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This qualitative study aims to examine bankers’ perspectives regarding financial inclusion, the challenges it faces and the scope for improvement. This research proposes a financial inclusion model, considering the inputs received by bankers. Financial exclusion of different sections is an issue common to emerging countries.

Data for qualitative research were collected through interviews with bank officials. The information was gathered from 32 bankers from India’s several zones (North, South, West and East). The data were collected from bankers from different public and private sector banks. Thematic analysis was performed up to the point of saturation to study the response received from bankers.

Bank-related issues such as frequent computer problems, network connectivity problems, costs, a shortage of bank branches, fewer transactions through automated teller machines and a shortage of banking staff affect customers’ confidence in formal banking. Banking services are disrupted by a lack of trust in banking correspondents (BCs), as they are not regular employees of banks. Limits on daily transactions discourage high-value customers from using BCs and kiosks. The time spent on administrative formalities impacts customers. Financial inclusion is affected by availability, accessibility, usage and affordability. Digital financial literacy is essential for ease of transaction, but awareness about financial products helps protect customers from cyber scams. The findings of this research would benefit financial institutions globally in developing their businesses and helping to achieve financial inclusion and the United Nation’s sustainable development goals (SDGs).

This research paper undertakes a qualitative analysis of the views collected from bankers. Bankers are crucial stakeholders in the successful implementation of the National Financial Inclusion Policy of the Government of India. Bankers’ perspectives will be important not only for India and its researchers but also in the global context, as the UN’s SDGs focus on leaving no one behind.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

This study aims to analyze the effects of e-government on corruption prevalence by using a sample of 29 European countries over the period 2012–2019.

This paper uses the panel corrected standard errors (PCSE) model to mitigate the problems of cross-sectional dependence. The PCSE model is also considered to reexamine the findings when the presence of heteroscedasticity, fixed effects and endogeneity issues are taken into account. The theoretical model incorporates one-year-lagged explanatory variables to deal with endogeneity. The autoregressive distributed lag method using the dynamic fixed effects estimator is chosen to deal with the time and country-fixed effects in the effort to measure the short- and long-run effects of e-government more precisely.

The results indicate that e-government plays a critical role in improving the population’s perception of corruption. Furthermore, e-government appears to have an effect in the short run. Notably, the estimation results show that there is a nonlinear relationship between e-government, especially user centricity and key enablers and the corruption perception index in the U-shaped curve.

The short-run and nonlinear effects of e-government on corruption prevalence suggest that the fight against corruption requires countries to pursue a consistent and continuous improvement and development of the e-government system.

The authors contribute to the literature by providing a consistent and precise answer to this relationship in the case of European countries. Another contribution of the work is to use diverse indicators to reflect e-government in a typical country, which helps us confirm the reliability and robustness of the findings.

This paper reviews recent research on the expected economic effects of developing artificial intelligence (AI) through a survey of the latest publications, in particular papers and reports issued by academics, consulting companies and think tanks.

Our paper represents a point of view on AI and its impact on the global economy. It represents a descriptive analysis of the AI phenomenon.

AI represents a driver of productivity and economic growth. It can increase efficiency and significantly improve the decision-making process by analyzing large amounts of data, yet at the same time it creates equally serious risks of job market polarization, rising inequality, structural unemployment and the emergence of new undesirable industrial structures.

This paper presents itself as a building block for further research by introducing the two main factors in the production function (Cobb-Douglas): labor and capital. Indeed, Zeira (1998) and Aghion, Jones and Jones (2017) suggested that AI can stimulate growth by replacing labor, which is a limited resource, with capital, an unlimited resource, both for the production of goods, services and ideas.

Our study contributes to the previous literature and presents a descriptive analysis of the impact of AI on technological development, economic growth and employment.

With the continuous development of the global COVID-19 epidemic, mobile learning has become one of the most significant learning approaches. The mobile learning resource is the basis of mobile learning; it may directly affect the effectiveness of mobile learning. However, the current learning resources cannot meet users' needs. This study aims to analyze the influencing factors of accepting open data as learning resources among users.

Based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), this study proposed a comprehensive theoretical research model. Data were obtained from 398 postgraduates from several universities in central China. Confirmatory factor analysis was used to determine the reliability and validity of the measurement model. Data has been analyzed using SPSS and AMOS software.

The results suggested that perceived usefulness, performance expectancy, social influence and facilitating conditions have a positive influence on accepting open data as learning resources. Perceived ease of use was not found significant. Moreover, it was further shown in the study that behavioural intention significantly influenced the acceptance of open data as learning resources.

There is a lack of research on open data as learning resources in developing countries, especially in China. This study addresses the gap and helps us understand the acceptance of open data as learning resources in higher education. This study also pays attention to postgraduates' choice of learning resources, which has been little noticed before. Additionally, this study offers opportunities for further studies on the continuous usage of open data in higher education.

Open innovation has attracted the attention of experts and business entities for the sustainable survivability of firms, especially in the post-COVID-19 era. The food and beverage industry has been facing sustainable survivability problems. It is important to identify and evaluate the factors of open innovation from the perspectives of the food and beverage industry. This study serves that purpose by identifying and evaluating the factors of open innovation in the post-COVID-19 era with a special reference to Pakistan's economy.

The present study integrates the Fuzzy Delphi Method (FDM), Interpretive Structural Modelling (ISM) and Matrice d’ Impacts Croises Multiplication Applique a Classement (MICMAC) methods to analyze the factors involved in the adoption of open innovation in the food and beverage industry in Pakistan. Firstly, based on an extensive literature review of the most relevant studies, the factors affecting open innovation have been identified and finalized using FDM and experts' opinions. Secondly, the hierarchical framework has also been prepared by implementing the ISM approach. Thirdly, the MICMAC approach was employed to evaluate the factors to examine the driving and dependence powers of the factors of open innovation adoption.

The study identified 17 factors of open innovation adoption in Pakistan's food and beverage industry and 16 factors were finalized using FDM. The ISM-MICMAC matrix unveiled that awareness seminars and training, along with a lack of executive commitments, were strong factors with high driving power, but these factors proved to be weakly dependent powers regarding the other factors. Moreover, a lack of innovation strategy, R&D and non-supportive organizational culture exhibited low driving power but strong dependent power.

The findings of the study could help firms and business entities understand the driving and dependent factors involved in open innovation for the sustainable survivability of the food and beverage industry. The study provides strong reasons to believe that an open innovation strategy, along with stakeholder collaboration, the adoption of rules and regulations and managerial commitment, could stimulate open innovation. Moreover, governments should promote the business sector, especially the food and beverage industry, to facilitate the sector while also providing awareness seminars and training, creating environments conducive to reducing innovation costs.

Some previous studies have analyzed the factors involved in green innovation from the perspective of the manufacturing industry and environmental protection. The present study is a pioneer study to examine the factors involved in the adoption of open innovation in the food and beverage industry in Pakistan from the perspective of the post-COVID-19 era. For this purpose, the present study uses an integrated Fuzzy Delphi-ISM-MICMAC approach for the analysis.

The study aims to add to the body of knowledge of open source tangible product management (also called open design). The objective is also to develop a guideline for efficient open source tangible product development and adoption.

The exploratory research design using secondary data (like newspapers, magazines, research articles, bogs, papers, etc.) is used to analyze open source tangible product design challenges and enablers. The success stories of Open Source Software projects (OSS) were studied for identification of critical success factors and further their relevancy was tested in the two popular cases of open source drug discovery (malaria and tuberculosis)

Open innovation has become a part of competitive strategy of current businesses. It requires an efficient intellectual property protection regime for its implementation. However, in a market dominated by proprietary benefits, the open source technology development can serve as remedy for innovation needs of neglected sectors. The OSS literature revealed managing two classes of factors, namely technology sponsor level factors and environmental factors for efficiency and effectiveness. The case study analysis in the context of applicability of these OSS critical factors showed their limitations in open source tangible products, and highlighted understanding additional challenges and remedies.

Open source innovation is a collaborative effort involving inputs from various/diverse players, hence monitoring the effort and motivation level of the contributors is a cumbersome task. Only the information that is available online and in print media is taken as research inputs in this work. Also the data taken were from two case studies; a lot more case studies in the open design domain can progress the theory. The implications of this study are far-reaching in the areas where profit motivated proprietary efforts lack in addressing societal need. It provides guidelines for addressing those unmet needs by developing products in a collaborative way without intellectual property hurdles.

The essence of open design is becoming more vital, and there is a pressing need to build theory to support it, which still is elusive and dispersed. The study fills the gap using secondary data and case study approach.

The purpose of this study was to investigate a framework for the implementation of freedom of information (FOI) legislation in South Africa, against Article 19’s nine principles of FOI legislation.

This qualitative study used semi-structured interviews to collect data from six experts selected by means of the snowball sampling technique and content analysis. The study used a modified Delphi design consisting of two rounds of interviews.

The results showed that little effort is made by government officials to demonstrate commitment to the implementation of FOI legislation.

The passing of FOI is expected to reduce corruption, increase public participation, reduce the level of secrecy and increase transparency and openness. This is not the case as the implementation of this socioeconomic right in South Africa is faced by numerous challenges, such as a lack of political will, secrecy laws providing for the opposite of what the FOI legislation seeks to achieve, poor legislative interpretation and a lack of clear policies. The study proposes a framework aimed at addressing these challenges.

The study provides a framework for the implementation of FOI legislation. The framework was developed under the guidance of Article 19 principles of freedom of information legislation.

This study aims to develop a methodology for predicting the resilience of individual firms to economic crisis, using historical government data to optimize one of the most important and costly interventions that governments undertake, the huge economic stimulus programs that governments implement for mitigating the consequences of economic crises, by making them more focused on the less resilient and more vulnerable firms to the crisis, which have the highest need for government assistance and support.

The authors are leveraging existing firm-level data for economic crisis periods from government agencies having competencies/responsibilities in the domain of economy, such as Ministries of Finance and Statistical Authorities, to construct prediction models of the resilience of individual firms to the economic crisis based on firms’ characteristics (such as human resources, technology, strategies, processes and structure), using artificial intelligence (AI) techniques from the area of machine learning (ML).

The methodology has been applied using data from the Greek Ministry of Finance and Statistical Authority about 363 firms for the Greek economic crisis period 2009–2014 and has provided a satisfactory prediction of a measure of the resilience of individual firms to an economic crisis.

The authors’ study opens up new research directions concerning the exploitation of AI/ML in government for a critical government activity/intervention of high importance that mobilizes/spends huge financial resources. The main limitation is that the abovementioned first application of the proposed methodology has been based on a rather small data set from a single national context (Greece), so it is necessary to proceed to further application of this methodology using larger data sets and different national contexts.

The proposed methodology enables government agencies responsible for the implementation of such economic stimulus programs to proceed to radical transformations of them by predicting the resilience to economic crisis of the firms applying for government assistance and then directing/focusing the scarce available financial resources to/on the ones predicted to be more vulnerable, increasing substantially the effectiveness of these programs and the economic/social value they generate.

To the best of the authors’ knowledge, this study is the first application of AI/ML in government that leverages existing data for economic crisis periods to optimize and increase the effectiveness of the largest and most important and costly economic intervention that governments repeatedly have to make: the economic stimulus programs for mitigating the consequences of economic crises.

COVID-19 lockdown imposed by the government in India affected markets, factories, industries, construction sites, restaurants, transport services, etc. in which migrant workers were engaged. Migrant workers expected that after the imposition of the lockdown government will facilitate transportation facilities to their hometowns as they lost their jobs. But due to negligence from both the central and the state governments, neither transportation was arranged nor food, shelter or health facilities were arranged. This paper aims to investigate the causes of the death of migrant workers under the theoretical framework of negligence theory.

The authors have applied the content analysis method and collected 926 death cases of migrant workers from 28 Indian states. The researchers applied this technique as the newspapers in India are one of the largest and most useful sources to generate a report/record on migrant workers causes during the COVID-19 lockdown. During the lockdown period, due to lack of attention from the government, police atrocities, hunger and accidental cases increased which resulted in the death of migrant workers.

The finding of the study proves that during the first phase of COVID-19 lockdown the death cases of migrant workers increased due to negligence by the governments. The number of death cases of migrant workers has been analysed through applying negligence theory which included duty, breach, cause, in fact, proximate cause and harm which increased the death cases. It has been noticed that the central government without any prior notice-imposed lockdown all over India, in which all the states were not received any guidelines or communication which indicate what they have to do or how to handle the Covid-19 situation.

To the best of the authors’ knowledge, this is an original work of researchers which is basically based on negligence theory.