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This research leverages an integrated framework that uses the technology acceptance model (TAM) and the theory of planned behaviour (TPB) to analyse the main drivers of users' intention to use food delivery apps. The purpose of this paper is to investigate the consumer's willingness to adopt online food delivery (OFD) using the models' constructs and extend them to consider food choices, convenience, trust and the effect of the perceived risks related to the coronavirus disease 2019 (COVID-19) pandemic as contextual factors.

The study adopts the partial least squares approach to structural equation modelling (PLS-SEM) to examine the data. The final sample consists of 425 people in Italy.

The authors have found that combining the TAM and the TPB provides a valid and significant model that can be used to understand OFD users' behavioural intentions. Moreover, the results show that subjective norms have a stronger effect on behavioural intentions than the personal attitude and that trustworthiness and the perception of risks related to COVID-19 have different effects. Accordingly, the authors derive several theoretical and managerial implications from these results.

This research contributes to the current debate on consumer behaviour in the OFD context. Only a few studies have integrated the TAM and TPB models in this context. This paper sheds light on the factors useful in predicting people's choice to buy food via OFD. Furthermore, it highlights the key role of some contextual factors and subjective norms over more technical ones.

Additive manufacturing (AM) technologies, also known as three-dimensional printing (3DP), is a technological breakthrough that have the potential to disrupt the traditional operations of supply chains. They open the way to a supply chains innovation that can significantly benefit hospitals and health-related organizations in dealing with crises or unexpected events in a faster and more flexible way. In this study the authors identify the boundary of this potential support.

The authors adopt a case study approach to understand the dynamics behind a well-known best practice to identify the main opportunities and the main pitfalls that AM may pose to health-related organizations wanting to leverage them.

The case highlights that it is possible to increase hospital flexibility using AM and that by leveraging the Internet it is possible to spread the benefits faster than what it would be normally possible using traditional supply chain processes. At the same time the case highlights that leveraging these technologies needs buy-in from all the relevant stakeholders.

The paper is one of the first, to the best of the authors' knowledge, to highlight the main opportunities and difficulties of implementing 3DP technologies in hospital supply chain management.

The purpose of this paper is to supply indications that may be useful in the process of development of new products that fully exploit the value potential of Internet of Things (IoT) technologies in the automotive industry. To this aim, the authors investigate how applications of the IoT to smart vehicles are perceived by consumers and describe different ways to increase their satisfaction.

After a literature review focused on IoT and consumer behaviour in the automotive industry, the authors apply the Kano model to find the drivers for achieving customer satisfaction with new product developments in smartcars.

Automotive companies need to consider what is attractive to drivers and what consumers consider to be “driver-friendly”. Using an empirical analysis, the authors highlight the motivations for developing a smartcar to fits the expectation of Italian drivers.

While the global framework given by this paper can be useful in all contexts, the empirical part is based on the Italian consumer. Further research may extend the application of the model to other countries to improve the generalizability of the results.

This study supplies an analysis of qualitative data that may prove useful to researchers and managers in planning their strategic and operative activities with the aim of improving the development of IoT-related products in the automotive industry.

This paper fulfils the need for an original research framework that sheds light on relevant insights and gives useful hints on the development of IoT technologies in the automotive sector.

Starting from the state-of-the-art of Fintech development, this study aims to propose some research propositions comparing reward-crowdfunding (RCF) and equity-crowdfunding (ECF). In this sense, the present research provides a comprehensive analysis of fintech development and – to conceptualize the comparison between RCF and ECF – it focuses on campaigns’ characteristics, aims and post-campaigns scenarios.

All the research propositions related to the comparison between RCF and ECF are rooted in dedicated literature. The methodological approach adopted in the present paper can be referred to theorizing.

This study suggests that five key elements characterize the development of fintech: regulation, infrastructure, technologies, finance and innovations. The research provides nine propositions: four related to the campaigns’ characteristics; two related to the use of crowdfunding models by entrepreneurs; and three related to the performance of crowdfunded companies.

By offering nine research propositions, this study is expected to foster and support the investigation of fintech development from an entrepreneurial and managerial point of view.

To the best of authors’ knowledge, this study is among the first to explore the fintech development and to propose a comparative approach between RCF and ECF. This research contributes to the current debate on fintech development as well as on the comparison between crowdfunding models.

This study aims to determine the prevalence and predictors of postpartum depression (PPD) among nursing mothers in Nsukka, Nigeria.

This study was a descriptive cross-sectional survey among nursing mothers in three hospitals in Nsukka, Nigeria. Data was collected using a self-administered Edinburgh Postpartum Depression Scale (EPDS) and sociodemographic form. The data obtained were analyzed using descriptive statistics, chi-square test and binary logistic regression.

A total of 270 nursing mothers participated in this survey, giving a response rate of 94.4%. The prevalence of PPD among the study group was 20.0%. However, women who did not have complications during childbirth were about two times (AOR = 0.417, 95% CI = 0.204, 0.852, P = 0.016) less likely to develop symptoms of PPD than women who experienced birth complications. In addition, women who had poor relationships with their partners have approximately seven times (AOR = 6.994, 95% CI = 1.110, 44.059, P = 0.038) higher odds of developing PPD compared with those women who had excellent relationships with their partners.

The sample size was small, hence, might limit the generalizability of its findings beyond the study group. Health-care practitioners should provide appropriate interventions to women at a higher risk of developing PPD on the need to maintain a healthy and supportive relationship with their partners.

This study provides unique insight into PPD among nursing mothers and its determinants from a different regional, socioeconomic, societal expectations, social support system, access to health care and cultural context.

Recent developments in the psychometric assessment of youth psychopathic traits suggest that the inclusion of a conduct disorder (CD) factor to the traditional three factors of the psychopathy construct may improve the incremental validity of these measures. The purpose of the current study is to examine whether the addition of a CD factor incrementally improves the ability of the Youth Psychopathic Traits Inventory Short version (YPI-S) to predict criminal recidivism.

A longitudinal quantitative research design was used with a sample detained male youth (N = 214, M_age = 16.4 years, SD_age = 1.3 years).

Results using the area under the curve analysis suggest that the inclusion of a CD factor slightly improves the capacity of the YPI-S to predict one-year general criminal recidivism, but it does not significantly increase its capacity to predict violent criminal recidivism. Results also indicate that a CD scale outperforms the YPI-S, even with an additional CD factor included, in terms of predicting one-year general and violent recidivism.

Self-reported youth psychopathic trait measures, even those that include a CD factor as a fourth factor, should be used with caution when the aim is to predict youth criminal recidivism.

To the best of the authors’ knowledge, this is the first study using a self-reported youth psychopathic traits measure with a CD factor to examine youth criminal recidivism.

This study aims to examine how gender variation in trans identities shape exposure to bias and discrimination. The authors then examine how trans identities intersect with race/ethnicity, education and social class to shape exposure risk to bias, discrimination and harassment in the workplace.

The authors use data from the 2015 U.S. Transgender Survey with 24,391 trans-identified respondents. To account for the nested nature of trans people in state contexts, the authors use two-level logistic multilevel models. The authors are guided by Puwar’s bodies out of place as the theoretical grounding for this study.

The authors find significant differences in how trans women and men experience discrimination. The authors also find differences in race, education and social class. Finally, the presence of anti-discrimination policies presents mixed results.

The authors’ analysis reveals important differences in trans workers’ exposure to discrimination based on gender identity, social class, race/ethnicity and policy context, and draws upon a rich and large data set.

The purpose of this paper is to provide a case study about the capacity utilization analysis in a small-sized manufacturing company through the application of time-driven activity-based costing (TDABC). After a brief overview of development of the TDABC system, a detailed application of TDABC and capacity utilization analysis in a bakery is given.

This paper is based on a case study about the application of TDABC in a small-sized Greek manufacturing firm. In the case study, time equations were developed for the supporting, operating and manufacturing departments and product costs determined based on the model. Capacity utilization analysis made through the application of TDABC system.

The study shows that TDABC is more applicable in small-sized manufacturing companies because of their labor-intensive nature. In contrast to previous studies, authors argue that even in small firms simple excel sheets are not enough to capture the complexity of the time equations and business intelligence software and programming coding is required.

Although the fundamental structure of TDABC is the same for all companies there is no strict form of application.

The practical implication of this paper is that each firm has unique characteristics that need to be reflected in the application of the TDABC model.

This paper contributes by providing insights into cost accounting in SMEs. More specifically, this paper contributes to the TDABC literature regarding the application of the system in small and medium sized manufacturing firms.

The paper seeks to propose a specific approach based on Dynamic Analysis and Chaos Theory aiming to emphasize the differences into the eddy current signals obtained by related non‐destructive tests, when the inspected specimens have flaws with different shapes.

Non‐linear eddy current analysis is very useful for flaw detection in many in‐service inspections. State‐of‐the‐art technologies allow one to define position and depth of defects, but the shape identification is still an open problem. In this paper, experimental data have been subjected to a dynamical analysis in order to relate the trend of eddy current signals to the shape of analyzed defect.

In particular, a dynamical reconstruction by means of recurrence plots (RPs) has been carried out in order to detect analogies and differentiations between different eddy current signals. Moreover, cross‐correlation between RPs of a reference benchmark and testing eddy current signals has been applied in order to emphasize a different dynamical behaviour and to detect a particular flaw's shape. In this way, a real‐time algorithm for defect shape classification has been performed.

Proposed approach is very interesting, and it is an innovation in non‐destructive testing procedures. In fact, the shape identification of a flaw is still an open challenge. The proposed approach, based on dynamic analysis, gives the key to solve this particular ill‐posed problem, by introducing a relation between the eddy current measurements and the shape of defect existing in the inspected specimen. Very interesting preliminary results have been obtained.