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There is a significant decrease in students' attendance in Zoom classes compared to traditional classes. This paper investigates the factors that affect students' attitudes, behavioral intentions and actual use of Zoom for online classes at higher educational institutions (HEIs) in Sri Lanka.

This research uses the technology acceptance model (TAM) as a theoretical model. The data are collected from HEI students via an online survey form. The hypotheses between constructs in the model are tested using partial least squared–structural equation model.

The analysis shows that computer self-efficacy (CSE) affects perceived usefulness (PU) and perceived ease of use (PEU), which affects attitude (ATT) and behavioral intention (BI) and actual use (AU) of Zoom in a chain reaction. Further, PEU affects PU, which, in turn, affects BI. Furthermore, the effect size of PU to BI is larger than ATT to BI.

Students' attendance for Zoom classes mainly depends on CSE, PU and PEU. Therefore, HEIs should promote Zoom with interactive training before teaching online. Further, officials should revise the curriculum in schools to upsurge the CSE of students.

During coronavirus-19, no research was published on students' use of Zoom for online classes in the Sri Lankan context. Moreover, the TAM model has been modified by including CSE as an external variable.

Inventory is a crucial part of a systematic supply chain of a business. Small firms mostly neglect inventory management (IM) by accumulating excessive inventory for a time. The study aims to examine the IM practices of small and medium enterprises (SMEs) in Bangladesh.

The study applied a qualitative case study design. Data were collected from ten SME owners in Bangladesh. The study employed a purposive sampling technique to collect data. This study used semi-structured interviews to generate data. The NVivo software was used to analyze the data.

The findings show that most SME business owners collect raw materials from the local market. Along with the local sources, they collect raw materials from international markets. Some SME entrepreneurs collect raw materials throughout the country as they dealt with recycled products. Frequently, they used digital technologies and online media to manage raw materials. SME owners could not buy many raw materials due to financial crisis, wastage, and damage, leading to a ratio of 10–15% losses.

This research contributes greatly to the government, SME Foundation, and trade associations concerning the SME IM system. The study recommends the government should reduce the tax rate on importing SME raw materials and inventories and exporting SME products.

This is the first study that focuses on the IM systems of SMEs in Bangladesh.

This paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.

The proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.

The proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.

The new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.

This paper explores the collaborative dynamics and dimensions within a virtual multi-cultural and interdisciplinary workplace. The study focusses on the use of online communication technologies to enhance social inclusion and networking within academia.

This study uses an autoethnographic approach to draw on the personal experiences of a team of four scholars, including three early-career researchers and a senior scholar. Their reflections on their academic positionality and the institutional constraints reveal both the strengths and vulnerabilities of collaborating in a virtual workplace.

The findings offer insights into the complexities of navigating social dynamics, such as delegating responsibilities, organising meetings across various time zones and encouraging continuous collaboration, inclusivity and effective communication during an extensive timeline. As a result, their experiences revealed that a virtual workplace culture with similar and different attributes to a “normal” workplace emerged.

The paper demonstrates how to create an effective and inclusive virtual workplace by exemplifying best practices in academia and providing practical guidance for individuals and institutions based on honest, co-produced autoethnographic reflections of the authors’ lived experiences.

This paper aims to develop and apply a service design method that allows for stronger recognition and integration of human activities into the front-end stages of the service design process.

Following a discussion of different service design perspectives and activity theory, the paper develops a method called activity-set mapping (ActS). ActS is applied to an exploratory service design project to demonstrate its use.

Three broad perspectives on service design are suggested: (1) the dyadic interaction, (2) the systemic interaction and (3) the customer activity perspectives. The ActS method draws on the latter perspective and focuses on the study of human activity sets. The application of ActS shows that the method can help identify and visualize sets of activities.

The ActS method opens new avenues for service design by zooming in on the micro level and capturing the set of activities linked to a desired goal achievement. However, the method is limited to activities reported by research participants and may exclude unconscious activities. Further research is needed to validate and refine the method.

The ActS method will help service designers explore activities in which humans engage to achieve a desired goal/end state.

The concept of “human activity set” is new to service research and opens analytical opportunities for service design. The ActS method contributes a visualization tool for identifying activity sets and uncovering the benefits, sacrifices and frequency of activities.