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The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.

This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).

The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.

This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.

This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.

The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making.

A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed.

Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics.

The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties.

The proposed model has the potential to avoid the mistake from a misleading data imputation.

The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations.

This is the first time that the whole data analytics is considered from the point of view of grey systems.

Digitalization accelerates the need of tourism and hospitality ecosystems to reframe business models in line with a data-driven orientation that can foster value creation and innovation. Since the question of data-driven business models (DDBMs) in hospitality remains underexplored, this paper aims at (1) revealing the key dimensions of the data-driven redefinition of business models in smart hospitality ecosystems and (2) conceptualizing the key drivers underlying the emergence of innovation in these ecosystems.

The empirical research is based on semi-structured interviews collected from a sample of hospitality managers, employed in three different accommodation services, i.e. hotels, bed and breakfast (B&Bs) and guesthouses, to explore data-driven strategies and practices employed on site.

The findings allow to devise a conceptual framework that classifies the enabling dimensions of DDBMs in smart hospitality ecosystems. Here, the centrality of strategy conducive to the development of data-driven innovation is stressed.

The study thus developed a conceptual framework that will serve as a tool to examine the impact of digitalization in other service industries. This study will also be useful for small and medium-sized enterprises (SMEs) managers, who seek to understand the possibilities data-driven management strategies offer in view of stimulating innovation in the managers' companies.

The paper reinterprets value creation practices in business models through the lens of data-driven approaches. In this way, this paper offers a new (conceptual and empirical) perspective to investigate how the hospitality sector at large can use the massive amounts of data available to foster innovation in the sector.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

As many businesses faced economic disruption due to the Covid-19 pandemic and sought financial relief, existing bank relationships became critical to getting a loan. This study examines factors associated with the development of personal relationships of rural small businesses with community bank representatives.

We applied a mixed-method approach. We employed descriptive statistics, principal factor analysis and logistic regression for data analysis. We distributed an online survey to rural small businesses in five states in the United States. Key informant interviews with community bank representatives supplemented the survey results.

A business owner’s trust in a banker was positively associated with the establishment of a business–bank relationship. However, an analysis of individual trust’s components revealed that the nature of trust is complex, and a failure of one or more components may lead to decreased trustworthiness in a banker. Small businesses that preferred personal communication with a bank were more inclined to relationship banking.

Due to the relatively small sample size and cross-sectional data, our results may not be conclusive but should be viewed as preliminary and as suggestions for future research. Bankers should be aware of the importance of trust for small business owners and of the actions that lead to increased trustworthiness.

The study extends the existing knowledge on the business–bank relationship by focusing mainly on social (instead of economic) factors associated with the establishment of the business–bank relationship in times of crisis and high uncertainty.

This study attempts to examine the missing middle (MM) phenomena in the context of the Indian manufacturing sector using the unit level information from the database of Ministry of Corporate Affair, Government of India.

Unlike the previous studies, the present study first bifurcated the missing enterprises into two categories such as “permanently” dropped and “reappeared,” in order to pursue a meaningful analysis and derive conclusions with policy insights. Various financial indicators were used to explain the causes of MM phenomena during 2009–2010 and 2016–2017, in a logistic framework.

The study found that profit margin ratio is higher for the group of medium sized enterprises which continued in comparison to the units which dropped out permanently. Similar is the case with the ratio of investment turnover. The econometric results, however suggest that the relationship between the chances of a firm being dropped out and financial indicators is weak as the coefficients of various financial indicators are found to be statistically significant only for a few years.

The study suggests that the missing middle phenomenon is not a myth in India as very large number of medium-sized firms have been disappearing from the market over the years. Based on firm level data it identifies the factors which resulted in such a phenomenon.

At present, there is no reference to Attention Autism (AA) as a framework and therapeutic tool with autistic children in occupational therapy (OT) literature. By way of introducing AA as a potential intervention to the OT community, this study aims to investigate the extent to which participation in a two-day AA training could contribute to increasing confidence and inspire changes in practice for Irish occupational therapists (OTs) supporting autistic children.

A pilot study design with mixed qualitative and quantitative methods was used to evaluate the impact of a two-day AA training on six OTs. The OTs support autistic children throughout Ireland across public, private and voluntary sectors. They completed brief, non-standardised questionnaires 2 weeks before the training (Time 1) and again 12 weeks post (Time 2) training session. At Time 2, additional exploratory questions were answered by OTs regarding their use of AA in practice.

This explorative study’s quantitative findings presented percentage change increases within three areas of confidence for all OTs. These include establishing attention, motivating and developing functional skill goals with autistic children. One of the participants did not score any change in confidence in a fourth area, building rapport, however, the five other participants scored percentage change increases. Qualitative data provided by participants showed that they were implementing AA in practice since attending the training. Five of the participants reported positive experiences of using AA and one participant reported the programme was not suitable for her caseload because of their level of understanding and need.

This was a small, exploratory, practice-based study. As this is the first study exploring this area of practice for OTs, to the best of the authors’ knowledge, there were no standardised methods of assessment available, therefore a self-designed survey was used by the author which had a limited number of open-ended questions and four Likert scale questions. This study was also limited in that there was one main researcher who also delivered the two-day AA training. The sample data set was small which resulted in the limitation of the choice of methods used to analyse the quantitative data. Percentage changes were used as the only available and reliable method for a small data set.

Findings of this study, despite their preliminary nature, indicate that AA training may be a useful professional development consideration for OTs who provide a service for autistic children. Further AA research in OT is required including larger and more rigorous studies. An alternative training option of The Curiosity Programme may be considered for OTs supporting children who may not yet be ready to participate in AA.

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.

The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues.

Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper.

This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

In this study, the authors explore how sensing and seizing of market opportunities, asset reconfiguration and top management team (TMT) consensus on these elements jointly relate to a firm's international expansion. By doing this, the authors contribute to the existing literature by addressing dynamic managerial capabilities at the TMT level instead of considering them as individual executives' traits. The authors use the qualitative comparative analysis (QCA) method to analyze our data from 261 TMT executives in 63 firms. The findings indicate that sensing, seizing and reconfiguration capabilities are highly relevant for internationalization but in different configurations for specific stages and elements of international business. Presence of sensing as a part of configurations is observable, especially in connection to a firm having foreign customers and explicit internationalization strategies, while configurations where seizing and reconfiguration emerge are connected to firms showing continuity in the international markets. The authors’ results also indicate that a lack of TMT consensus in connection to dynamic managerial capabilities is a driving force that allows the firm not to stagnate with regards to internationalization. Yet, lack of TMT consensus combined with low reconfiguration capabilities seems to generate negative results, which suggests that different views are not helpful if the firm is incapable of changing its approaches.

This study uses data gathered with a questionnaire where the executives select either “yes” or “no” in response to statements describing the firm situation with regard different managerial aspects and progress of international growth. The authors analyze these data from 261 TMT executives from 63 firms using the QCA method.

The findings indicate that sensing, seizing and reconfiguration capabilities are highly relevant for internationalization but to different extents for specific elements of international business; generally, while sensing is needed, in particular, for having foreign customers and internationalization strategies in the first place, seizing and reconfiguration became relevant for continuity in the international markets. Consensus or rather lack of it on these elements also plays a role. It seems that some disagreement is a driving force that allows the firm not to stagnate with regards to internationalization. However, TMT disagreement combined with low reconfiguration capabilities seems to generate negative results, which suggests that different views are not helpful if the firm is incapable of changing its approaches.

The findings contribute to existing knowledge by exploring how managerial capabilities influence firm-level dynamic capabilities from the point of view of the TMT. The authors also add to existing research that has often focused on the relationships between TMT executives' demographic traits and TMT consensus and, further, the (subsequent) firm performance by looking at different configuration rather than linear linkages. Together, these notions further mean that the authors change the point of view on diversity. The authors consider the consensus on existing managerial dynamic capabilities rather than evaluate the functional diversity or the TMT executives' agreement on strategic moves.

All capabilities are important. TMT does not need to agree on everything, as long as they acknowledge where their problem areas are, and they can capture at least some of the relevant trends and opportunities. In fact, having some lack of consensus seems to be a driving force that allows capabilities to be questioned and potentially keeps (false) under-appreciation of existing capabilities from becoming a barrier to international expansion.

Unlike previous studies that have focused on the relationship between the TMT executives' demographic characteristics and firm performance or the relationship of the demographics and TMT strategic consensus at a general level – or studies that have explained international performance with TMT consensus (or with dynamic managerial capabilities), this study brings forth how the dynamic managerial capabilities and the TMT executives' strategic consensus with regard to these capabilities influence the firm's international expansion. Here, the authors consider internationalization widely, looking at whether the firm has foreign customers or international expansion strategy in place, and whether there this activity is sustained and continuous (with repeated trading and long-term international contracts, in particular). To our knowledge, there is no research on TMT strategic consensus that explains how the unanimity among executives on dynamic managerial capabilities connects to the firm's international expansion.

To better understand the reality of living with the diseases and conditions that its drugs and therapies are developed to treat, the Novartis leadership determined a need for more meaningful insights into patients’ lives. They sought to develop a systematic, creative methodology – informed by the psychology of insightful rather than analytical thinking – to properly integrate and deploy the research commissioned into its day-to-day business decision-making. For it is well established that better understanding of the patient reality drives both compliance and adherence “beyond the pill”. The purpose of this paper is to bring the novel methodology of creativity to a wider audience and ensure that many others – notably in patient advocacy organizations – can benefit from this approach.

A core team of Insight and Analytics and Patient Engagement leads from various therapeutic area teams worked in partnership with a psychologist and practitioner in the field of insightful thinking, to develop an effective methodology that could reliably surface and articulate genuine patient insights. This methodology – the i4i Insights Discovery™ process – was developed, piloted, refined and codified in 2020 and implemented across the company in 2021–2022. It uses a combination of convergent and divergent thinking techniques – human rather than artificial intelligence, combining diverse research outputs – to understand patients’ lives better. With enhanced understanding, the insights then shape educational and behavioral strategies to drive adherence and compliance.

At a time of tightening budgets and demands to deliver enhanced impact from research budgets, i4i Insights Discovery™ has enabled Novartis teams to turn existing research outputs into profound and useful understandings of what it means to live with specific diseases and develop evidence-based patient engagement strategies; insight-driven decision-making around the lifecycle of any compound. i4i Insights Discovery™ has been applied across Novartis’s diverse areas of expertise, from heart disease to cancer, from organ transplantation to dermatology, from food allergy to ophthalmology.

The i4i Insights Discovery™ process enables Novartis teams to gain deeper understanding of patients’ lives without the need to commission additional research; to do more with less. These insights enable cross-functional Novartis teams to develop better-informed strategies that better address the needs of patients and their care partners, of health-care professionals and health-care systems. The team creating the process is looking to make the i4i Insights Discovery™ approach a gold standard of insight discovery, both for pharma and health care and in other categories, too.

The i4i Insights Discovery™ process is a practical, novel application of well-established principles in the psychology of insightful thinking to address a clear business imperative. By repurposing and reinterpreting existing research outputs using creative verbal and visual exercises, it delivers a more human and empathetic understanding of the patient reality. It moves teams from “So what?” – this is what the data mean – to “Now what?” – this is what we should do as a result.