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Recruiting immigrant populations, particularly recent arrivals, is challenging due to lack of sampling frames and other factors. The purpose of this paper is to report the feasibility of using a quasi-random sampling strategy for recruiting recent Latin American (LA) immigrants.

The initial recruitment strategy included random selection of two census tracts (CTs) with high concentrations and numbers of recent LAs in Toronto, and door-to-door recruitment. Based on challenges encountered this strategy was modified by consulting trusted community members and recruiting participants residing in selected CTs using cultural venues.

Door-to-door recruitment of the target group is difficult. Challenges included accessing individuals living in apartment buildings, lack of trust and fear of deportation, transitory residency, and difficulty recruiting very recent arrivals. The modified strategy was more efficient and yielded higher recruitment rates, and was more acceptable to participants.

The limited timeframe of the study and lack of timely census data may have prevented full exploration of study methodologies.

The study demonstrated that recruitment rates of recent immigrants and refugees can be improved by randomly selecting CTs with high concentrations and numbers of recent immigrants and using culturally appropriate recruitment strategies. These groups may not be homogeneously distributed in selected geographic areas (e.g. CTs); it may be necessary to focus on pockets of high concentration as identified by community members who are familiar with the area.

Longitudinal investigations are often suggested but rarely used in operations and supply chain management (OSCM), mainly due to the difficulty of obtaining data. There is a silver lining in the form of existing large-scale and planned repeated cross-sectional (RCS) data sets, an approach commonly used in sociology and political sciences. This study aims to review all relevant RCS surveys with a focus on OSCM, as well as data and methods to motivate longitudinal research and to study trends at the plant, industry and geographic levels.

A comparison of RCS, panel and hybrid surveys is presented. Existing RCS data sets in the OSCM discipline and their features are discussed. In total, 30 years of Global Manufacturing Research Group data are used to explore the applicability of analytical methods at the plant and aggregate level and in the form of multilevel modeling.

RCS analysis is a viable alternative to overcome the confines associated with panel data. The structure of the existing data sets restricts quantitative analysis due to survey and sampling issues. Opportunities surrounding RCS analysis are illustrated, and survey design recommendations are provided.

The longitudinal aspect of RCS surveys can answer new and untested research questions through repeated random sampling in focused topic areas. Planned RCS surveys can benefit from the provided recommendations.

RCS research designs are generally overlooked in OSCM. This study provides an analysis of RCS data sets and future survey recommendations.

This paper aims to examine the concept of “consumer animosity”, model its antecedents, and assess its influence on intention to purchase.

Survey questionnaires were distributed by a quasi‐random sample of school pupils across Taiwan to an adult member of their household, for completion and return. A return rate of 70 percent yielded 456 usable questionnaires, the data from which were analysed by the LISREL structural equation modelling software.

The results suggest that perceived personal economic hardship and the normative influence of members of a consumers' reference group have a positive impact on the phenomenon of consumer animosity, which in turn negatively affects the intentions of consumers in Taiwan to purchase products originating in mainland China and Japan. Contradicting previous studies, consumer animosity was found to be dependent on judgments of product quality.

The research model was built from data collected by non‐probability sampling in a single country. There was no evidence of sampling bias, but future studies would benefit from inclusion of more independent variables and a wider geographical scope.

The findings contain many practical lessons for planners of export marketing strategy.

Two existing theories of social behaviour are integrated with the concept of consumer animosity to explain consumption choices in an international context.

The purpose of this paper is to investigate the influence of brand prominence on willingness to buy luxury brands. It also aims to investigate the direct and moderating roles of luxury brand values, social influence and vanity on willingness to buy luxury brands.

A convenience sampling method was employed. Survey questionnaires were distributed by mall intercept to quasi-random samples in downtown Perth, Western Australia for completion and return. The return yielded 779 usable questionnaires, the data from which were analysed using SPSS 22.

The findings support the influence of brand prominence on purchase intention for luxury brands. It has also been found that social influence has a significant influence on physical vanity and willingness to buy luxury brands. However, some relationships with and isolations from the earlier studies have been identified.

This study provides some meaningful insights for marketing managers regarding brands prominence that they can use in better understanding the consumers’ intention to buy luxury products. A luxury goods manufacturer may want to be cautious to not over popularize its trademark for short-term gains. There must be a delicate balance between the uses of prominent and subtle signals in luxury branding in order to maintain value as a prestigious label.

Previous studies have mainly focused on the antecedents of willingness to buy luxury brands, whereas this paper incorporates the construct of brand prominence, adding new insights into the construct.

The literature claims that respondents who fill in guest satisfaction questionnaires do not form a truly representative sample of the hotel guests because they do so on a voluntary basis. This study explores the distribution of guest satisfaction questionnaires and the biases that employees bring to the process. Data were gathered via in‐depth, semi‐structured interviews. Individuals with different characteristics were selected in an attempt to reduce the risk of producing findings specific to a certain hotel settings. The results suggest that sampling procedures that were supposed to be carried out systematically are more often done at the convenience of hotel employees or for other, self‐presentational reasons. It is suggested that the employees' intervention is a factor that biases the data and renders it less than informative. Recommendations for hotel operators are provided.

The interest in the ability to detect damage at the earliest possible stage is pervasive throughout the civil engineering over the last two decades. In general, the experimental techniques for damage detection are expensive and require that the vicinity of the damage is known and readily accessible; therefore several methods intend to detect damage based on numerical model and by means of minimum experimental data about dynamic properties or response of damaged structures. The paper aims to discuss these issues.

In this paper, the damage detection problem is formulated as an optimization problem such as to obtain the minimum difference between the numerical and experimental variables, and then a modified ant colony optimization (ACO) algorithm is proposed for solving this optimization problem. In the proposed algorithm, the structural damage is detected by using dynamically measured flexibility matrix, since the flexibility matrix of the structure can be estimated from only the first few modes. The continuous version of ACO is employed as a probabilistic technique for solving this computational problem.

Compared to classical methods, one of the main strengths of this meta-heuristic method is the generally better robustness in achieving global optimum. The efficiency of the proposed algorithm is illustrated by numerical examples. The proposed method enables the deduction of the extent and location of structural damage, while using short computational time and resulting good accuracy.

Finding accurate results by means of minimum experimental data, while using short computational time is the final goal of all researches in the structural damage detection methods. In this paper, it gains by applying flexibility matrix in the definition of objective function, and also via using continuous ant colony algorithm as a powerful meta-heuristic techniques in the constrained nonlinear optimization problem.

Introduction A product may be called new for two reasons: either because it is a novelty in the fullest sense of the word, intended to cater for a hitherto latent demand, or merely because it is a new brand, which will have to compete with a number of other brands in an established market.

This research was conducted to outline the capturing and analysis of composite texts. We contextualize this using selfies as image and textual data sourced from Instagram and analyzed using a three stage analysis approach from a genre perspective.

The capturing of composite texts is outlined for numerous services available to researchers to study social media contexts. The analysis applies a three-stage technique of (1) what is shown, (2) what is said, and (3) what is the central narrative to overcome interpretive limitations of privileging text over image or vice versa.

Based on their structural characteristics, seven genre types emerged from the coded sample set.

Issues arise in capturing this data as social media platforms change their access and usage policies and as capturing services alter their capabilities.

The paper outlines a novel approach to capturing and understanding the mimesis and diegesis of selfies as composite texts.

Social movements are heterogeneous because they attract organizations from other movements and encourage activists to create organizations “indigenous” to the movement. This chapter examines the structural and technical differences between these kinds of organizations. Employing a contingency theory framework, it is shown that older “spill over” groups are much more likely to be multi-issue national organizations with particular organizational structures. Then, it is shown that these older groups have correlated environments and internal structures, but not their more contemporary counterparts. Finally, it is shown that the adoption of a new technology, the Facebook group, is mainly a path dependency outcome, and not correlated with contingency factors.