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This chapter focusses on how qualitative research can capture the lived experiences of entrepreneurial individuals by exploring their subjective experiences. Traditional methods of data collection involve listening to what entrepreneurial individuals say and observing their actions, although particular attention to their feelings is often absent. To achieve data congruence and to gain a deeper understanding of their lived experiences, it is crucial to also take into consideration how they feel. This chapter will recount a confessional tale recorded during a recent field study in entrepreneurship that will shed light on the pivotal role that a researcher’s sensory processing sensitivity (SPS) trait can play during qualitative data collection in helping researchers becoming mindful of the feelings of entrepreneurial individuals, even when those feelings are not directly expressed by them. The introduction of the researcher’s SPS trait in promoting data congruence during qualitative data collection will be this chapter’s principal contribution.

This study assessed the heterogeneous impact of the Anchor Borrower Program (ABP) on the welfare distribution of rice farming households in Nigeria.

Self-selection bias and treatment endogeneity were accounted for by employing the Instrumental Variable Quantile Regression (IVQR) model. The estimates obtained from the IVQR model were further compared with those from the conventional quantile regression, and quantile regression using Propensity Score Matching. This was to highlight the extent to which endogeneity bias has been purged from the treatment, in order to establish a consistent causal link between participation in the ABP and the welfare of a cross-section of rice farming households.

ABP farmers had significantly higher rice yields across all quantiles of the yield distribution under treatment exogeneity assumption, and in only two quantiles upon controlling for observable confounders. However, this yield gain did not translate to higher Per capita Consumption Expenditure (PCE). The estimates of the more robust IVQR model provided further evidence that the rice yield and PCE of ABP farmers are not statistically different from that of non-ABP farmers across all quantiles of the welfare distribution.

The negligible impact of ABP was relatively higher for lower-yielding households. Thus, implying that, although the ABP is a pro-poor development intervention, the program has not been sufficiently implemented to significantly improve the welfare of the dominant resource-poor farming households in Nigeria.

This study assessed the impact of ABP beyond the conventional potential mean outcome framework by accounting for heterogeneity in treatment effect.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0083

Climate change is expected to cause larger and more frequent precipitation events in key agricultural regions of the United States, damaging crops and soils. Subsurface tile drainage is an important technology for mitigating the risks of a wetter climate in crop production. In this study, the authors examine how quickly farmers adapt to increased precipitation by investing in drainage technology.

Using farm-level data from the 2018 Agricultural Resource Management Survey (ARMS) of soybean producers, the authors construct a drainage adoption timeline based on when the operator began farming their land and when tile drainage was installed, if at all. The authors examine both the initial investment decision and the speed with which drainage is installed by adopters. A Heckman-style Poisson regression is used to model the count nature of adoption speed (measured in years taken to install tile drainage) and to correct for potential sample-selection bias.

The authors find that local precipitation is not a significant determinant of the drainage investment decision but may be highly influential in the timing of adoption among drainage users. Farms exposed to crop-damaging levels of precipitation install tile drainage faster than those with low to moderate levels of rainfall. Estimates of farm adaptation speeds are heterogeneous across farm and operator characteristics, most notably land tenure status.

Understanding how US farmers adapt to extreme weather through technology adoption is key to predicting the long-term impacts of climate change on America's food system. This study extends the existing climate adaptation literature by focusing on the speed of adoption of an important and increasingly common climate-mitigating technology – subsurface tile drainage.

One in six people globally are affected by infertility, and many turn to fertility treatment in a bid to have a child(ren). While many countries offer work-related legislative protections and provisions for those who are successful in conceiving a child, in the form of maternity and paternity-related supports and protection again discrimination – the same cannot be said for those struggling to conceive. There are similar inequalities when it comes to workplace policy and support. Drawing on data from our two-year research study on “complex fertility journeys” and employment, this chapter sets out the work-life challenges that arise when individuals find themselves navigating the considerable “reproductive work” of fertility treatment alongside the demands of paid employment, and how affected employees respond. It also touches on the challenges experienced by line managers tasked with offering support. The chapter concludes with implications for practice in terms of making organizations more “fertility friendly,” which should extend beyond support for attending fertility treatment appointments to include awareness raising, manager training, and support for the varied outcomes of treatment cycles, including involuntary childlessness.

In Japan, health-care systems have long been supported by physicians' long working hours. To solve this problem, there is an urgent need to improve the working environment for physicians while practicing patient-centered medicine and controlling health-care costs. This paper aims to examine the effect of task shifting to nurses and discuss its usefulness from the perspective of health-care value co-creation.

This paper analyzes task shifting to nurses in acute care hospitals in Japan as a solution for the shortage of anesthesiologists. The authors discuss how the value was created from the perspective of the health-care ecosystem, with conceptual consideration of the value co-creation mechanism through patient-centered practices.

The study showed that task shifting initiatives in Japan can improve the motivation of nurses through human resource development while maintaining high quality. The study also suggested that task shifting from physicians to nurses may contribute to improving net income and maintaining the health-care system.

The findings are highly reproducible and can be immediately applied to initiatives at other medical institutions in Japan. Furthermore, it is suggested that these findings might provide some perspective on the realignment of fragmented healthcare in the USA.

It was confirmed in practical terms that micro-level initiatives have an impact on the macro level as well. In addition, the academic presentation of the concept has contributed to the deepening of value research.

This chapter examines the role of higher education institutions (HEIs) as partners in multi-stakeholder initiatives to implement sustainable development goals (SDGs) in tourism. Accordingly, the study describes the actions and leadership of the HEIs, explaining how they engage with diverse stakeholders to enable transformative change at various levels. A conceptual model is proposed and used to evaluate the 12 case studies identified by a systematic literature search. The results generate insights into the actions of the HEIs in terms of modes of partnership and their commitment. The cases document the diversity of roles assumed by HEIs for creating impact at different levels when integrating SDGs in tourism and paving the way for transformative change and sustainable development through tourism. The findings suggest a critical leadership role for HEIs through sense-making, interpretation of societal challenges, and alignment of stakeholders’ values and goals by facilitating multi-stakeholder consultations.

In this chapter, the authors aimed to analyze the existing sustainability curriculum being followed by higher education institutions (HEIs) in the emerging world, and call out the underlying inadequacies within it and provide solutions for the same, by drawing insights through interviews with key stakeholders in this area. The authors planned to talk to higher education policymakers, Educational Institutional heads, researchers and faculty members and corporates (who deal in sustainable products and who will benefit from this subject). The respondents’ sample consisted of both Indian and international interviewees to help us better understand and analyze the perspective and scenario globally in terms of north-south as well as understand multiple point of views. The interview analyses were carried out using the N Vivo software tool. The expected outcome includes a curriculum contour on sustainability for the HEIs.

The UK government has taken the lead in accelerating the capacity of higher education to engage with sustainability accounting and adopting a novel systematic approach toward a collective implementation of and contribution to Sustainable Development Goals (SDGs). The UN SDG 16 “Peace, Justice & Strong Institutions” promotes the (re)building of effective and accountable institutions. In line with the institutional logics metatheory, we provide empirical evidence on how the alignment between social mechanisms alongside the reputation of higher education institutions (HEIs) and SDGs on transparent and responsible service (SDG 16) affect the students' overall experience. Using a sample of 142 UK HEIs, interpretative content analysis and ordinary least squares, the results show that integrating HEIs' responsible-oriented research agenda proactively with high sustainability reputation adds significantly to greater student satisfaction.

Social media rumor combating is a global concern in academia and industry. Existing studies lack a clear definition and overall conceptual framework of users' rumor-combating behaviors. Therefore, this study attempts to empirically derive a typology of rumor-combating behaviors of social media users.

A three-phase typology development approach is adopted, including content analysis, multidimensional scaling (MDS), interpreting and labeling. Qualitative and quantitative data collection and analysis methods are employed.

The elicited 40 rumor-combating behaviors vary along two dimensions: high versus low difficulty of realization, and low versus high cognitive load. Based on the two dimensions, the 40 behaviors are further divided into four categories: rumor-questioning behavior, rumor-debunking behavior, proactive-appealing behavior, and literacy enhancement behavior.

This typology will serve as reference for social media platforms and governments to further explore the interventions to encourage social media users to counter rumor spreading based on various situations and different characteristics of rumor-combating behaviors.

This study provides a typology of rumor-combating behaviors from a novel perspective of user participation. The typology delves into the conceptual connotations and basic forms of rumor combating, allowing for a comprehensive understanding of the complete spectrum of users' rumor-combating behaviors. Furthermore, the typology identifies the similarities and the differences between various rumor-combating behaviors, thus providing implications and directions for future research on rumor-combating behaviors.

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life.

The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer.

The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set.

The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.