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The purpose of this study is to provide insights into the way in which understanding and implementation of disruptive technology, specifically big data analytics and the Internet of Things (IoT), have changed over time. The study also examines the ways in which research in supply chain and related fields differ when responding to and managing disruptive change.

This study follows a four-step systematic review process, consisting of literature collection, descriptive analysis, category selection and material evaluation. For the last stage of evaluating relevant issues and trends in the literature, the latent semantic analysis method was adopted using Leximancer, which allows more rapid, reliable and consistent content analysis.

The empirical analysis identified key research trends in big data analytics and IoT divided over two time-periods, in which research demonstrated steady growth by 2015 and the rapid growth was shown afterwards. The key finding of this review is that the main interest in recent big data is toward overlapping customer service, support and supply chain network, systems and performance. Major research themes in IoT moved from general supply chain and business information management to more specific context including supply chain design, model and performance.

In addition to providing more awareness of this research approach, the authors seek to identify important trends in disruptive technologies research over time.

Career preparedness is critical to successfully transitioning from college to a full-time work environment. Being prepared means students have the technical and non-technical skills to help an organization meet its strategic goals. In many instances, students lack the requisite career competencies and collaborative skills. This study proposes designing an instructor-led, career-driven quasi-virtual internship to address this issue and improve students’ preparedness and teamwork.

Our research integrates and expands the four-step processes outlined in instructional and course redesign theories, including analysis, design and development, implementation and evaluation. In the evaluation phase, a survey is used to collect data, and natural language processing is applied to identify the emerging themes. The sample included 104 undergraduate students enrolled in an information systems degree program, which resulted in a digital corpus of 40,744 words for analysis.

Results indicate that the implementation of instructor-led quasi-virtual internships offered a comprehensive career experience comparable to an onsite or virtual company-sponsored internship in five notable areas: (1) application of technical knowledge, (2) critical thinking, (3) time management, (4) application of IT project management and (5) collaborative skills.

Our study only evaluated participants in our treatment group. Future research should examine the differences between students who complete sponsored versus instructor-led quasi-virtual internships. Researchers can add a control group of students who receive a theoretically based capstone course. Future research can simulate randomized controlled trials (RCT) (Chalmers et al., 1981) to measure the effectiveness of quasi-virtual internships. A benefit of this research is that an open-ended survey allows for collecting rich, multifaceted primary data. The second limitation is that the study included only single-item questions. Future authors can create and validate a survey instrument based on the themes and concepts emanating from our investigation. For example, they can operationalize team dynamics and career preparedness using a reflective model in which the underlying construct drives the indicators, requiring multiple items to examine each construct (Coltman et al., 2008). Third, the sample size in the study was relatively small. We can increase the sample size using a time-series dataset with multiple measurement periods. The fourth limitation is context; this study was conducted at a public university; other institutions may have varied teaching approaches, student demographics and resources. By expanding our study using a multi-site approach (Ballantyne et al., 2012), we can increase cross-sectional sample sizes and improve the generalizability of the study’s results.

This study provides several practical insights for educators by examining quasi-virtual internships. Academic institutions that want to offer internships but struggle to establish industry partners can implement the quasi-internship model as an alternative approach. The study highlights that students gain critical insight into their future careers during these internships by being familiar with industry concepts and tools. We also provide insights into engaging students in “meaningful collaboration.” We suggest the instructor provide some time during the class sessions for group work to improve coordination and introduce industry-level tools that provide a more applied approach to IS education.

The National Survey of College Internships (NSCI) 2021 indicates that underrepresented and first-generation students were less likely to participate in internships. Our research may positively impact diverse communities since the quasi-virtual internship allows all students to participate once they are enrolled in a capstone class.

To our knowledge, this study is the first to utilize latent semantic analysis to analyze students’ feedback to improve course design, career preparedness and team dynamics.

In the wake of the pandemic (COVID-19) throughout the United States, many educators had to promptly switch to online modality to continue to provide education to students with safety through physical distancing requirements. This study mainly focuses on delivering an Information Systems module aligned with the information systems curriculum model during a pandemic.

The authors used data collection techniques from student assignments and course tasks. These data are used for the purpose of academic assessment for the Information Systems program. The student performance is assessed on a 5-point scale (1 being low and 5 being high) for the synchronous and the asynchronous tasks related to the course. The authors compared the student performance during the pandemic to the pre-pandemic semester.

This study revealed that the technical module of an information systems course can be successfully delivered during a pandemic in a remote session. However, the authors found that there is a decline in the student performance in synchronous tasks and asynchronous tasks. But the decline of the student performance in the synchronous tasks is greater than that of the asynchronous tasks. The result of this study helps the Information Systems program with their assessment and to improve their course delivery during a pandemic.

In this paper, the authors examine the delivery of a technical module in the field of information systems via online learning models. The authors particularly examine the synchronous and asynchronous online learning models in the delivery of the technical module. The lessons learned from transitioning to the online modality can help universities better prepare for the future during unprecedented times.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

The adoption of climate-smart agricultural practices (CSAPs) is important for sustaining Indian agriculture in the face of climate change. Despite considerable effort by both national and international agricultural organizations to promote CSAPs in India, adoption of these practices is low. This study aims to examine the elements that affect the likelihood and intensity of adoption of multiple CSAPs in Bihar, India.

The probability and intensity of adoption of CSAPs are analyzed using multivariate and ordered probit models, respectively.

The results show significant correlations between multiple CSAPs, indicating that their adoptions are interrelated, providing opportunities to exploit the complementarities. The results confirm that both the probability and intensity of adoption of CSAPs are affected by numerous factors, such as demographic characteristics, farm plot features, access to market, socio-economics, climate risks, access to extension services and training. Farmers who perceive high temperature as the major climate risk factor are more likely to adopt crop diversification and minimum tillage. Farmers are less likely to adopt site-specific nutrient management if faced with short winters; however, they are more likely to adopt minimum tillage in this case. Training on agricultural issues is found to have a positive impact on the likelihood and the intensity of CSAPs adoption.

The major policy recommendations coming from of our results are to strengthen local institutions (public extension services, etc.) and to provide more training on CSAPs.

By applying multivariate and ordered probit models, this paper provides some insights on the long-standing discussions on whether farmers adopt CSAPs in a piecemeal or in a composite way.

Conservation agriculture-based wheat production system (CAW) can serve as an ex ante measure to minimize loss due to climate risks, especially the extreme rainfall during the wheat production season in India. This study aims to examine whether farmers learn from their past experiences of exposure to climate extremes and use the knowledge to better adapt to future climate extremes.

The authors used data collected from 184 farmers from Haryana over three consecutive wheat seasons from 2013-2014 to 2015-2016 and multivariate logit model to analyse the driver of the adoption of CAW as an ex ante climate risk mitigating strategies based on their learning and censored Tobit model to analyse the intensity of adoption of CAW as an ex ante climate risk mitigation strategy. Farmer’s knowledge and key barriers to the adoption of CAW were determined through focus group discussions.

The analysis shows that the majority of farmers who had applied CAW in the year 2014-2015 (a year with untimely excess rainfall during the wheat season) have continued to practice CAW and have increased the proportion of land area allocated to it. Many farmers shifted from CTW to CAW in 2015-2016.

While farmers now consider CAW as an ex ante measure to climate risks, a technology knowledge gap exists, which limits its adoption. Therefore, designing appropriate methods to communicate scientific evidence is crucial.

This paper uses three years panel data from 184 farm households in Haryana, India, together with focus groups discussions with farmers and interviews with key informants to assess if farmers learn adaptation to climate change from past climate extremes.

This study aims to examine smallholder farmers’ perceptions toward the adoption of climate-smart agriculture (CSA) in smallholder farmers in the Upper Blue Nile Highlands of Ethiopia. Available research focused on profitability and economic constraints alone, disregarding the farmers’ perception of the adoption of CSA innovations. There is relatively little empirical work on farmers’ perceptions of innovations. Hence, a critical research gap that will strengthen CSA innovation research and practice includes understanding farmers’ perceptions about CSA innovations and how these perceptions interact with their adoption.

A cross-sectional household survey was conducted among 424 smallholder farmers selected from five agro-ecosystems. A structured questionnaire was used to collect primary data and a review of literature and documents was used to collect secondary data. The study used a multivariate probit model to examine perception factors affecting the likelihood of adopting multiple CSA innovations. The dependent variables were eight CSA innovations, while the independent variables were crafted from the three pillars of CSA.

Major CSA innovations adopted by farmers include improved variety, crop residue management, crop rotation, compost, row planting, soil and water conservation, intercropping and agroforestry. Farmers’ perception toward CSA innovations includes: CSA innovations sustainably increase productivity and income; enhance soil fertility; diversify livestock feed and energy sources; reduce soil erosion, weed infestation and crop failure; enhance soil organic matter, reduce chemical fertilizer use and rehabilitate land. Farmers’ positive perceptions of the benefits of CSA innovations for increasing crop productivity, reducing agricultural vulnerability to climate change and lowering farm greenhouse gas emissions have boosted adoption.

Farmers’ perceptions toward CSA innovations must be enhanced to increase the adoption of CSA innovations in the smallholder agriculture system. The CSA innovation scale-up strategies should focus on farmers’ perception of CSA innovation benefits toward food security, climate change adaption and mitigation outcomes. Awareness of CSA needs the close collaboration of public extension as well as local institutions such as farmers’ training centers.

The study adopts a multivariate probit model that models farmers’ simultaneous CSA innovation choices. Hence, this study contributes to the literature in four significant areas. First, it argues for differential treatment of the perception of smallholder farmers about innovations is needed. Second, it recognizes the interdependence of the adoption of innovations. Third, it directly assesses the farmers’ perception, while others use proxies to measure it. Finally, there are limited or no studies that address the perception of innovations within the lens of adopter perception theory.

Explainable artificial intelligence (XAI) has importance in several industrial applications. The study aims to provide a comparison of two important methods used for explainable AI algorithms.

In this study multiple criteria has been used to compare between explainable Ranked Area Integrals (xRAI) and integrated gradient (IG) methods for the explainability of AI algorithms, based on a multimethod phase-wise analysis research design.

The theoretical part includes the comparison of frameworks of two methods. In contrast, the methods have been compared across five dimensions like functional, operational, usability, safety and validation, from a practical point of view.

A comparison has been made by combining criteria from theoretical and practical points of view, which demonstrates tradeoffs in terms of choices for the user.

Our results show that the xRAI method performs better from a theoretical point of view. However, the IG method shows a good result with both model accuracy and prediction quality.

The purpose of this study is to provide a review of the existing research landscape on work-life balance and women’s career motivation. It examines the relationship between work-life balance and career motivation in the context of Indian women. Specifically, it explores how the work-life balance of women influences the motivational aspects of their careers.

The research uses a systematic literature review to identify and analyze relevant literature on work-life balance and women’s career motivation among Indian women from the Scopus database.

The study uncovers critical insights into the connection between work-life balance and women’s career decisions. It gives insight on how work-life balance significantly impacts women’s career choices. The SLR reveals a notable and consistent upward trend in the domains of work-life balance and career motivation among women.

The findings of this study can inform organizations in tailoring policies that foster women’s career growth while simultaneously supporting a healthy work-life balance. In addition, the research can empower women to make informed decisions about their careers and personal lives. Ultimately, it contributes to creating a more inclusive and gender-equitable work environment, promoting both women’s career aspirations and their overall well-being.

This research stands out in its examination of the relationship between work-life balance and women’s career motivation, particularly in the unique context of Indian women. While previous studies have explored these topics individually, this research bridges the gap by investigating their interplay. Moreover, the application of a systematic literature review approach to these variables in the context of Indian women represents a novel contribution.