Search results

This pilot study aimed to evaluate the impact of the big five personality traits on user engagement with chatbots at the early stages of artificial intelligence (AI) adoption.

The pilot study involved 62 participants segmented into two groups to measure variables including engagement duration, task performance and future AI usage intentions.

The findings advocate for the incorporation of psychological principles into technology design to facilitate more tailored and efficient human–AI collaboration.

This pilot research study highlights the relationship between the big five personality traits and chatbot usage and provides valuable insights for customizing chatbot development to align with specific user characteristics. This will serve to enhance both user satisfaction and task productivity.

This study investigates the relationships between team dynamics and performance in healthcare operations. Specifically, it explores, through wearable sensors, how team coordination mechanisms can influence the likelihood of surgical glitches during routine surgery.

Breast surgeries of a large Italian university hospital were monitored using Sociometric Badges – wearable sensors developed at MIT Media Lab – for collecting objective and systematic measures of individual and group behaviors in real time. Data retrieved were used to analyze team coordination mechanisms, as it evolved in the real settings, and finally to test the research hypotheses.

Findings highlight that a relevant portion of glitches in routine surgery is caused by improper team coordination practices. In particular, results show that the likelihood of glitches decreases when practitioners adopt implicit coordination mechanisms rather than explicit ones. In addition, team cohesion appears to be positively related with the surgical performance.

For the first time, direct, objective and real time measurements of team behaviors have enabled an in-depth evaluation of the team coordination mechanisms in surgery and the impact on surgical glitches. From a methodological perspective, this research also represents an early attempt to investigate coordination behaviors in dynamic and complex operating environments using wearable sensor tools.

This study aims to enrich the current theoretical debate on the growth of start-up firms by extensively investigating the ongoing empirical studies in this research stream. Moreover, this study identifies drivers whose support roles are confirmed in the literature and recommends further research opportunities.

In this study, we analysed the results of 316 empirical studies on start-up firms and growth and also identified and categorised 66 growth drivers. We presented these drivers in three-dimensional charts: 1) the frequency of using each driver in the 316 studies, 2) the consistency of each driver as measured by the number of studies supporting its statistical significance and 3) the net effect (positive or negative) of each driver on growth.

Our analysis compares extant studies on growth drivers and shows some under-explored growth factors of start-up firms.

Both start-up managers and policymakers can benefit from this study. This study provided managers with a fine-grained tool on the main growth drivers and can guide policymakers in supporting policies for start-up firms.

This study provides a rich, fine-grained and coherent picture of several potential growth drivers of start-up firms. Moreover, we extended our analysis to various potential drivers more than previous studies on this topic, thereby providing fruitful insights into the critical growth factors for start-up firms.

Why are some lean workfloor teams able to improve their already high performance, over time, and others not? By studying teams' and leaders' behaviour-value patterns, this abductive field study uncovers a dynamic capability at the team level.

Various methods were employed over three consecutive years to thoroughly examine five initially high-performing lean workfloor teams, including their leaders. These methods encompassed micro-behavioural coding of 59 h of film footage, surveys, individual and group interviews, participant observation and archival data, involving objective and perceptual team-performance indicators. Two of the five teams continued to improve and perform highly.

Continuously improving high lean team performance is found to be associated with (1) team behaviours such as frequent performance monitoring, information sharing, peer support and process improvement; (2) team leaders who balance, over time, task- and relations-oriented behaviours; (3) higher-level leaders who keep offering the team face-to-face support, strategic clarity and tangible resources; (4) these three actors' endorsement of self-transcendence and openness-to-change work values and alignment, over time, with their behaviours; and (5) coactive vicarious learning-by-doing as a “stable collective activity pattern” among team, team leader, and higher-level leadership.

Since lean has been undertheorised, the authors invoked insights from organisational behaviour and management theories, in combination with various fine- and coarse-grained data, over time. The authors uncovered actors' behaviour-value patterns and a collective learning-by-doing pattern that may explain continuous lean team performance improvement. Four theory-enriching propositions were developed and visualised in a refined model which may already benefit lean practitioners.

This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis.

This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review.

This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward.

While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research.

Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas.

Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society.

The purpose of this article is to discuss the role of operations management in society. The article detects trends, raises critical questions to operations management research and articulates a research agenda to increase the value of such research in addressing societal problems.

This paper evaluates the papers presented at the EurOMA 2019 conference to detect trends and discuss the contributions of operations management research to society. It further goes to identify gaps in the research agenda.

The article finds several important streams of research in operations management: sustainable operations and supply chains, health care and humanitarian operations, innovation, digitalisation and 4.0, risk and resilience. It highlights new trends such as circular economy research and problematises when to stop implementing innovation and how to address and report their potential failure. Importantly, it shows how it is not just a question of offshoring vs reshoring but of constant change in manufacturing that operations management addresses.

The article highlights not just novel research areas but also gaps in the research agenda where operations management seeks to add value to society.

The purpose of this paper is to asses the welfare and macroeconomic implications of three distinct degrowth strategies designed to reduce carbon emissions: penalizing fossil fuel demand, substituting aggregate consumption with leisure and disincentivizing total factor productivity (TFP) growth.

Using an environmental dynamic general equilibrium (eDGE) model that incorporates both green renewable technologies and fossil fuels in the production process, this study sets an emissions reduction target aligned with the goals of the Paris Agreement by 2050.

The results reveal that the conventional degrowth strategy, wherein a reduction in the consumption of goods and services is compensated with an increase in leisure, may entail significant economic consequences, leading to a notable decline in welfare. In particular, a degrowth scenario resulting from a decline in TFP yields the most pronounced reduction in welfare. Conversely, inducing a reduction in fossil fuel demand by fiscally inflating the price of the imported commodity, despite potential social backlash, exhibits noticeably less detrimental welfare effects compared to other degrowth policies. Furthermore, under this degrowth strategy, the findings suggest that a globally coordinated strategy could result in long-term welfare gain.

To the best of the authors’ knowledge, this is the first contribution that uses an eDGE model to evaluate the welfare implications of an additional degrowth strategy amidst the ongoing inertial reduction of carbon emissions.