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Achieving an appropriate indoor environment quality (IEQ) is crucial to a green office environment. Whilst much research has been carried out across the globe on the ideal IEQ for green offices, little is known about which indoor environment New Zealand office workers prefer and regard as most appropriate. This study investigated New Zealand office workers' preference for a green environment.

Workers were conveniently selected for a questionnaire survey study from two major cities in the country – Wellington and Auckland. The perception of 149 workers was analysed and discussed based on the workers' demographics. The responses to each question were analysed based on the mean, standard deviation, frequency of responses and difference in opinion.

The results showed that workers' preferences for an ideal IEQ in green work environments depend largely on demographics. New Zealand office workers prefer work environments to have more fresh air and rely on mixed-mode ventilation and lighting systems. Also New Zealand office workers like to have better acoustic quality with less distraction and background noise. Regarding temperature, workers prefer workspaces to be neither cooler nor warmer. Unique to New Zealand workers, the workers prefer to have some (not complete) individual control over the IEQ in offices.

This study was conducted in the summer season, which could have impacted the responses received. Also the sample size was limited to two major cities in the country. Further studies should be conducted in other regions and during different seasons.

This study provides the opportunity for more studies in this area of research and highlights significant findings worthy of critical investigations. The results of this study benefit various stakeholders, such as facilities managers and workplace designers, and support proactive response approaches to achieving building occupants' preferences for an ideal work environment.

This study is the first research in New Zealand to explore worker preferences of IEQ that is not limited to a particular building, expanding the body of knowledge on workers' perception of the ideal work environment in the country.

The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.

This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.

With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.

Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.

Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.

Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.