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The primary purpose of this study is to examine the factors leading to shopping cart abandonment and construct a model depicting interrelationship among them using interpretive structural modeling (ISM) and Matriced Impact Croises Multiplication Appliquee an un Classement (MICMAC).

Initially, 20 factors leading to shopping cart abandonment were extracted through a systematic literature review and expert opinions. Fifteen factors were finalized using the importance index and CIMTC method, for which consistency has been checked in SPSS software through a statistical reliability test. Finally, ISM and MICMAC approach is used to develop a model depicting the contextual relationship among finalized factors of shopping cart abandonment.

The ISM model depicts a technical glitch (SC8), cash on delivery not available (SC4), bad checkout interface (SC9), just browsing (SC11), and lack of physical examination (SC12) are drivers or independent factors. Additionally, four quadrants have been formulated in MICMAC analysis based on their dependency and driving power. This facilitates technical managers of e-commerce companies to focus more on factors leading to shopping cart abandonment according to their dependency and driving power.

Taking an expert’s opinion as a base may affect the results of the study due to biases based on subjectivity.

This study’s outcomes would accommodate practitioners, researchers, and multinational or national companies to indulge in e-commerce to anticipate factors restricting the general public from online shopping.

For the successful running of an e-commerce business and to retain the confidence of e-shoppers, every e-commerce company must make a strategy for controlling factors leading to shopping cart abandonment at the initial stage. So, this paper attempts to highlight the main factors leading to shopping cart abandonment and interrelate them using ISM and MICMAC approaches. It provides a clear path to technical heads, researchers, and consultants for handling these shopping cart abandonment factors.

By integrating uses and gratification theory (UGT) and online buying behavior theory (OBBT), this study aims to examine the impact of escapism motives (self-suppression and self-expansion) and attitude toward online shopping (ATS) on eCart abandonment. In addition, the mediating role of ATS between escapism motives and eCart abandonment is examined.

Structural equations modeling was performed on the data of 400 consumers using AMOS 26.

The results indicated that escapism motivations impacted users’ eCart abandonment, and the attitude toward online shopping mediated this relationship.

The findings of this study imply that online sellers should understand the consumer motives for website use. In response, better strategies should be developed to reduce eCart abandonment.

This study extends knowledge of eCart abandonment by theoretical integration of UGT and OBBT and identification of the intrinsic predictors of virtual cart abandonment behavior. In addition, it is one of the early attempts to examine the dimensional impact of escapism on eCart abandonment.

This conceptual paper explores the complex, and neglected, area of mentor development in initial teacher education (ITE) in Australia. It focuses on the emotionality of mentoring, drawing on concepts of emotional labour and emotional intelligence to develop a framework of effective mentoring that helps explain the essence of a mentor’s role in supporting preservice teachers.

This conceptual paper draws together mentor-support practice wisdom and research literature from several relevant areas. It draws on constructive developmental theories and complex stage theory to reaffirm the intricate nature of mentor learning and development. This paper critiques the current utilitarian emphasis on mentoring as a way to improve student outcomes without first having clarity on how to improve mentoring itself.

We introduce the mentoring as emotional labour framework as a way to better understand the nature of mentoring within ITE and as a tool for developing more effective mentor supports. We present “exemplar cases”, which are amalgamations of field observations to illustrate aspects of the framework – however, we do not claim they provide evidence of the utility or accuracy of the framework.

Previous research and policy have tended to gloss over the skills required for effective mentoring, whereas this paper places the emotional labour of mentoring front and centre, explicitly conceptualising and describing the personal and interpersonal skills required in a way that aims to support and empower mentors to recognise existing strengths and areas of potential growth.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

The research paper aims to study dissatisfaction of teleworking employees in Spain during the Covid-19 health pandemic in order to propose three models: sociodemographic profile of the teleworking dissatisfied employee; advantages and disadvantages for the teleworking dissatisfied employee and advantages for the teleworking dissatisfied employee.

This study uses official open data obtained from the Spanish National Statistical Institute (INE, 2022) through Decision Trees statistical multivariable models implementing Classification and Regression Trees and Recursive Partitioning and Regression Trees techniques to determine the variables that can influence the satisfaction or dissatisfaction of the subjects.

This investigation offers three models with two sociodemographic profiles of dissatisfied teleworking employee, who is a high/middle-level manager/employee around 45 years old, and she/he lives with the partner. Regarding the most important advantage of teleworking, employees consider “use/saving of time” and as disadvantage “worse organization and coordination of work”.

This research provides empirical evidence with inductive reasoning on understanding the challenges of teleworking dissatisfied employees in Spain not only in turbulent times but also in “normalcy” to improve overall teleworker well-being and accomplish company’s and organization’s long-term objectives for better productivity and effectivity. The study has high practical value due to the integral approach incorporating dissatisfaction as a driver that can trigger negative behaviours towards the organizations and that is seldom addressed in the literature. Additionally, this paper could provide some new ideas for accomplishing “Spain Digital 2025” and “Europe’s Digital Decade: 2030” plans on institutional level.

This paper discusses the effects of enabling conditions of project knowledge management in building volatile organizational memory. The theoretical rationale underlies a recursive relationship among enabling conditions of project knowledge management, organizational learning and memory.

This research employs a qualitative descriptive single case study approach to examine a mobile application development project undertaken by a major software company in Brazil. The analysis focuses on the project execution using an abductive analytical framework. The study data were collected through in-depth interviews and company documents.

Based on the research findings, the factors that facilitate behavior and strategy in managing project knowledge pose a challenge when it comes to fostering organizational learning. While both these factors play a role in organizational learning, the exchange of information from previous experience could be strengthened, and the feedback from the learning process could be improved. These shortcomings arise from emotional tensions that stem from power struggles within knowledge hierarchies.

Based on the research, it is recommended that project-structured organizations should prioritize an individual’s professional experience to promote organizational learning. Organizations with well-defined connections between their projects and strategies can better establish interconnections among knowledge creation, sharing and coding.

The primary contribution is to provide a comprehensive view that incorporates the conditions required to manage project knowledge, organizational learning and memory. The findings lead to four propositions that relate to volatile memory, intuitive knowledge, learning and knowledge encoding.

Surgical services are settings where resilient performance (RP) is necessary to cope with a wide range of variabilities. Although RP can benefit from a supportive built environment (BE), prior studies have focused on the operating room, giving scant attention to support areas. This study takes a broader perspective, aiming at developing BE design knowledge supportive of RP at the surgical service as a whole.

Seven BE design prescriptions developed in a previous work in the context of internal logistics of hospitals, and thus addressing interactions between workspaces, were used as a point of departure. The prescriptions were used as a data analysis framework in a case study of the surgical service of a medium-sized private hospital. The scope of the study included surgical and support areas, in addition to workflows involving patients and family members, staff, equipment, sterile instruments and materials, supplies, and waste. Data collection included document analysis, observations, interviews, and meetings with hospital staff.

Results identified 60 examples of using the prescriptions, 77% of which were related to areas other than the operating rooms. The developed design knowledge is framed as a set of prescriptions, examples, and their association to workflows and areas, indicating where it should be applied.

The design knowledge is new in surgical services and offers guidance to both BE and logistics designers.

This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.