be powerhouses where academic knowledge is discovered, created, disseminated, shared, and re-invented. They must be able to fully grasp the value of managing knowledge to be able to effect positive and purposeful change to the community. Future Research: Future work should include staff and student surveys that examine the knowledge management need of the learning organization to better inform the design of a KMS for the RTN. Thereafter, future research can test the stage to test the effectiveness of the conceptual design.

Aim/Purpose This paper focuses on systematizing and rethinking the conformity of modern transdisciplinarity with its prime cause and initial ideas. Background The difficulties of implementing transdisciplinarity into science and education are connected with the fact that its generally accepted definition, identification characteristics, and methodological features are still missing. In order to eliminate these disadvantages of transdisciplinarity, its prime cause and initial ideas had to be detected. It is also important to analyze the correspondence of the existing opinions about transdisciplinarity with the content of these cause and ideas. Methodology The qualitative analysis of the literature reviews on the subject of transdisciplinary was used in order to determine the correspondence of the opinions about the transdisciplinarity with the meaning of its prime cause and initial ideas. These opinions had to be generalized as well. Through this method, it was possible to detect and classify opinions into 11 groups including 39 stereotypes of transdisciplinarity. For substantiation of transdisciplinary approaches that are consistent with the approaches of contemporary science, C.F. Gauss random variables normal distribution was used. The “Gauss curve” helped to show the place of transdisciplinary and systems transdisciplinary approaches in the structure of academic and systems approaches. The “Gauss curve” also demonstrated the step-by-step “broadening of the scientific worldview horizon due to sequential intensification of synthesis, integration, unification, and generalization of the disciplinary knowledge.” Contribution After reconsideration of the results on qualitative analysis of the literature reviews, the generalized definition of transdisciplinarity could be formulated, including the definition for transdisciplinary and systems transdisciplinary approaches. It was proven that transdisciplinarity is a natural stage for the development of contemporary science and education, and the transdisciplinary approaches were able to suggest the methods and tools to solve the complex and poorly structured problems of science and the society. Findings Many existing stereotypes of transdisciplinarity do not meet its prime cause and initial ideas. Such stereotypes do not have deep philosophic and theoretical substantiation. They also do not suggest the transdisciplinary methods and tools. Thus, the authors of such stereotypes often claim them to be transdisciplinary or suggest perceiving them as transdisciplinarity. This circumstance is the reason why many disciplinary scientists, practitioners, and initiators of higher education view transdisciplinarity as a marginal direction of contemporary science. Based on the generalized definition of transdisciplinarity, as well as its prime cause and initial ideas, it was shown that transdisciplinarity is presented in contemporary science in the form of two different approaches, i.e., the transdisciplinary approach and systems transdisciplinary approach. The objective of the transdisciplinary approach is to ensure science development at the stage of synthesis and integration of disciplinary knowledge, while the objective of the systems transdisciplinary approach is to ensure that the problems of modern society are solved through unification and generalization of the disciplinary knowledge. Recommendation for Researchers The researchers should consider that within the limits of the transdisciplinary approach, the disciplinary specialists are managed. Within the limits of the systems transdisciplinary approach, the disciplinary knowledge is managed. Thus, the transdisciplinary approach is efficient for organization and research with participation of the scientists of the complementary disciplines. An example of such research can be a team of researchers of medical disciplines and complementary disciplines from chemistry, physics, and engineering. The systems transdisciplinary approach is efficient for organization and performance of research with participation of the scientists of non-complementary disciplines such as economics, physics, meteorology, chemistry, ecology, geology, and sociology. Future Research In terms of the main initial idea, transdisciplinarity is formed as a global approach. The global approach should have a traditional institutional form: it should be a science discipline (meta-discipline) and have carriers with the transdisciplinary worldview. Training for such carriers can be organized by the universities within the limits of the systems transdisciplinarity departments and Centers of Systems Transdisciplinary Retraining for Disciplinary Specialists. Thus, it is reasonable to initiate discussion for the idea to reform the disciplinary structure of the universities considering creation of such departments and centers.

Aim/Purpose: Designing augmented reality (AR) experiences for education, health or entertainment involves multidisciplinary teams making design decisions across several areas. The goal of this paper is to present a classification schema that describes the design choices when constructing an AR interactive experience. Background: Existing extended reality schema often focuses on single dimensions of an AR experience, with limited attention to design choices. These schemata, combined with an analysis of a diverse range of AR applications, form the basis for the schema synthesized in this paper. Methodology: An extensive literature review and scoring of existing classifications were completed to enable a definition of seven design dimensions. To validate the design dimensions, the literature was mapped to the seven-design choice to represent opportunities when designing AR iterative experiences. Contribution: The classification scheme of seven dimensions can be applied to communicating design considerations and alternative design scenarios where teams of domain specialists need to collaborate to build AR experiences for a defined purpose. Findings: The dimensions of nature of reality, location (setting), feedback, objects, concepts explored, participant presence and interactive agency, and style describe features common to most AR experiences. Classification within each dimension facilitates ideation for novel experiences and proximity to neighbours recommends feasible implementation strategies. Recommendations for Practitioners: To support professionals, this paper presents a comprehensive classification schema and design rationale for AR. When designing an AR experience, the schema serves as a design template and is intended to ensure comprehensive discussion and decision making across the spectrum of design choices. Recommendations for Researchers: The classification schema presents a standardized and complete framework for the review of literature and AR applications that other researchers will benefit from to more readily identify relevant related work. Impact on Society: The potential of AR has not been fully realized. The classification scheme presented in this paper provides opportunities to deliberately design and evaluate novel forms of AR experience. Future Research: The classification schema can be extended to include explicit support for the design of virtual and extended reality applications.

This project shows a creative approach to the familiar scavenger hunt game. It involved the implementation of an iPhone application, HUNT, with Augmented Reality (AR) capability for the users to play the game as well as an administrative website that game organizers can use to create and make available games for users to play. Using the HUNT mobile app, users will first make a selection from a list of games, and they will then be shown a list of objects that they must seek. Once the user finds a correct object and scans it with the built-in camera on the smartphone, the application will attempt to verify if it is the correct object and then display associated multi-media AR content that may include images and videos overlaid on top of real world views. HUNT not only provides entertaining activities within an environment that players can explore, but the AR contents can serve as an educational tool. The project is designed to increase user involvement by using a familiar and enjoyable game as a basis and adding an educational dimension by incorporating AR technology and engaging and interactive multimedia to provide users with facts about the objects that they have located

Aim/Purpose: This paper aims to present an example of the application of a Knowledge Man-agement (KM) framework to automotive original component manufacturers (OEMs). The objective is to explore KM according to the four pillars of a selected KM framework. Background: This research demonstrates how a framework, namely the George Washington University’s Four Pillar Framework, can be used to determine the KM status of the automotive OEM industry, where knowledge is complex and can influence the complexity of the KM system (KMS) used. Methodology: An empirical study was undertaken using a questionnaire to gather quantitative data. There were 38 respondents from the National Association of Automotive Component and Allied Manufacturers (NAACAM) and suppliers from three major automotive OEMs. The respondents were required to be familiar with the company’s KMS. Contribution: Currently there is a limited body of research available on the KM implementation frameworks for the automotive industry. This study presents a novel approach to the use of a KM framework to reveal the status of KM in automotive OEMs. At the time of writing, the relationship between the four pillars and the complexity of KMS had not yet been determined. Findings: The results indicate that there is a need to improve KM in the automotive OEM industry. According to the relationships investigated, the four pillars, namely leadership, organization, technology and learning, are considered important for KM, regardless of the level of KMS complexity, Recommendations for Practitioners: Automotive OEMs need to ensure that the KM aspects are established and should be periodically evaluated by using a KM framework such as the George Washington University’s Four Pillar Framework to identify KM weaknesses. Recommendation for Researchers: The establishment and upkeep of a successful KM environment is challenging due to the complexity involved with various influencing aspects. To ensure that all aspects are considered in KM environments, comprehensive KM frameworks, such as the George Washington University’s Four Pillar Framework, need to be applied. Impact on Society: The status of KM management and accessibility of knowledge in organizations needs to be periodically examined, in order to improve supplier and OEM knowledge sharing. Future Research: Although the framework used provides a process for KM status determination, this study could be extended by investigating a methodology that includes KMS best practice and tools. This study could be repeated at a national and international level to provide an indication of KM practice within the entire automotive industry.

Aim/Purpose: This work aims to understand why a disparity between the popularity of smart phones and the limited adoption of m-banking exists. Accordingly, this study investigates factors that affect a person’s decision to adopt m-banking services. Such an investigation seeks to determine if and to what extent customers’ mobile phone experience as well as their awareness of m-banking services influence their intention to use such services? Background: This study developed a conceptual model to determine the influence that users’ mobile phone experience as well as users’ awareness of m-banking services had on users’ behavioral intention to use m-banking in Saudi Arabia. Methodology: The quantitative method used to collect data was a survey questionnaire tech-nique. A questionnaire with non-structured (close-ended) questions was formulated. A random sample, targeting banking customers in Saudi Arabia, was selected. This study collected data using a cross-sectional survey. Of those surveyed, 389 provided valid responses eligible for data analysis. SPSS v.22 was used to analyze the data. Contribution: This study produced helpful results and a new m-banking conceptual model. The developed conceptual model focused integrally on users’ awareness and experience as antecedents of m-banking adoption and highlighted the im-portance of differentiating between measuring the users’ characteristics in adopting e-banking in general and m-banking services in particular. In addition, this type of model has the ability to synthesize new control variables as well as to study technology acceptance in developing countries. This study, based on an extended UTAUT model, set out to discover what factors might affect customers’ intentions to use m-banking in Saudi Arabia. Findings: The results show that service awareness has a direct effect on performance and effort expectancy, but not on perceived risk. Moreover, mobile phone experience fails to impact the relationships in the same hypothesized direction. As anticipated, performance expectancy, effort expectancy, and perceived risk have direct and significant effects on behavioral intentions to use m-banking. However, customer awareness fails to impact the relationships of performance expectancy, effort expectancy, and perceived risk on behavioral intentions to use m-banking. Recommendations for Practitioners: Banks should target customers by distributing useful information and applying measures to increase acceptance. Banks need to introduce something imaginative to convince bank customers to abandon existing service channels and adopt m-banking services. Banks should make m-banking services the easiest service for conducting bank transactions and/or help customers conduct transactions that they cannot do any other way. Recommendation for Researchers: Other factors, such as trust, culture, and/or credibility should be investigated along with user’s awareness and experience factors in m-banking services. There is a need to focus on a specific type of m-banking. Thus, it may be fruitful to study the adoption of different systems of m-banking services. Impact on Society: This study suggests that m-banking services should be designed and built based on a deep understanding of customers’ needs using extensive testing to assure that applications and sites function well in a mobile setting. Future Research: Future researchers should apply the conceptual model developed in this study in different settings, different countries, and to different technologies.

Aim/Purpose: This paper reports our implementation of a prototype system, namely PRATO (Proposals Reviewers Automated Taxonomy-based Organization), for automatic assignment of proposals to reviewers based on categorized tracks and partial matching of reviewers’ profiles of research interests against proposal keywords. Background: The process of assigning reviewers to proposals tends to be a complicated task as it involves inspecting the matching between a given proposal and a reviewer based on different criteria. The situation becomes worse if one tries to automate this process, especially if a reviewer partially matches the domain of the paper at hand. Hence, a new controlled approach is required to facilitate the matching process. Methodology: Proposals and reviewers are organized into categorized tracks as defined by a tree of hierarchical research domains which correspond to the university’s colleges and departments. In addition, reviewers create their profiles of research interests (keywords) at the time of registration. Initial assignment is based on the matching of categorized sub-tracks of proposal and reviewer. Where the proposal and a reviewer fall under different categories (sub-tracks), assignment is done based on partial matching of proposal content against re-viewers’ research interests. Jaccard similarity coefficient scores are calculated of proposal keywords and reviewers’ profiles of research interest, and the reviewer with highest score is chosen. The system was used to automate the process of proposal-reviewer assignment at the Umm Al-Qura University during the 2017-2018 funding cycle. The list of proposal-reviewer assignments generated by the system was sent to human experts for voting and subsequently to make final assignments accordingly. With expert votes and final decisions as evaluation criteria, data system-expert agreements (in terms of “accept” or “reject”) were collected and analyzed by tallying frequencies and calculating rejection/acceptance ratios to assess the system’s performance. Contribution: This work helped the Deanship of Scientific Research (DSR), a funding agency at Umm Al-Qura University, in managing the process of reviewing proposals submitted for funding. We believe the work can also benefit any organizations or conferences to automate the assignment of papers to the most appropriate reviewers. Findings: Our developed prototype, PRATO, showed a considerable impact on the entire process of reviewing proposals at DSR. It automated the assignment of proposals to reviewers and resulted in 56.7% correct assignments overall. This indicates that PRATO performed considerably well at this early stage of its development. Recommendations for Practitioners: It is important for funding agencies and publishers to automate reviewing process to obtain better reviewing quality in a timely manner. Recommendation for Researchers: This work highlighted a new methodology to tackle the proposal-reviewer assignment task in an automated manner. More evaluation might be needed with consideration of different categories, especially for partially matched candidates. Impact on Society: The new methodology and knowledge about factors influencing the implementation of automated proposal-reviewing systems will help funding agencies and publishers to improve the quality of their internal processes. Future Research: In the future, we plan to examine PRATO’s performance on different classification schemes where specialty areas can be represented in graphs rather than trees. With graph representation, the scope for reviewer selection can be widened to include more general fields of specialty. Moreover, we will try to record the reasons for rejection to identify accurately whether the rejection was due to improper assignment or other reasons.

Aim/Purpose: The wide use of online banking and technological advancement has attracted the interest of malicious and criminal users with a more sophisticated form of attacks. Background: Therefore, banks need to adapt their security systems to effectively stem threats posed by imposters and hackers and to also provide higher security standards that assure customers of a secured environment to perform their financial transactions. Methodology : The use of authentication techniques that include the mutual secure socket layer authentication embedded with some specific features. Contribution: An approach was made through this paper towards providing a more reliable and complete solution for implementing multi-level user authentication in a banking environment. Findings: The use of soft token as the final stage of authentication provides ease of management with no additional hardware requirement. Recommendations for Practitioners : This work is an approach made towards providing a more reliable and complete solution for implementing multi-level user authentication in a banking environment to stem cybercrime. Recommendation for Researchers: With this approach, a reliable system of authentication is being suggested to stem the growing rate of hacking activities in the information technology sector. Impact on Society :This work if adopted will give the entire populace confidence in carrying out online banking without fear of any compromise. Future Research: This work can be adopted to model a real-life scenario.

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: Although the significance of data provenance has been recognized in a variety of sectors, there is currently no standardized technique or approach for gathering data provenance. The present automated technique mostly employs workflow-based strategies. Unfortunately, the majority of current information systems do not embrace the strategy, particularly biodiversity information systems in which data is acquired by a variety of persons using a wide range of equipment, tools, and protocols. Background: This article presents an automated technique for producing temporal data provenance that is independent of biodiversity information systems. The approach is dependent on the changes in contextual information of data items. By mapping the modifications to a schema, a standardized representation of data provenance may be created. Consequently, temporal information may be automatically inferred. Methodology: The research methodology consists of three main activities: database event detection, event-schema mapping, and temporal information inference. First, a list of events will be detected from databases. After that, the detected events will be mapped to an ontology, so a common representation of data provenance will be obtained. Based on the derived data provenance, rule-based reasoning will be automatically used to infer temporal information. Consequently, a temporal provenance will be produced. Contribution: This paper provides a new method for generating data provenance automatically without interfering with the existing biodiversity information system. In addition to this, it does not mandate that any information system adheres to any particular form. Ontology and the rule-based system as the core components of the solution have been confirmed to be highly valuable in biodiversity science. Findings: Detaching the solution from any biodiversity information system provides scalability in the implementation. Based on the evaluation of a typical biodiversity information system for species traits of plants, a high number of temporal information can be generated to the highest degree possible. Using rules to encode different types of knowledge provides high flexibility to generate temporal information, enabling different temporal-based analyses and reasoning. Recommendations for Practitioners: The strategy is based on the contextual information of data items, yet most information systems simply save the most recent ones. As a result, in order for the solution to function properly, database snapshots must be stored on a frequent basis. Furthermore, a more practical technique for recording changes in contextual information would be preferable. Recommendation for Researchers: The capability to uniformly represent events using a schema has paved the way for automatic inference of temporal information. Therefore, a richer representation of temporal information should be investigated further. Also, this work demonstrates that rule-based inference provides flexibility to encode different types of knowledge from experts. Consequently, a variety of temporal-based data analyses and reasoning can be performed. Therefore, it will be better to investigate multiple domain-oriented knowledge using the solution. Impact on Society: Using a typical information system to store and manage biodiversity data has not prohibited us from generating data provenance. Since there is no restriction on the type of information system, our solution has a high potential to be widely adopted. Future Research: The data analysis of this work was limited to species traits data. However, there are other types of biodiversity data, including genetic composition, species population, and community composition. In the future, this work will be expanded to cover all those types of biodiversity data. The ultimate goal is to have a standard methodology or strategy for collecting provenance from any biodiversity data regardless of how the data was stored or managed.

Aim/Purpose: This research study aimed to explore ecommerce fraud practitioners’ experiences and develop a grounded theory framework to help define an ecommerce fraud incident response process, roles and responsibilities, systems, stakeholders, and types of incidents. Background: With a surge in global ecommerce, online transactions have become increasingly fraudulent, complex, and borderless. There are undefined ecommerce fraud roles, responsibilities, processes, and systems that limit and hinder cyber incident response to fraudulent activities. Methodology: A constructivist grounded theory approach was used to investigate and develop a theoretical foundation of ecommerce fraud incident response based on fraud practitioners’ experiences and job descriptions. The study sample consisted of 8 interviews with ecommerce fraud experts. Contribution: This research contributes to the body of knowledge by helping define a novel framework that outlines an ecommerce fraud incident response process, roles and responsibilities, systems, stakeholders, and incident types. Findings: An ecommerce fraud incident response framework was developed from fraud experts’ perspectives. The framework helps define processes, roles, responsibilities, systems, incidents, and stakeholders. The first finding defined the ecommerce fraud incident response process. The process includes planning, identification, analysis, response, and improvement. The second finding was that the fraud incident response model did not include the containment phase. The next finding was that common roles and responsibilities included fraud prevention analysis, tool development, reporting, leadership, and collaboration. The fourth finding described practitioners utilizing hybrid tools and systems for fraud prevention and detection. The fifth finding was the identification of internal and external stakeholders for communication, collaboration, and information sharing. The sixth finding is that research participants experienced different organizational alignments. The seventh key finding was stakeholders do not have a holistic view of the data and information to make some connections about fraudulent behavior. The last finding was participants experienced complex fraud incidents. Recommendations for Practitioners: It is recommended to adopt the ecommerce fraud response framework to help ecommerce fraud and security professionals develop an awareness of cyber fraud activities and/or help mitigate cyber fraud activities. Future Research: Future research could entail conducting a quantitative analysis by surveying the industry on the different components such as processes, systems, and responsibilities of the ecommerce fraud incident response framework. Other areas to explore and evaluate are maturity models and organizational alignment, collaboration, information sharing, and stakeholders. Lastly, further research can be pursued on the nuances of ecommerce fraud incidents using frameworks such as attack graph generation, crime scripts, and attack trees to develop ecommerce fraud response playbooks, plans, and metrics.

Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

Aim/Purpose: Accurate detection of vacant parking spaces is crucial for autonomous parking. Deep learning, particularly Graph Neural Networks (GNNs), holds promise for addressing the challenges of diverse parking lot appearances and complex visual environments. Our GNN-based approach leverages the spatial layout of detected marking points in around-view images to learn robust feature representations that are resilient to occlusions and lighting variations. We demonstrate significant accuracy improvements on benchmark datasets compared to existing methods, showcasing the effectiveness of our GNN-based solution. Further research is needed to explore the scalability and generalizability of this approach in real-world scenarios and to consider the potential ethical implications of autonomous parking technologies. Background: GNNs offer a number of advantages over traditional parking spot detection methods. Unlike methods that treat objects as discrete entities, GNNs may leverage the inherent connections among parking markers (lines, dots) inside an image. This ability to exploit spatial connections leads to more accurate parking space detection, even in challenging scenarios with shifting illumination. Real-time applications are another area where GNNs exhibit promise, which is critical for autonomous vehicles. Their ability to intuitively understand linkages across marking sites may further simplify the process compared to traditional deep-learning approaches that need complex feature development. Furthermore, the proposed GNN model streamlines parking space recognition by potentially combining slot inference and marking point recognition in a single step. All things considered, GNNs present a viable method for obtaining stronger and more precise parking slot recognition, opening the door for autonomous car self-parking technology developments. Methodology: The proposed research introduces a novel, end-to-end trainable method for parking slot detection using bird’s-eye images and GNNs. The approach involves a two-stage process. First, a marking-point detector network is employed to identify potential parking markers, extracting features such as confidence scores and positions. After refining these detections, a marking-point encoder network extracts and embeds location and appearance information. The enhanced data is then loaded into a fully linked network, with each node representing a marker. An attentional GNN is then utilized to leverage the spatial relationships between neighbors, allowing for selective information aggregation and capturing intricate interactions. Finally, a dedicated entrance line discriminator network, trained on GNN outputs, classifies pairs of markers as potential entry lines based on learned node attributes. This multi-stage approach, evaluated on benchmark datasets, aims to achieve robust and accurate parking slot detection even in diverse and challenging environments. Contribution: The present study makes a significant contribution to the parking slot detection domain by introducing an attentional GNN-based approach that capitalizes on the spatial relationships between marking points for enhanced robustness. Additionally, the paper offers a fully trainable end-to-end model that eliminates the need for manual post-processing, thereby streamlining the process. Furthermore, the study reduces training costs by dispensing with the need for detailed annotations of marking point properties, thereby making it more accessible and cost-effective. Findings: The goal of this research is to present a unique approach to parking space recognition using GNNs and bird’s-eye photos. The study’s findings demonstrated significant improvements over earlier algorithms, with accuracy on par with the state-of-the-art DMPR-PS method. Moreover, the suggested method provides a fully trainable solution with less reliance on manually specified rules and more economical training needs. One crucial component of this approach is the GNN’s performance. By making use of the spatial correlations between marking locations, the GNN delivers greater accuracy and recall than a completely linked baseline. The GNN successfully learns discriminative features by separating paired marking points (creating parking spots) from unpaired ones, according to further analysis using cosine similarity. There are restrictions, though, especially where there are unclear markings. Successful parking slot identification in various circumstances proves the recommended method’s usefulness, with occasional failures in poor visibility conditions. Future work addresses these limitations and explores adapting the model to different image formats (e.g., side-view) and scenarios without relying on prior entry line information. An ablation study is conducted to investigate the impact of different backbone architectures on image feature extraction. The results reveal that VGG16 is optimal for balancing accuracy and real-time processing requirements. Recommendations for Practitioners: Developers of parking systems are encouraged to incorporate GNN-based techniques into their autonomous parking systems, as these methods exhibit enhanced accuracy and robustness when handling a wide range of parking scenarios. Furthermore, attention mechanisms within deep learning models can provide significant advantages for tasks that involve spatial relationships and contextual information in other vision-based applications. Recommendation for Researchers: Further research is necessary to assess the effectiveness of GNN-based methods in real-world situations. To obtain accurate results, it is important to employ large-scale datasets that include diverse lighting conditions, parking layouts, and vehicle types. Incorporating semantic information such as parking signs and lane markings into GNN models can enhance their ability to interpret and understand context. Moreover, it is crucial to address ethical concerns, including privacy, potential biases, and responsible deployment, in the development of autonomous parking technologies. Impact on Society: Optimized utilization of parking spaces can help cities manage parking resources efficiently, thereby reducing traffic congestion and fuel consumption. Automating parking processes can also enhance accessibility and provide safer and more convenient parking experiences, especially for individuals with disabilities. The development of dependable parking capabilities for autonomous vehicles can also contribute to smoother traffic flow, potentially reducing accidents and positively impacting society. Future Research: Developing and optimizing graph neural network-based models for real-time deployment in autonomous vehicles with limited resources is a critical objective. Investigating the integration of GNNs with other deep learning techniques for multi-modal parking slot detection, radar, and other sensors is essential for enhancing the understanding of the environment. Lastly, it is crucial to develop explainable AI methods to elucidate the decision-making processes of GNN models in parking slot detection, ensuring fairness, transparency, and responsible utilization of this technology.

Aim/Purpose: This study aims to analyze the factors that influence user addiction to AR face filters in social network applications and their impact on the online social anxiety of users in Indonesia. Background: To date, social media users have started to use augmented reality (AR) face filters. However, AR face filters have the potential to create positive and negative effects for social media users. The study combines the Big Five Model (BFM), Sense of Virtual Community (SVOC), and Stimuli, Organism, and Response (SOR) frameworks. We adopted the SOR theory by involving the personality factors and SOVC factors as stimuli, addiction as an organism, and social anxiety as a response. BFM is the most significant theory related to personality. Methodology: We used a quantitative approach for this study by using an online survey. We conducted research on 903 Indonesian respondents who have used an AR face filter feature at least once. The respondents were grouped into three categories: overall, new users, and old users. In this study, group classification was carried out based on the development timeline of the AR face filter in the social network application. This grouping was carried out to facilitate data analysis as well as to determine and compare the different effects of the factors in each group. The data were analyzed using the covariance-based structural equation model through the AMOS 26 program. Contribution: This research fills the gap in previous research which did not discuss much about the impact of addiction in using AR face filters on online social anxiety of users of social network applications. Findings: The results of this study indicated neuroticism, membership, and immersion influence AR face filter addiction in all test groups. In addition, ARA has a significant effect on online social anxiety. Recommendations for Practitioners: The findings are expected to be valuable to social network service providers and AR creators in improving their services and to ensure policies related to the list of AR face filters that are appropriate for use by their users as a form of preventing addictive behavior of that feature. Recommendation for Researchers: This study suggested other researchers consider other negative impacts of AR face filters on aspects such as depression, life satisfaction, and academic performance. Impact on Society: AR face filter users may experience changes in their self-awareness in using face filters and avoid the latter’s negative impacts. Future Research: Future research might explore other impacts from AR face filter addiction behavior, such as depression, life satisfaction, and so on. Apart from that, future research might investigate the positive impact of AR face filters to gain a better understanding of the impact of AR face filters.

Aim/Purpose: This study examines the adoption and continued use of m-learning in Iraqi universities amidst an unstable environment by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Expectation-Confirmation Model (ECM) models. The primary goal is to address the specific challenges and opportunities in Iraq’s higher education institutions (HEIs) due to geopolitical instability and understand their impact on student acceptance, satisfaction, and continued m-learning usage. Background: The research builds on the growing importance of m-learning, especially in HEIs, and recognizes the unique challenges faced by institutions in Iraq, given the region’s instability. It identifies gaps in existing models and proposes extensions, introducing the variable “civil conflicts” to account for the volatile context. The study aims to contribute to a deeper understanding of m-learning acceptance in conflict-affected regions and provide insights for improving m-learning initiatives in Iraqi HEIs. Methodology: To achieve its objectives, this research employed a quantitative survey to collect data from 399 students in five Iraqi universities. PLS-SEM is used for the analysis of quantitative data, testing the extended UTAUT2 and ECM models. Contribution: The study’s findings are expected to contribute to the development of a nuanced understanding of m-learning adoption and continued usage in conflict-affected regions, particularly in the Iraqi HEI context. Findings: The study’s findings may inform strategies to enhance the effectiveness of m-learning initiatives in Iraqi HEIs and offer insights into how education can be supported in regions characterized by instability. Recommendations for Practitioners: Educators and policymakers can benefit from the research by making informed decisions to support education continuity and quality, particularly in conflict-affected areas. Recommendation for Researchers: Researchers can build upon this study by further exploring the adoption and usage of m-learning in unstable environments and evaluating the effectiveness of the proposed model extensions. Impact on Society: The research has the potential to positively impact society by improving access to quality education in regions affected by conflict and instability. Future Research: Future research can expand upon this study by examining the extended model’s applicability in different conflict-affected regions and assessing the long-term impact of m-learning initiatives on students’ educational outcomes.

Breast cancer is leading cause of mortality among women compared to other types of cancers. Hence, early breast cancer diagnosis is crucial to the success of treatment. Various pathological and imaging tests are available for the diagnosis of breast cancer. However, it may introduce errors during detection and interpretation, leading to false-negative and false-positive results due to lack of pre-processing of it. To overcome this issue, we proposed a effective image pre-processing technique-based on Otsu's thresholding and single-seeded region growing (SSRG) to remove artefacts and segment the pectoral muscle from breast mammograms. To validate the proposed method, a publicly available MIAS dataset was utilised. The experimental finding showed that proposed technique improved 18% breast cancer detection accuracy compared to existing methods. The proposed methodology works efficiently for artefact removal and pectoral segmentation at different shapes and nonlinear patterns.

Large graphs or automata have their data that cannot fit in a single machine, or may take unreasonable time to be processed. We implement with MapReduce and Giraph two algorithms for intersecting and minimising large and distributed automata. We provide some comparative analysis, and the experiment results are depicted in figures. Our work experimentally validates our propositions as long as it shows that our choice, in comparison with MapReduce one, is not only more suitable for graph-oriented algorithms, but also speeds the executions up. This work is one of the first steps of a long-term goal that consists in a high level distributed graph processing language.

This study aims to identify and understand the perspectives of automotive aftersales stakeholders regarding current challenges posed by decarbonisation strategies. It examines potential responses that the automotive aftersales business could undertake to address these challenges. Semi-structured interviews were undertaken with automotive industry experts from Europe and Latin America. This paper focuses primarily on impacts of decarbonisation upon automotive aftersales and the potential role of data in that business. Results show that investment in technology will be a condition for businesses that want to remain active in the industry. Furthermore, experts agree that incumbent manufacturers are not filling the technology gap that the energy transition is creating in the automotive sector, a consequence of which will be the entrance of new players from other sectors. The current aftersales businesses will potentially lose bargaining control. Moreover, policy makers are seen as unreliable leaders of the transition agenda.

Aim/Purpose: The research investigates the impact of Information and Communication Technologies (ICT) on Iranian refugees’ settlement in Australia. Background: The study identifies the issues of settlement, such as language, cultural and social differences. Methodology: The Multi-Sited Ethnography (MSE), which is a qualitative methodology, has been used with a thematic analysis drawing on a series of semi-structured interviews with two groups of participants (51 Iranian refugees and 55 people with a role in assisting refugees). Contribution: The research findings may enable the creation of a model for use by the Aus-tralian Government with Iranian refugees. Findings: The findings show the vital role ICT play in refugees’ ongoing day-to-day life towards settlement. Recommendations for Practitioners: The results from this paper could be generalised to other groups of refugees in Australia and also could be used for Iranian refugees in other countries. Recommendation for Researchers: Researchers may use a similar study for refugees of different backgrounds in Australia and around the world. Impact on Society: ICT may assist refugees to become less isolated, less marginalized and part of mainstream society. Future Research: Future research could look into the digital divide between refugees in Australia and main stream Australians.

Aim/Purpose: This paper presents some of the issues that academia faces in both the detection of plagiarism and the aftermath. The focus is on the latter, how academics and educational institutions around the world can address the challenges that follow the identification of an incident. The scope is to identify the need for and describe specific strategies to efficiently manage plagiarism incidents. Background: Plagiarism is possibly one of the major academic misconduct offences. Yet, only a portion of Higher Education Institutes (HEIs) appear to have well developed policies and procedures aimed at dealing with this issue or to follow these when required. Students who plagiarize and are not caught pose challenges for academia. Students who are caught pose equal challenges. Methodology: Following a literature review that identifies and describes the extent and the seriousness of the problem, procedures and strategies to address the issue are recommended, based on the literature and best practices. Contribution: The paper alerts academics regarding the need for the establishment of rigorous and standardized procedures to address the challenges that follow the identification of a plagiarism incident. It then describes how to streamline the process to improve consistency and reduce the errors and the effort required by academic staff. Recommendations for Practitioners: To ensure that what is expected to happen takes place, HEIs should structure the process of managing suspected plagiarism cases. Operationalization, workflow automation, diagrams that map the processes involved, clear in-formation and examples to support and help academics make informed and consistent decisions, templates to communicate with the offenders, and data-bases to record incidents for future reference are strongly recommended. Future research: This paper provides a good basis for further research that will examine the plagiarism policy, the procedures, and the outcome of employing the procedures within the faculties of a single HEI, or an empirical comparison of these across a group of HEIs. Impact on Society: Considering its potential consequences, educational institutions should strive to prevent, detect, and deter plagiarism – and any type of student misconduct. Inaction can be harmful, as it is likely that some students will not gain the appropriate knowledge that their chosen profession requires, which could put in danger both their wellbeing and the people they will later serve in their careers.

Aim/Purpose: Most research on work-based learning and research relates to theory, including perspectives, principles and curricula, but few studies provide contemporary examples of work-based projects, particularly in the Australian context; this paper aims to address that limitation. Background: The Professional Studies Program at University of Southern Queensland is dedicated to offering advanced practice professionals the opportunity to self-direct organizational and work-based research projects to solve real-world workplace problems; two such examples in the Australian context are provided by this paper. Methodology: The paper employs a descriptive approach to analyzing these two work-based research projects and describes the mixed methods used by each researcher. Contribution: The paper provides examples of work-based research in (a) health, safety, and wellness leadership and its relation to corporate performance; and (b) investigator identity in the Australian Public Service; neither topic has been examined before in Australia and little, if anything, is empirically known about these topics internationally. Findings: The paper presents the expected outcomes for each project, including discussion of the ‘triple dividend’ of personal, organizational, and practice domain benefits; as importantly, the paper presents statements of workplace problems, needs and opportunities, status of the practice domain, background and prior learning of the researchers, learning objectives, work-based research in the practice domain, and lessons learned from research which can be integrated into a structured framework of advanced practice. Recommendations for Practitioners: This is a preliminary study of two work-based research projects in Australia; as these and other real-world projects are completed, further systematic and rigorous reports to the international educational community will reveal the granulated value of conducting projects designed to change organisations and concordant practice domains. Recommendation for Researchers: While introducing the basic elements of research methods and expected out-comes of work-based projects, examples in this paper give only a glimpse into the possible longer-term contributions such research can make to workplaces in Australia. Researchers, as a consequence, need to better understand the relationship between practice domains, research as a valuable investigative tool in workplaces, and organizational and social outcomes. Impact on Society: Work-based learning and research have been developed to not only meet the complex and changing demands of the global workforce but have been implemented to address real-world organizational problems for the benefit of society; this paper provides two examples where such benefit may occur. Future Research: Future research should focus on the investigation of triple-dividend outcomes and whether they are sustainable over the longer term.

Aim/Purpose: Work-based learning has been identified in the literature, and is established in academia and in the global worlds of work; however, an examination of work-based research, particularly at the doctoral level, has been less well articulated. Moreover, a paucity of published literature on either work-based research or Professional Studies means little is known about the dynamics and drivers of these domains. This study aims to begin addressing the shortfall in literature on work-based research and Professional Studies programs, using the program at University of Southern Queensland as an example Background: This paper examines work-based research in the context of the Professional Studies program at University of Southern Queensland in Australia, with which the authors are affiliated. Methodology: Analysis of work-based research includes discussion of ‘messy’ research environments and the changing nature of workplaces, along with the opportunities and challenges such environments pose for action researchers. Contribution: In addition to addressing a shortfall in the published literature on work-based research, the paper also contributes insight into the mechanisms used to promote reflective practice and the generation of professional artefacts. Findings: Often driven by altruism, work-based research as implemented in the Professional Studies program results in a so-called ‘triple dividend’, designed to benefit the individual researcher, work environment, and community of practice. Recommendations for Practitioners: To be successful contributors to work-based research, practitioners need to reflect carefully and deeply on experience, planning and outcomes, using what in this paper we call ‘micro-reflective’ (personal) and ‘macro-reflective’ (program) cycles of reflection. Recommendation for Researchers: In addition to generating new knowledge and expanding the frontiers of workplaces, work-based research is often motivated by complicated and wide-reaching imperatives; work-based researchers therefore need to consider the goals, objectives, priorities and vision of their work environments, as well as understand issues related to bias, ethical practice and the nature of insider research. Impact on Society: Work-based learning and research address the complexities, challenges and future demands of Australian workplaces along with the work, mobility and personal development needs of mid- to senior-career professionals. Future Research: In addition to the multitude of action research programs possible in work-places in Australia, more research is needed to understand higher education work-based learning and its relation to, and impact on, work-based research, particularly when applying mixed methods research to work environments.