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This paper aims to evaluate the Digital Government Collaborative Platform (DGCP), which facilitates collaborations between the citizens and the government to address environmental issues in Sri Lanka. The DGCP is an artifact developed by the value-sensitive design approach.

The DGCP is evaluated following the Framework for Evaluation in Design Science Research (FEDS). In total, 224 citizens participated in the survey based on the User Experience Questionnaire (UEQ) and open questions about human values embedded in the design. Fifteen government officers were interviewed to enhance the evaluation.

The DGCP received positive evaluations from the citizens and government officers. The platform is attractive, novel and pragmatic, also generating hedonic experiences for the citizens. The users believed that human values are reflected in the DGCP. Furthermore, they shared a few suggestions to improve it.

The paper contributes knowledge to evaluating digital government systems, especially in developing countries. The human-value-centered DGCP was evaluated using multiple methods of quantitative (i.e. UEQ Survey) and qualitative (i.e. qualitative interviews with stakeholders) techniques. Furthermore, the systematic process of DGCP evaluation produces a case-based guideline for evaluating related and similar digital government systems using FEDS.

Service robots are expected to become increasingly common, but the ways in which they can move around in an environment with humans, collect and store data about humans and share such data produce a potential for privacy violations. In human-to-human contexts, such violations are transgression of norms to which humans typically react negatively. This study examines if similar reactions occur when the transgressor is a robot. The main dependent variable was the overall evaluation of the robot.

Service robot privacy violations were manipulated in a between-subjects experiment in which a human user interacted with an embodied humanoid robot in an office environment.

The results show that the robot's violations of human privacy attenuated the overall evaluation of the robot and that this effect was sequentially mediated by perceived robot morality and perceived robot humanness. Given that a similar reaction pattern would be expected when humans violate other humans' privacy, the present study offers evidence in support of the notion that humanlike non-humans can elicit responses similar to those elicited by real humans.

The results imply that designers of service robots and managers in firms using such robots for providing service to employees should be concerned with restricting the potential for robots' privacy violation activities if the goal is to increase the acceptance of service robots in the habitat of humans.

To date, few empirical studies have examined reactions to service robots that violate privacy norms.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

Qualitative research that involves the use of human participants calls for the need to protect those participants to give their honest view during data collection. This is an important part of every primary data collection in qualitative studies using interviews. This paper aims to investigate all available ethical considerations that need to be observed by the researcher when conducting primary data collection through interview and to explore the theories that underpin the ethics in qualitative studies.

This paper systemically reviewed existing qualitative data on ethics and gathered information that were analysed and presented on the topic area.

The findings show that ethical considerations deal with the various approaches adopted by the researcher to make the participants feel safe to participate in any given researcher. During an interview process in qualitative research, the findings show that anonymity, voluntary participation, privacy, confidentiality, option to opt out and avoiding misuse of findings are ethical considerations that must be observed by the researcher. The outcome of the investigation also shows that deontology and utilitarianism, rights and virtue are the main theories that underpin ethical considerations in research.

The rights of the research participants need to be respected in qualitative research to assist in gathering accurate information to achieve the objectives of study. This and other ethical principles such as anonymity, privacy, confidentiality, voluntary participation and option to opt out guide the researcher to systematically adhere to data collection approaches that yield valid results in qualitative data collection using interviews.

This paper examines the relationship between marketing automation emergence and the marketers' use of heuristics in their decision-making processes. Heuristics play a role for the integration of human decision-making models and automation in augmentation processes, particularly in marketing where automation is widespread.

This study analyzes qualitative data about the impact of marketing automation on the scope of heuristics in decision-making models, and it is based on evidence collected from interviews with twenty-two experienced marketers.

Marketers make extensive use of heuristics to manage their tasks. While the adoption of new automatic marketing tools modify the task environment and field of use of traditional decision-making models, the adoption of heuristics rules with a different scope is essential to defining inputs, interpreting/evaluating outputs and control the marketing automation system.

The paper makes a contribution to research on the relationship between marketing automation and decision-making models. In particular, it proposes the results of in-depth interviews with senior decision makers to assess the impact of marketing automation on the scope of heuristics as decision-making models adopted by marketers.

This paper aims to investigate the relationship between global economic fluctuations and human development through four transmission channels (foreign direct investment (FDI), official development aid (ODA), remittances and export earnings) in Egypt as an open developing economy, in the period 1990–2015.

The paper uses a vector autoregressive model, which implies examining the impulse response functions and variance decompositions.

The results indicate that human development is negatively affected by global economic fluctuations through the four channels, namely, ODA, FDI, export earnings and remittances. In addition, the most effective transmission channels are FDI in the short run and export earnings in the long run.

While a large body of literature addresses the direct impact of business cycles and economic shocks on human development, only some studies focus on the indirect impact. The contribution is to identify the indirect impact of global economic fluctuations on human development in a developing economy, considering four transmission channels and to determine the most important of these channels. Moreover, using the human development index is an addition in this paper as most previous literature depends on other human development indicators such as children’s health, employment and schooling.

The purpose of the study was to specify the perceived outdated nature and lack of definitional clarity associated with the concept of work and further to outline that the nature of work has dramatically changed in the 21st century, while definitions of work referenced in research remain those that were dominant in the previous century. Lastly, the study aimed to propose an updated conceptualisation and definition of work to aid future research.

A scoping literature review was adopted as the methodology guiding the study. A scoping review is particularly suited to identifying the conceptual boundaries on a given multi-disciplinary topic and is used to map the key concepts underpinning a research area as well as to clarify working definitions.

Nine main themes underpinning the concept of work were extracted from the extant literature. These were assimilated with contemporary literature across multiple disciplines. Contexts of work as they relate to dimensions of work and workspace are developed and visualised. A proposed contemporary definition of work is presented.

The aim of the study was to address the problem with current and future research continuing to refer to traditional conceptualisations of work, while the nature of work has dramatically changed. The findings are preliminary and intended to stimulate further discourse towards a greater consensus of a definition. The implications of proposing an updated definition of work is that it is intended to better inform future research reflective of its multi-disciplinary and significantly changed nature.

The implications to practice are the main impetus of this study. The authors found that research associated with work was being confounded by traditional and outdated interpretations, excluding alternative forms of work or not recognising its multi-dimensionality. It is proposed by the paper that an updated conceptualisation of the nature of work in this era, as it is reflected across disciplines and practice, would positively contribute to the understanding, management and conceptualisation of work in practice.

A systematic literature review across disciplines of the definition of work will reveal the outdated nature and disparate interpretation of the concept of work. An inclusive, multi-disciplinary and contemporary definition of work has not been suggested. This scoping review was conducted to address this problem and gap in the literature. Further, this paper presents a multi-dimensional and spatial conceptualisation of work that is proposed to better inform future research and practice associated with work.

This paper aims to critically examine the digital transformation of anti-money laundering (AML) and countering the financing of terrorism (CFT) in light of the Financial Action Task Force (FATF) San Jose principles, the Organisation for Economic Co-operation and Development (OECD) principles for artificial intelligence (AI) and the proposed European Union (EU) Artificial Intelligence Act. The authors argue that AI tools can revolutionize AML/CFT and asset recovery, but there is a need to strike a balance between optimizing AML efficiency and safeguarding fundamental rights.

This paper draws on reports, legislation, legal scholarships and other open-source data on the digital transformation of AML/CFT, particularly the deployment of AI in this context.

A new regulatory framework with robust safeguards is necessary to mitigate the risks associated with the use of new technologies in the AML context.

This study is one of the first to examine the use of AI in the AML/CFT context in light of the FATF San Jose principles, the OECD AI principles and the proposed EU AI Act.

Social hackathons are events designed to craft social change using technology that enables citizen empowerment or addresses societal issues by deploying data. Hackathons provide a framework for organizing to help create prototypes and business models through interaction with technology. The relevance of the sociomateriality of the emergent technology (prototype) and organizational structure raises the question if viable and impactful solutions can be developed within such frames.

This study applies an inductive research methodology based on ethnographic participant observation, interviews with participants and event organizers, and qualitative insights from surveys.

Events such as social hackathons are centered around technology and share a vision of creating opportunities for change. The materiality of prototypes may define their interaction patterns. The differentiation of the embodiment and emergent structuration of technology may be a breaking point for in-group dynamics and a barrier to social innovation. The emergent structuration of technology with a longer initial phase of problem definition and ideation within a group was found to have more potential for impactful embodiment with the technological artifact. Some cases reveal that “expert” participants who shared visions of change enabled by technology were constrained by other members.

The paper suggests an extended view on the connection of sociomateriality, organizing and social impact.

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.