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The main impetus of the current paper is to identify and prioritize the work–family balance (WFB) satisfaction attributes specifically for working sole mothers' by deploying the Kano technique and weighted average method.

A multi-stage methodology has been used in the present analysis. Initially, the Kano method has been utilized to categorize the WFB satisfaction attributes using a three-dimensional WFB satisfaction scale. Afterward, the satisfaction coefficient technique was employed on Kano outcomes to get the WFB satisfaction and dissatisfaction index. Subsequently, the weighted average method was employed to prioritize the WFB satisfaction attributes.

Findings uncovered the significance of a non-linear association between WFB attributes and employed sole mothers' WFB satisfaction. The findings revealed that one-dimensional and must-be-based WFB satisfaction attributes are responsible for sole mothers' WFB satisfaction and employing organizations must not overlook them. Additionally, the results of weighted average method-based prioritization can help organizations to focus on particular WFB satisfaction criteria according to their priority level.

The findings are useful for WFB policy-makers and managers to formulate a suitable WFB strategy specifically for single mothers.

Results provide a path for employers to minimize the work–family role conflict and societal dissatisfaction that helps sole mothers to attain the desired WFB.

This study first employed a novel approach that incorporates the Kano application with the weighted average method in order to prioritize the WFB satisfaction attributes for lone mothers.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0074

The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems.

First, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided.

The results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion.

Compared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.

From a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply chain can not only broaden the scope of business but also increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects. How to coordinate multiple circular economy supply chain projects is at the core of its operation.

This paper first analyzes some typical supply chain projects in China and summarizes the main features of these projects. Secondly, considering the benefits of the project and the stakes of each project, a multi-stage stochastic programming model is established. Finally, Cplex, nested decomposition, LocalSolver and other methods are adopted to simulate and analyze the model.

The final experimental results find that the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

There are still some limitations to this study; for example, it is limited to the analysis of circular economy supply chain projects in China. The study focused on third-party logistics companies, and other enterprises in the circular economy supply chain were not considered. The research also assumed that the benefits of each circular economy project and the stakes of each project were known, which may not always be the case in real-world scenarios.

This manuscript found that investing in other circular economy projects in the supply chain can broaden the scope of business and increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects, such as recycling and repurposing initiatives. It highlights the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

Data sharing is increasingly being recognized as an essential component of scholarly research and publishing. Sharing data improves results and propels research and discovery forward. Given the importance of data sharing, the purpose of the study is to unveil the present scenario of research data repositories (RDR) and sheds light on strategies and tactics followed by different countries for efficient organization and optimal use of scientific literature.

The data for the study is collected from registry of RDR (re3data registry) (re3data.org), which covers RDR from different academic disciplines and provides filtration options “Search” and “Browse” to access the repositories. Using these filtration options, the researchers collected metadata of repositories i.e. country wise contribution, content-type data, repository language interface, software usage, metadata standards and data access type. Furthermore, the data was exported to Google Sheets for analysis and visualization.

The re3data registry holds a rich and diverse collection of data repositories from the majority of countries all over the world. It is revealed that English is the dominant language, and the most widely used software for the creation of data repositories are “DataVerse”, followed by “Dspace” and “MySQL”. The most frequently used metadata standards are “Dublin Core” and “Datacite metadata schema”. The majority of repositories are open, with more than half of the repositories being “disciplinary” in nature, and the most significant data sources include “scientific and statistical data” followed by “standard office documents”.

The main limitation of the study is that the findings are based on the data collected through a single registry of repositories, and only a few characteristic features were investigated.

The study will benefit all countries with a small number of data repositories or no repositories at all, with tools and techniques used by the top repositories to ensure long-term storage and accessibility to research data. In addition to this, the study provides a global overview of RDR and its characteristic features.

This case study will allow students to understand and analyse the process for conducting equity valuation by building a three-statement financial model, to understand and apply the workings of discounted cash flow (DCF) valuation methodology and its components, to apply the concepts related to the calculation of the weighted average cost of capital in the determination of discounting rate, to understand the terminal value calculation and assumptions thereof and to analyse the intrinsic valuation for the target company using the traditional multi-stage DCF model for investment decision-making.

In July 2019, Kapil Agarwal, an equity analyst operating out of Mumbai, India, was carefully looking over the financials of Asian Paints, a leading paints company in India. As an equity analyst, Kapil was constantly on the lookout for fundamentally strong but undervalued companies that could create long-term wealth for his equity fund. To decide upon the right valuation of Asian Paints, Kapil conducted fundamental analysis using the DCF method on the basis of available financial information. This case study puts students in an investment analyst role wherein they forecast financial statements and conduct DCF valuation for Asian Paints to discover potentially undervalued stocks for investment decision-making.

This case study is designed for use in an undergraduate or postgraduate programme in business management, particularly in a course on business valuation or investment management or security analysis.

Teaching notes are available for educators only.

CSS 1: Accounting and Finance.

Maize and rice constitute the two major staple crops in Ghana. The demand for maize and rice outstrips production levels in Ghana, creating deficits for farmers to capitalize on to increase yield. Farmers procure certified seeds from different supply outlets to improve crop yield. This study assessed the effectiveness of supply outlets of certified maize and rice seeds. This study also examined factors that influence smallholder farmers' decisions in sourcing seed from the identified supply outlets.

Multi-stage sampling was used to collect data from smallholder farmers. A total of 360 farmers were interviewed for the analysis. The multivariate probit model was used to estimate the main drivers of the supply outlets of certified seeds.

The findings revealed that farmers' decisions to use certified seeds from a particular supply outlet are contingent on household size, years of farming, extension contacts, distance to the nearest market, distance to the district capital, access to credit and farm size.

Sourcing certified seeds from National Seed Traders Association of Ghana, Research Institutions and Ministry of Food and Agriculture is more effective than input dealers, open market and family and friends.

This study is one of its kind that looked at how effective are the supply outlets of certified seed purchasing outlets.

This study aims to adopt a holistic approach to understand cruise revenue management (RM) practices that cover ticket and onboard revenues, through a cross-disciplinary literature review and practitioner interviews. An integrated cruise RM framework was developed and served as a blueprint for future cruise studies and practices.

A multi-stage approach was adopted, including a systematic literature review, two-waves of interviews with 26 cruise industry practitioners and the development of a holistic RM framework.

This study clarifies cruise RM functions across product planning, delivery stages and identifies ticket and onboard RM components. These are incorporated into the integrated framework, with weather and itinerary/ route attractiveness as additional considerations. Interviews revealed that there is no difference in the RM cycle before and during the COVID-19 pandemic, although strategies and tactics may vary in response to the market situation.

Suggestions are made regarding product and service bundling and ways for ticket and onboard revenue teams to work together to optimize total revenue. Future research directions are also provided under the categories of RM applications and concepts, ticket core activities, onboard core activities and overall issues.

To the best of the authors’ knowledge, this is the first paper to conduct a cross-disciplinary systematic literature review of cruise RM without imposing publication dates or specific databases and the first to develop an integrated cruise “total” RM framework that includes ticket and onboard revenues.

While the idea of the resilience of information systems security exists, there is a lack of research that conceptualizes, defines and specifies a way to measure it as a dynamic capability. Drawing on relevant cybersecurity and dynamic capabilities literature, this study aims to define Information Systems Security Resilience (ISSR) as a “dynamic capability of a firm to respond to, and recover from, a security attack” and test it as a new construct.

The authors employ a methodology including multiple phases to develop and test this construct of ISSR. The authors first interview senior managers from various organizations to establish the face validity of the construct; then develop and analyze a pilot survey for internal validity and reliability; and finally, design and deploy a field survey to test and externally validate the construct.

The authors conceptualize and define the construct of ISSR as a dynamic capability, develop a scale for its measurement and test it in a pilot and field survey. The construct is valid, and the measurement tool works. It demonstrates that resilience is something that is done, rather than had. As a capability, organizations need to track and measure ISSR, which is what this tool provides the ability to do.

This research contributes to the information systems and cybersecurity literature and offers valuable insights for organizations to manage their security effectively.

The growing popularity of gig and sharing economy changes not only consumption models but also employment patterns. This study aims to analyze the potential entrepreneurial nature of gig and sharing economy initiatives. As such, the authors compare the entrepreneurial intentions of gig and sharing economy workers to the general population. Further, the authors consider commonalities and differences in terms of the driving forces of both intentions to start-up and participation in gig and sharing economy, treating them as connected phenomena. Finally, the authors look into gig and sharing economy experience as a direct antecedent to entrepreneurial intentions formation.

The empirical settings for this study are derived from the sample of 1,257 individuals who participated in the Global Entrepreneurship Monitor Adult Population Survey 2018 in Russia. Methodologically, the authors rely on analysis of variance-test and binary logistic regression analysis to test the study hypotheses.

The results indicate that entrepreneurial intentions of gig and sharing economy workers are significantly higher when compared to the general population. In terms of antecedents to gig and sharing economy participation and startup intentions, similar effects of age, entrepreneurial social capital, prior entrepreneurial exit and intrapreneurial experience were revealed, while perceived self-efficacy was associated only with engagement into digital platforms. Finally, gig and sharing economy experience showed significant and positive effect on entrepreneurial intentions formation.

This study represents a first substantive effort to systematize antecedents to gig and sharing economy participation through an entrepreneurship perspective. Beyond that, this research adds to the contextualization of entrepreneurship literature stream, further defining the mechanism of entrepreneurial intentions formation in empirical settings of an emerging market with a relatively low propensity of population to develop intentions to start-up.

This study aims to focused on conducting a comprehensive assessment of the technology readiness level (TRL) of Iran’s oil field intelligence compared to other countries with similar oil reservoirs. The ultimate objective is to optimize oil extraction from this field by leveraging intelligent technology. Incorporating intelligent technology in oil fields can significantly simplify operations, especially in challenging-to-access areas and increase oil production, thereby generating higher income and profits for the field owner.

This study evaluates the level of maturity of present oil field technologies from the perspective of an intelligent oil field by using criteria for measuring the readiness of technologies. A questionnaire was designed and distributed to 18 competent oil industry professionals. Using weighted criteria, a mean estimate of oil field technical maturity was derived from the responses of respondents. Researchers evaluated the level of technological readiness for Brunei, Kuwait and Saudi Arabia’s oil fields using scientific studies.

None of the respondents believe that the intelligent oil field in Iran is highly developed and has a TRL 9 readiness level. The bulk of experts believed that intelligent technologies in the Iran oil industry have only reached TRL 2 and 1, or are merely in the transfer phase of fundamental and applied research. Clearly, Brunei, Kuwait and Saudi Arabia have the most developed oil fields in the world. In Iran, academics and executive and contracting firms in the field of intelligent oil fields are working to intelligently develop young oil fields.

This study explores the level of maturity of intelligent technology in one of Iran’s oil fields. It compares it to the level of maturity of intelligent technology in several other intelligent oil fields throughout the globe. Increasing intelligent oil fields TRL enables better reservoir management and causes more profit and oil recovery.