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The complexity and uncertainty of healthcare operations increasingly require agility to safeguard a high quality of care. Using a microfoundations of dynamic capabilities perspective, this study investigates the effects of nurses' implicit voice theories (IVTs) on the behaviors that influence their individual agility.

This research uses quantitative survey data collected from 2,552 Canadian nurses during the fourth wave of the Covid-19 pandemic in the fall of 2021. Structural equation modeling is used to test a conceptual model that hypothesizes the effects of three different IVTs on nurses' creativity, spontaneity, agility and the quality of care they deliver to patients.

The results reveal that voice-inhibiting cognitions (like “suggestions are criticisms for higher-ups”, “I first need a solution or solid data”, and “speaking up has negative repercussions”) negatively impact nurses' creativity and spontaneity in crafting solutions to problems they face daily. In turn, this affects nurses' individual agility as they attempt to adapt to changing circumstances and, ultimately, the quality of care they provide to their patients.

Even if organizations have little control over employees' pre-held beliefs regarding voice, they can still reverse them by developing and nurturing a voice-welcoming culture to boost their workers' agility.

This study combines two theoretical frameworks, voice theory and dynamic capabilities theory, to study how individual-level factors (cognitions and behaviors) contribute to nurses' individual agility and the quality of care they provide to their patients. It answers the recent calls of scholars to study the mechanisms through which healthcare operations can develop and sustain dynamic capabilities, such as agility, and better face the “new normal”.

This study explored how the use of different social media is related to subjective well-being among university students during the COVID-19 pandemic in Japan.

We surveyed 1,681 university students in the Kanto region of Japan in May 2021 to investigate how social media use relates to subjective well-being. We also examined the effects of self-consciousness and friendship, self-presentation desire, generalized trust, online communication skills, depression tendency and social support from others.

The responses revealed 15 possible patterns of social media usage on four widely used social media in Japan (LINE, Twitter, Instagram and Facebook). We selected users with the top five patterns for further statistical analyses: LINE/Twitter/Instagram/Facebook, LINE/Twitter/Instagram, LINE/Twitter, LINE/Instagram and LINE only. Overall, self-establishment as a factor of self-consciousness and friendship, and social support from others had positive effects on the improvement of subjective well-being, whereas depression tendency had negative effects on their subjective well-being regardless of their usage patterns, of which the results of social support from others and depression tendency were consistent with the results of previous studies. Regarding other factors, they had different effects on subjective well-being due to different patterns. Effects on subjective well-being from self-indeterminate and self-independency as factors of self-consciousness and friendship, praise acquisition, self-appeal and topic avoidance as factors of self-presentation desire were observed.

This is among the earliest studies on the relationship between young generations’ social media use and subjective well-being through social media usage patterns during the COVID-19 pandemic in Japan.

The recent COVID-19 pandemic caused a severe economic downturn. Employees working in these organisations face employment uncertainty. The pandemic disrupted their daily routines, and it added a layer of complexity to the already resource-constrained environment. During these times, employees would conserve their resources to maintain competitiveness, one of which is knowledge hiding. While economic activities are resuming, the appearance of new variants could mean the transition towards endemicity could be put on hold. Hence, there is a need to rethink the behaviour of employees as they would have elevated levels of anxiety towards resuming daily work activities. Therefore, this study aims to address the question of understanding employees’ perspectives toward knowledge sharing and knowledge hiding.

Drawing on the conservation of resources theory, social learning theory and the social exchange theory (SET), a conceptual framework involving ethical leadership was developed to examine if knowledge hiding or knowledge sharing behaviour is a resource for employees during these times. The partial least squares method of structural equation modelling was used to analyse results from 271 white-collar employees from Singapore.

The results show that ethical leadership encourages knowledge sharing but does not reduce knowledge hiding. At the same time, knowledge hiding, not knowledge sharing, improves one’s perception of work performance. Additionally, psychological safety is the key construct that reduces knowledge hiding and encourages sharing behaviour.

Overall, this study extends the theories, demonstrating that, first and foremost, knowledge hiding is a form of resource that provides employees with an added advantage in work performance during the endemic. At the same time, we provide a new perspective that ethical leaders’ demonstration of integrity, honesty and altruism alone is insufficient to encourage knowledge sharing or reduce knowledge hiding. It must lead to a psychologically safe environment.

Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

Several conceptual works suggest that more digitalized information systems in management accounting have the potential to make this corporate function more effective. Against this backdrop, this study aims to investigate the impact of information systems quality in management accounting on the effectiveness of management control systems. Additionally, this study examines the moderating effect of process automation.

A cross-sectional survey of 125 German Mittelstand firms and hierarchical regression analyses were used for data collection and analysis.

The findings confirm the assumed positive effect of information systems quality in management accounting on management control effectiveness. They also confirm the assumed moderating effect of process automation. The authors find that the relationship between information systems quality in management accounting and management control effectiveness is more pronounced if the firm features a higher degree of process automation.

Several earlier case studies and a few quantitative studies indicated the potentially positive effect of high-quality information systems in management accounting on management control effectiveness. To the best of the authors‘ knowledge, this study is among the first to deliver quantitative proof of this relationship in the context of German Mittelstand firms. Moreover, the authors add to this literature the moderating effect of process automation in the relationship between information systems quality in management accounting and management control effectiveness.

Social media celebrities are getting popular in promotions, and more people have experienced social media to receive information on diet and health tips. This research presents a study showing how social media can influence young people's diet behavior through collaboration with social media celebrities.

Through recruiting on various social media and online education forums, this research recruited 196 young Hongkongers to participate in an online survey developed based on the AIDA (Action, Interest, Desire, Attention) marketing communication model on how youths access diet information from social media celebrities in Hong Kong.

Hong Kong youths consume diet information from social media celebrities through instant messaging systems, social networking sites and online videos, and, in particular, information on food calories and nutrition. However, sponsorship from vendors would decrease their desire to agree with the messages from social media celebrities. After receiving this information, some participants would follow tips and guides from social media celebrities. However, they seldom share such information. Interestingly, males are more willing to follow these tips and guides.

First, this study fills the gap of prior research, which did not study much on how social media celebrities contribute to diet promotion to youths in Asia. Second, through the AIDA Model, this study shows how social media can affect the awareness and accessibility of diet information by young Hongkongers, followed by initiating their interest in this topic and retrieving more relevant information. Furthermore, the authors further understand their desire to follow and improve their behavior as promoted by social media influencers and how they practice such behavior. Based on these findings, health-related brands could consider using social media influencers helping to promote their products and services, and these brands could further use social media to secure customer engagement.

This research uses a multifaceted approach to develop an MPA/MPP curriculum to support a data science track within the existing MPA/MPP programs by identifying the core and elective areas needed.

The approach includes (1) identifying a suitable structure for MPA/MPP programs which can allow the program to develop its capacity to train students with the data science and general public administration skills to solve public policy problems and leave explicit space for local experimentation and modification; (2) defining bridging modules and required modules for the MPA/MPP programs; and (3) developing of data science track thought to make suggestions for the inclusion of suitable data science modules into the data science track and benchmarking the data science modules suggested with the best practices developed by other professional bodies. The authors review 46 NASPAA-accredited MPA/MPP programs from 40 (or 22.7%) schools to identify the suitable required modules and some potential data science and analytics courses that MPA/MPP programs currently provide as electives.

The proposal includes a three-course (six–nine credits, not counted in the program but as prerequisites) bridging module, a nine-course (27 credits) required module and a five-course (15 credits) data science track/concentration.

This work can provide a starting point for the public administration education community to develop graduate programs focusing on data science to cater to the needs of both public managers and society at large.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

This study aims to offer empirical evidence on how integrated thinking affects the integrated reporting (IR) practice and how integrated thinking originates from board and management involvement, cross-functional integration and integral link between capitals and strategies.

This study is cross-sectional and uses a mixed-method approach. The empirical data for the quantitative approach were collected from the 129 public companies listed on Colombo Stock Exchange in Sri Lanka. The personale responsible for preparing the annual report are selected as the respondents of this study. This study used partial least square modelling to test the hypotheses. The quantitative approach results are triangulated across a qualitative research approach in semi-structural interviews with ten responsible officers of integrated reporting practices.

The central finding of this study is the significant positive relationship between integrated thinking and integrated reporting practice. The qualitative results supported the quantitative findings and show that board and management involvement, cross-functional integration and integral link between capital and strategy enhance the integrated reporting practice. Top management and board management have positive beliefs about the integrated reporting practice; they initiate, encourage, influence, involve and support it. Furthermore, all company departments are involved with the integrated reporting led by the finance department and practice good coordination, communication and collaboration between departments. Moreover, it also evidenced their concern about the linkage between capital and strategy and how they do it in their organisation when practising integrated reporting.

The firms which intend to practice or enhance integrated reporting will be benefited from this study. Hence, this research assists in constructing IT through the direct role of the board and senior leadership, breaking down silos to diffuse IR throughout structures and processes, and concentrating on strategies while managing their capitals and relationships over the long term.

This study provides the initial quantitative empirical evidence on the impact of integrated thinking on integrated reporting practice. To the best of the authors’ knowledge, this study is the first to operationalise both integrated thinking and integrated reporting based on a questionnaire that developed and tested both constructs as higher-order reflective formative and on the relationship between integrated thinking and integrated reporting. The mixed-method approach to examine the relationship between integrated thinking and integrated reporting provides additional insights into the existing literature.

By far, the corpus for the quotation extraction and quotation attribution tasks in Indonesian is still limited in quantity and depth. This study aims to develop an Indonesian corpus of public figure statements attributions and a baseline model for attribution extraction, so it will contribute to fostering research in information extraction for the Indonesian language.

The methodology is divided into corpus development and extraction model development. During corpus development, data were collected and annotated. The development of the extraction model entails feature extraction, the definition of the model architecture, parameter selection and configuration, model training and evaluation, as well as model selection.

The Indonesian corpus of public figure statements attribution achieved 90.06% agreement level between the annotator and experts and could serve as a gold standard corpus. Furthermore, the baseline model predicted most labels and achieved 82.026% F-score.

To the best of the authors’ knowledge, the resulting corpus is the first corpus for attribution of public figures’ statements in the Indonesian language, which makes it a significant step for research on attribution extraction in the language. The resulting corpus and the baseline model can be used as a benchmark for further research. Other researchers could follow the methods presented in this paper to develop a new corpus and baseline model for other languages.