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The aim of this study is to investigate subject independent emotion recognition capabilities of EEG and peripheral physiological signals namely: electroocoulogram (EOG), electromyography (EMG), electrodermal activity (EDA), temperature, plethysmograph and respiration. The experiments are conducted on both modalities independently and in combination. This study arranges the physiological signals in order based on the prediction accuracy obtained on test data using time and frequency domain features.

DEAP dataset is used in this experiment. Time and frequency domain features of EEG and physiological signals are extracted, followed by correlation-based feature selection. Classifiers namely – Naïve Bayes, logistic regression, linear discriminant analysis, quadratic discriminant analysis, logit boost and stacking are trained on the selected features. Based on the performance of the classifiers on the test set, the best modality for each dimension of emotion is identified.

 The experimental results with EEG as one modality and all physiological signals as another modality indicate that EEG signals are better at arousal prediction compared to physiological signals by 7.18%, while physiological signals are better at valence prediction compared to EEG signals by 3.51%. The valence prediction accuracy of EOG is superior to zygomaticus electromyography (zEMG) and EDA by 1.75% at the cost of higher number of electrodes. This paper concludes that valence can be measured from the eyes (EOG) while arousal can be measured from the changes in blood volume (plethysmograph). The sorted order of physiological signals based on arousal prediction accuracy is plethysmograph, EOG (hEOG + vEOG), vEOG, hEOG, zEMG, tEMG, temperature, EMG (tEMG + zEMG), respiration, EDA, while based on valence prediction accuracy the sorted order is EOG (hEOG + vEOG), EDA, zEMG, hEOG, respiration, tEMG, vEOG, EMG (tEMG + zEMG), temperature and plethysmograph.

Many of the emotion recognition studies in literature are subject dependent and the limited subject independent emotion recognition studies in the literature report an average of leave one subject out (LOSO) validation result as accuracy. The work reported in this paper sets the baseline for subject independent emotion recognition using DEAP dataset by clearly specifying the subjects used in training and test set. In addition, this work specifies the cut-off score used to classify the scale as low or high in arousal and valence dimensions. Generally, statistical features are used for emotion recognition using physiological signals as a modality, whereas in this work, time and frequency domain features of physiological signals and EEG are used. This paper concludes that valence can be identified from EOG while arousal can be predicted from plethysmograph.

Digital inequality is considered one of the leading causes of socioeconomic disparities nowadays and a barrier to sustainable development. However, a dearth of empirical research has examined the impact of digital inequality in attaining sustainable development. This study aims to systematically review the scientific publications on the impact of digital inequality in achieving sustainable development.

The preferred reporting items for systematic reviews and meta-analyses (PRISMA, 2020) guidelines were followed to carry out the systematic literature review (SLR) using Scopus, Web of Science, ProQuest and Google Scholar electronic databases. Numerous inclusion/exclusion criteria were employed to obtain the most relevant literature. Finally, 54 articles were included to prepare the final database and qualitative synthesis was performed using 12 variables.

While the findings show that there has been a substantial expansion of scientific publications on the focused area in recent years, there is still a lack of empirical and comparative studies; less focus on the offline benefits of online activities were also demonstrated by the results. Moreover, SDGs 04 and 05 were identified as the predominant goals in the literature. Findings further highlighted the importance of an accurate conceptualization of digital inequality.

In general, this study investigates the level of impact of digital inequality on the United Nations' Sustainable Development Goals. Moreover, it shows the evolution of scientific publications on digital inequality in terms of its contribution when achieving sustainable development.

Although politicians' use of performance information affects political decisions and, through them, the well-being of society, there is a lack of studies exploring what contextual factors are associated with annual active performance information use among politicians. Furthermore, past studies on this subject have been cross-sectional rather than longitudinal.

In this qualitative case study, triangulation of observations and 10 semi-structured interviews were used to ensure the robustness of findings. The study was conducted in a Finnish municipality known as Kangasala.

A dialogue culture, constructive political climate, trusted information sources and high-quality information attained via accessible information channels explained the high information use in primarily unfavorable conditions to such use. The authors’ findings contradict many prior interview and survey studies that did not recognize the simultaneous contributions of the information provider, channel and quality, along with organizational and environmental factors to high performance information use. The results contradict to some extent the findings from other countries as these studies have explained high levels of use with unique combinations of drivers, whereas we identify common attributes of these combinations and talk about their meaning in the success of Kangasala's public financial management. However, the findings of this case study cannot be generalized.

This study describes a case organization that created a supportive environment for politicians' frequent performance information use that contributed to improvements. Past studies provide little knowledge about establishing sustained high levels of information use among politicians, so the case offers ideas and inspiration for improving this use.

Increasing amount of empirical research in operations and supply chain management is using institutional theory as its theoretical lens. Yet, a common scale to measure the three institutional pressures – coercive, mimetic and normative – is lacking. Many studies use proxies or a single, grouped, construct of external pressures which present methodological challenges. This study aims to present the development of multi-item scales to measure institutional pressures (in a purchasing context).

First, items were generated based on the theoretical construct definitions. These items were then tested through academic sorting and an international survey. The first empirical testing failed to produce reliable and valid scales, and further refinement and analysis revealed that coercive pressure splits into two separate constructs. A second q-sorting was then conducted with purchasing practitioners, followed by another survey in Italy to verify the new measurement scale for four institutional pressures.

The multimethod and multistage measurement development reveals that empirically the three institutional pressures actually turn into four pressures. The theoretical construct of coercive pressure splits into two distinct constructs: coercive market pressure and coercive regulatory pressure.

The results of the paper, namely, the measurement scales, are an important theoretical and methodological contribution to future empirical research. They present a much-needed measurement for these theoretical constructs increasingly used in management research.

This study aims to co-develop a Frailty, Health and Care Needs Assessment (FHCNA) questionnaire for people experiencing homelessness and explore the feasibility of its use by non-clinical staff in homeless hostels.

The FHCNA, aimed at identifying frailty and other health and care priorities for people experiencing homelessness, was co-designed in workshops (online and in person) with homelessness and inclusion health staff. Its feasibility was tested by staff and their clients in two hostels, with pre- and post-study focus groups held with hostel staff to gain input and feedback.

The FHCNA was co-developed and then used to collect 74 pairs of resident and key worker inputted data (62% of eligible hostel residents). The mean age of clients was 48 years (range 22–82 years). High levels of unmet need were identified. Over half (53%) were identified as frail. Common concerns included difficulty walking (46%), frequent falls (43%), chronic pain (36%), mental health issues (57%) and dental concerns (50%). In total, 59% of clients reported difficulty in performing at least one basic activity of daily living, while only 14% had undergone a Care Act Assessment. Hostel staff found using the FHCNA to be feasible, acceptable and potentially useful in facilitating explorations of met and unmet health and social care needs of hostel clients. By identifying unmet needs, the FHCNA has the potential to support staff to advocate for access to health and social care support.

To the best of the authors’ knowledge, this is the first study to co-develop and feasibility test a questionnaire for use by non-clinically trained staff to identify frailty and other health and care needs of people experiencing homelessness in a hostel setting.

Using a mobile phone is increasingly becoming recognized as very dangerous while driving. With a smartphone, users feel connected and have access to information. The inability to access smartphone has become a phobia, causing anxiety and fear. The present study’s aims are as follows: first, quantify the association between nomophobia and road safety among motorists; second, determine a cut-off value for nomophobia that would identify poor road safety so that interventions can be designed accordingly.

Participants were surveyed online for nomophobia symptoms and a recent history of traffic contraventions. Nomophobia was measured using the nomophobia questionnaire (NMP-Q).

A total of 1731 participants responded to the survey; the mean age was 33 ± 12, and 43% were male. Overall, 483 (28%) [26–30%] participants received a recent traffic contravention. Participants with severe nomophobia showed a statistically significant increased risk for poor road safety odds ratios and a corresponding 95% CI of 4.64 [3.35-6.38] and 4.54 [3.28-6.29] in crude and adjusted models, respectively. Receiver operator characteristic (ROC)-based analyses revealed that NMP-Q scores of = 90 would be effective for identifying at risk drivers with sensitivity, specificity and accuracy of 61%, 75% and 72%, respectively.

Nomophobia symptoms are quite common among adults. Severe nomophobia is associated with poor road safety among motorists. Developing screening and intervention programs aimed at reducing nomophobia may improve road safety among motorists.

Financial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction.

This study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity.

This study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics.

The major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency.

Finally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020).

This study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.

Massive Open Online Courses (MOOCs) are a form of distance education courses. They have been celebrated as revolutionizing the way learners access education and the way colleges and universities could expand education on a global scale beyond their traditional campuses. The purpose of this study is to identify the pedagogical strategies used for instruction and assessment in leadership-oriented MOOCs and gain a more refined understanding of the current state of MOOCs in leadership education. This study examines 96 leadership MOOCs across the platforms of Coursera, EdX, FutureLearn, Canvas.net, and Standford Online through a content analysis research framework. The study concludes with a discussion of leadership MOOC pedagogy and presents the current state of MOOCS among leadership education and professional development.

This study aims to empirically investigate how an industrial region has reacted to different shocks – competitive, market and environmental – through its economic subsystems – its firms, workers and institutions – in a longitudinal perspective for the period 1985–2021.

The authors used a mixed-method approach applied to a case study of the Brazilian T&C industrial region. The authors used two data sources and two stages for data collection. The first stage involved documentary research and the second in-depth interviews. The analysis of qualitative data took place in two stages. In the first, the authors applied content analysis, and in the second stage, the authors used the exploratory statistical technique of simple correspondence analysis and the categorical data.

The results provide evidence that different types of shock provoke different reactions. However, the shock–reaction relationship is invariable over time. The authors observed proportionality in the size of the shock and the regional actors involved in the regional response – firms, workers and institutions.

The authors went a step further, presenting empirical research on the shock–reaction relationship using the “type of shock” as a variable. This paper provides a holistic understanding of the factors behind regional resilience through insights into the role that resources, structures, institutions and actors play in the regional response to distinct types of shocks, reaching four main conclusions.

The Accounting Information Systems (AIS) research field emerged around 30 years ago as a subfield of accounting but is at risk to develop further as an isolated discipline. However, given the importance of digitalization and its relevance for accounting, an amalgamation of the parent research field of accounting and the subfield of accounting information systems is pivotal for continuing relevant research that is of high quality. This study empirically investigates the distance between AIS research that is included in accounting literature and AIS research that prevails in dedicated AIS research outlets.

To understand which topics define AIS research, all articles published in the two leading AIS journals since 2000 were analyzed. Based on this topical inventory, all AIS studies that were published in the top 16 accounting journals, also since 2000, are identified and categorized in terms of topic, subtopic and research methodology. Next, AIS studies published in the general accounting field and AIS studies published in the AIS field were compared in terms of topics and research methodology to gain insights into the distance between the two fields.

The coverage of AIS topics in accounting journals is, to no small extent, concentrated around the topics “information disclosure”, “network technologies” and “audit and control”. Other AIS topics remain underrepresented. A possible explanation might be the focus on archival studies in accounting outlets, but other elements might play a role. The findings suggest that there is only a partial overlap between the parent accounting research field and the AIS subfield, in terms of both topic and research methodology diversity. These findings suggest a considerable distance between both fields, which might hold detrimental consequences in the long run, if no corrective actions are taken.

This is the first in-depth investigation of the distance between the AIS research field and its parent field of accounting. This study helped develop an AIS classification scheme, which can be used in other research endeavors. This study creates awareness of the divergence between the general accounting research field and the AIS subfield. Given the latter's relevance to the accounting profession, isolation or deterioration of the AIS research must be avoided. Some actionable suggestions are provided in the paper.