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Online grocery shopping possesses characteristics that can make it more difficult than regular online shopping. There are numerous buying decisions to make each shopping session, there are large ranges of product types to choose from and there is varied arithmetical complexity. The purpose of this paper is to examine how such characteristics influence the attitude of consumers toward online grocery shopping websites.

The authors hypothesized that the product type (search or experience product), the task arithmetic complexity, and the attention and cognitive load associated with browsing through product pictures have an effect on the attitude of online shoppers toward these websites. To test the hypotheses, 31 subjects participated in a within-subject laboratory experiment.

The results suggest that visual attention to product pictures has a positive effect on the attitude of online shoppers toward a website when they are shopping for experience goods, but that it has a negative effect on their attitude toward a website when the task arithmetic complexity is greater. They also suggest that the cognitive load associated with browsing through product pictures has a negative effect on the attitude of online shoppers toward a website when they are shopping for experience goods, and that greater cognitive load variation has a positive effect on their attitude toward a website when arithmetic task complexity is greater.

When designing online grocery websites, providing clear single unit quantities with pictures corresponding to the sales unit could help establish a clear baseline on which consumers can work out their quantity requirements. For decisions involving experience goods, product pictures may act as an important complementary information source and may even be more diagnostic than text description.

Results reinforce the relevance of enriching the study of self-reported measures of the user experience on e-commerce sites with automatic measures.

The rise of digital transaction technology has been transformative for businesses however consumer attitudes to this technology can vary. The comparison of Australians, Chinese and Sri Lankans’ consumers salient attitudes toward payment methods at the Point-of-Sale (POS) provides businesses with insights into the factors impacting consumers' payment preference.

A qualitative methodology was employed for data collection from Australian, Chinese and Sri Lankan participants. A combination of focus groups and individual interviews were carried out with a total of 35 participants.

Results indicate that factors of perceived relative advantage, perceived compatibility, perceived risk, perceived rewards, perceived situations and social influence impact consumers' payment preference at POS across all three countries, however the degree of impact varies in importance across the three countries.

In the cross-cultural comparison of the consumers' payment preference, this research highlights the complex interplay of factors that shapes these payment preferences. The findings, given the growing digitization of transactions, provides banking and financial institutions with a foundational model that can be used to improve their services and business model.

Previous studies failed to distinguish between payment choice at the time of the transaction and payment preference which is repeated behaviour. This study is the first to compare the consumers' payment preference across Australian, Chinese and Sri Lankan consumers and responds to calls for additional research that generalises consumers' payment preferences across cultures.

The purpose of this paper is to systematically summarise and evaluate recent articles on modern homebased work (2000‐2009). In identifying the key recurrent themes and commonalities in the existing research, it brings order to the variety of contributions to provide future directions for inquiry and knowledge production.

Papers are identified through systematic keyword searches of multi‐disciplinary databases. The aim is to identify papers that explore the social/organisational embeddedness of homebased work, rather than framing it as a technology related phenomena/problem.

The review highlights some contradictory evidence about the potential for change entailed in homeworking practices and an absence of studies which focus on “less visible” workers engaged in homebased production. It also argues that few longitudinal studies exist which could address the question of the ability of homebased work to initiate change.

The paper provides an evaluation of the literature to make sense of the diversity of themes and issues within existing research. The insights gained are of use to both academics researching this form of working and practitioners implementing it. Gaps within existing knowledge and directions for future study are also identified.

This paper is a timely review of the recent articles that have been published on homebased work.

The purpose of this paper is to present a novel Kriging meta-model assisted method for multi-objective optimal tolerance design of the mechanical assemblies based on the operating conditions under both systematic and random uncertainties.

In the proposed method, the performance, the quality loss and the manufacturing cost issues are formulated as the main criteria in terms of systematic and random uncertainties. To investigate the mechanical assembly under the operating conditions, the behavior of the assembly can be simulated based on the finite element analysis (FEA). The objective functions in terms of uncertainties at the operating conditions can be modeled through the Kriging-based metamodeling based on the obtained results from the FEA simulations. Then, the optimal tolerance allocation procedure is formulated as a multi-objective optimization framework. For solving the multi conflicting objectives optimization problem, the multi-objective particle swarm optimization method is used. Then, a Shannon’s entropy-based TOPSIS is used for selection of the best tolerances from the optimal Pareto solutions.

The proposed method can be used for optimal tolerance design of mechanical assemblies in the operating conditions with including both random and systematic uncertainties. To reach an accurate model of the design function at the operating conditions, the Kriging meta-modeling is used. The efficiency of the proposed method by considering a case study is illustrated and the method is verified by comparison to a conventional tolerance allocation method. The obtained results show that using the proposed method can lead to the product with a more robust efficiency in the performance and a higher quality in comparing to the conventional results.

The proposed method is limited to the dimensional tolerances of components with the normal distribution.

The proposed method is practically easy to be automated for computer-aided tolerance design in industrial applications.

In conventional approaches, regardless of systematic and random uncertainties due to operating conditions, tolerances are allocated based on the assembly conditions. As uncertainties can significantly affect the system’s performance at operating conditions, tolerance allocation without including these effects may be inefficient. This paper aims to fill this gap in the literature by considering both systematic and random uncertainties for multi-objective optimal tolerance design of mechanical assemblies under operating conditions.