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The purpose of this article is to identify design guidelines for online resources based on the subjective assessment criteria used by individuals to assess and process information resources. This method of creating design guidelines targeted at precise user groups has the potential to aid designers and developers to create more user-centred information resources.

The authors gathered data using a prospective longitudinal study investigating the information behaviour of expectant and new mothers. Women were asked to report on their information-seeking activities in a series of semi-structured interviews covering pregnancy and early motherhood.

This research identified 15 assessment criteria that were utilised by women to assess and process information resources. The most popular resource criteria amongst participants were credibility and convenience, while completeness and relevance were the most popular information content criteria. The authors found that assessment criteria were not considered in isolation, with criteria such as formatting and search engine ranking impacting on participants' perception of other criteria.

This research demonstrates the potential of linking a user groups subjective assessment criterion to design guidelines. The authors propose that these guidelines could be used to help design an online information resource. They could also be used to assess if an existing online resource met the needs of a user group. The methodology used in this study could be leveraged to create design guidelines for user groups.

This research uses subjective assessment criteria as a means of understanding how expectant new mothers process information resources. People use subjective judgements when processing information resources, and this should be incorporated into the design of information resources. Analysing longitudinal data allowed the authors to build a rich picture of how participants evaluated and compared different information resources.

This study aims to, first, propose a valid and reliable scale to document the COVID-19 Pandemic Shopping Experience (CPSE) and, second, determine the impact of its variables on the postpurchase shopping experience (PPSE).

For scale development, published studies were scanned and the variables were shortlisted. These shortlisted variables were validated by 52 faculties from four universities in Saudi Arabia. Data were collected from 318 respondents to purify the CPSE Scale. In Study 2, a path analysis was performed on a sample of 354 respondents to determine the individual impact of each variable on PPSE.

A total of 14 items were found to be aligned under four variables, social distance (SD), shop hygiene, operational time and entertainment venues. SD was found to have the greatest influence on PPSE, followed by operational time and shop hygiene.

This research has important implications for retailers to initiate changes in store layout so that they can implement social distancing by physically marking stickers on the floors and by placing barricading on billing counters. Store hygiene can be ensured by making sanitizers and hand gloves available at the entry points, periodically cleaning the floor and sanitizing the premises. Rationing the operating time proved to be an effective tool to minimize the exposure time, thereby limiting consumers' time inside the store.

To the best of the authors’ knowledge, this is the first study to propose a full-scale measure of the customer shopping experience (SE) during a pandemic. This scale can be generalized to measure SE in similar situations.

Precise vehicle localization is a basic and critical technique for various intelligent transportation system (ITS) applications. It also needs to adapt to the complex road environments in real-time. The global positioning system and the strap-down inertial navigation system are two common techniques in the field of vehicle localization. However, the localization accuracy, reliability and real-time performance of these two techniques can not satisfy the requirement of some critical ITS applications such as collision avoiding, vision enhancement and automatic parking. Aiming at the problems above, this paper aims to propose a precise vehicle ego-localization method based on image matching.

This study included three steps, Step 1, extraction of feature points. After getting the image, the local features in the pavement images were extracted using an improved speeded up robust features algorithm. Step 2, eliminate mismatch points. Using a random sample consensus algorithm to eliminate mismatched points of road image and make match point pairs more robust. Step 3, matching of feature points and trajectory generation.

Through the matching and validation of the extracted local feature points, the relative translation and rotation offsets between two consecutive pavement images were calculated, eventually, the trajectory of the vehicle was generated.

The experimental results show that the studied algorithm has an accuracy at decimeter-level and it fully meets the demand of the lane-level positioning in some critical ITS applications.