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The authors investigate how prices of condominiums are affected by the size of the tenant-owner associations that they belong to.

The authors use data of sold apartments in the Swedish municipality Malmö 2013–2018 and estimate hedonic price regressions. The authors also perform semi-structured interviews with three senior professionals in real estate companies.

The authors find significantly negative relationships between the prices of condominiums and the size of tenant-owner associations. Also, regression results indicate that associations should be no smaller than 6–10 apartments. The interviews support that associations should not be too small or too big. The lower and upper limit was suggested by the respondents to 40–50 and 80–150 apartments, respectively. In these ranges, economies of scale can be achieved, and residents will not lose the sense of community and responsibility.

The authors do not prove causality. Smaller associations may have relatively exclusive common amenities, about which we lack data. The same relationships may not exist in different market conditions.

The authors are not aware of previous studies with the same research question. The size of tenant-owner associations may affect the price through different channels. First, several of the banks in Sweden do not always grant mortgages for condominiums that belong to small associations. Second, larger associations may have better economies of scale and more efficient property management. Third, homeowners may prefer smaller tenant-owned associations, because they may feel less anonymous and provide more influence on common amenities.

Real estate is a capital-intensive industry for which the asset values tend to be highly volatile and uncertain. Transaction costs in the industry are therefore high, and transparency for investors may be low. The need to signal reliable estimates of property assets, in the communication to external stakeholders, can therefore be expected to be of extra importance in this sector. The purpose of this paper is to investigate how real estate firms use big four auditors to signal quality.

The authors use Swedish firm level data containing all limited liability real estate companies in the country to determine the determinants of big four auditors. The data set consists of 34,306 observations and is analyzed through logit regressions.

The results show that big four companies are primarily contracted by large and mature companies, rather than new firms or firms with volatile financial records, although the latter could be expected to have a large need to signal quality. The authors also find that firms listed on the stock market and firms targeting public use real estate are more inclined to use big four companies.

Real estate is a capital-intensive industry for which the asset values tend to be highly volatile and uncertain. Transaction costs in the industry are therefore high, and transparency for investors may be low. The need to signal reliable estimates of property assets, in the communication to external stakeholders, can therefore be expected to be of extra importance in this sector. No prior study of this area has been detected.

This paper aims to explore the components of a negative memorable Airbnb experience.

Two studies of North American and British nationals were conducted online using an open-ended survey questionnaire with photo-elicitation via Amazon Mechanical Turk (MTurk). The grounded theory was used to analyse the collected data.

The findings of the current study are destination-specific and generalisation is limited. In addition, this study gathered data using an open-ended survey questionnaire with visual images (photo-elicitation technique) in MTurk. Moreover, the study participants were mainly Westerners.

Airbnb could provide hosts with a service quality checklist to warrant quality assurance across listings. Hosts must be informed, guided and monitored so that service quality standards are fulfilled. In addition, hosts should be incentivised to write an honest and accurate description of their listing.

Airbnb can provide hosts with a service quality checklist to ensure standardisation and quality assurance across listings. Hosts must be informed, guided and monitored so that service quality standards are fulfilled. In addition, hosts might benefit from training or workshops on the role of hosting and service quality management.

This is one of the first studies to explore the components of a negative memorable experience in the context of Airbnb.

负面的难忘体验：北美及英国Airbnb住客视角分析

在本文中, 我们探讨负面难忘的Airbnb体验的组成部分。

两项研究针对北美和英国居民的展开, 并使用亚马逊土耳其机器人（Amazon Mechanical Turk）来通过匿名调查问卷的方式在线进行照片提取。 采用扎根理论对收集到的数据进行了分析。

这项研究确定了负面令人难忘的Airbnb体验的三个最常见的组成部分：肮脏和恶劣的房间条件；恶劣的, 欺骗性的和粗鲁的主人行为；以及糟糕的客户服务。调查结果支持研究表明Airbnb的服务质量是不可预测的。

目前研究的结果是基于特定目的地的, 并且其概括性是有限的。 此外, 这项研究收集数据使用匿名调查问卷的MTurk视觉图像（照片提取技术）。 此外, 研究参与者主要是西方人。

爱彼迎可以为业主提供服务质量清单, 以保证整个列表款项的质量。 房主必须得到通知、指导和监测, 以便满足服务质量标准。 此外, 应该鼓励房主写一个诚实和准确的房源描述。

此研究在探索在Airbnb的背景下负面难忘体验的组成部分中处于领先地位。

爱彼迎, 记忆, 负面的难忘体验, 服务质量, 共享经济

研究论文

Experiencias negativas memorables: Perspectivas de los huéspedes de Airbnb de Norteamérica y Gran Bretaña

En este documento, exploramos los componentes de una experiencia negativa y memorable de las aerobombas.

Se realizaron dos estudios de ciudadanos norteamericanos y británicos en línea utilizando un cuestionario de encuesta abierto con foto-elicitación a través de Amazon Mechanical Turk. Se utilizó la teoría fundamentada para analizar los datos recogidos.

Este estudio identificó los tres componentes más comunes de las experiencias memorables negativas de las Airbnb: condiciones sucias y pobres de la habitación; comportamiento malo, engañoso y grosero del anfitrión; y un pobre servicio al cliente. Los resultados apoyan los estudios que indican que la calidad del servicio de Airbnb es impredecible.

Los hallazgos del estudio actual son específicos para cada destino y la generalización es limitada. Además, este estudio recopiló datos mediante un cuestionario de encuesta abierto con imágenes visuales (técnica de fotoelicitación) en MTurk. Además, los participantes del estudio eran principalmente occidentales.

Airbnb podría proporcionar a los anfitriones una lista de control de calidad del servicio para garantizar la calidad de los listados. Los anfitriones deben ser informados, guiados y monitoreados para que se cumplan los estándares de calidad del servicio. Además, se debería incentivar a los anfitriones para que escriban una descripción honesta y precisa de su listado.

Este es uno de los primeros estudios que explora los componentes de una experiencia negativa y memorable en el contexto de Airbnb.

Calidad de servicio, Memoria, Airbnb, Economía de compartir, Experiencia negativa memorable

Trabajo de investigación

The purpose of this paper is to propose how a bottom-up creation of an ambidextrous organization can be enabled. By integrating research on “contextual ambidexterity” and “individual and organizational capacity building”, an “innovation capacity building” framework is conceptualized that suggests how balance between exploration and exploitation can be maintained.

The study is conceptual. As no data are utilized, focus is on discussing the links between the two theoretical perspectives and the advantages of the proposed innovation capacity building framework.

The innovation capacity building framework discusses the influence, both positive and negative, of the local organizational context for ambidexterity, and the interactions required such as feedback between the management team and the employees so they together can build an ambidextrous working culture. A culture in which it is the individual employee that is responsible for switching between activities related to exploration and exploitation and where the management team empowers the employees to do so.

This study focuses on contextual ambidexterity and how contextual ambidexterity can be implemented as a way of working in contemporary organizations. The originality lies in the proposed framework and in the dedicated focus on “how” ambidexterity can be implemented in organizations.

The present study focused on examining the effect of treated wastewater (TWW) on soil chemical properties. Also, efforts were made to compare the soil chemical properties under TWW irrigation with that under groundwater (GW).

During the years 2021 and 2022, surface and subsurface soil samples were randomly collected in triplicate by using an auger fortnightly at two depths (20 and 40 cm) from the selected spot areas to represent the different types of irrigation water sources: TWW and GW. Samples of the GW and the TWW were collected for analysis.

This study examines the impact of TWW on soil characteristics and the surrounding environment. TWW use enhances soil organic matter, nutrient availability and salt redistribution, while reducing calcium carbonate accumulation in the topsoil. However, it negatively affects soil pH, electrical conductivity and sodium adsorption ratio, although remaining within acceptable limits. Generally, irrigating with TWW improves most soil chemical properties compared to GW.

In general, almost all of the soil’s chemical properties were improved by irrigating with TWW rather than GW. Following that, wastewater is used to irrigate the soil. Additionally, the application of gypsum to control the K/Na and Ca/Na ratios should be considered under long-term TWW and GW usage in this study area in order to control the salt accumulation as well as prevent soil conversion to saline-sodic soil in the future. However, more research is needed to thoroughly investigate the long-term effects of using TWW on soil properties as well as heavy metal accumulation in soil.

This study aims to present a step-by-step implementation of the culturo–techno-contextual approach (CTCA) in a university classroom to teach industry and competitive analysis in the Ghanaian undergraduate entrepreneurship development curriculum. It further investigates the efficacy of the CTCA in breaking difficulties related to the study of industry and competitive analysis as a difficult concept in the Ghanaian entrepreneurship development curriculum. In doing this, the CTCA is compared with the lecture method.

The study adopts a quantitative approach. A quasi-experimental design is employed to gather data from 215 level 400 (4th-year undergraduate students) entrepreneurship development students at a Ghanaian public university. The experimental group was taught with CTCA, while the control group used the lecture method. The data was collected using the industry and competitive analysis achievement test (ICAAT). As random assignment to experimental and control groups were not possible, the data were subjected to an analysis of covariance approach with pre-test scores added as a covariate.

The results show that the experimental group significantly outperformed the control group. The results further indicate the efficacy of CTCA in improving undergraduate students’ performance in complex concepts of entrepreneurship.

Researchers usually test alternative teaching methods to break down barriers to study difficulties. The study’s uniqueness stems from the CTCA’s ground-breaking application to the study of entrepreneurship development in a Ghanaian public university.

This study aims to perform a benefit segmentation and then a classification of visitors that travel to the Rocha Department in Uruguay from the capital city of Montevideo during the summer months.

A convenience sample was obtained with an online survey. A total of 290 cases were usable for subsequent data analysis. The following statistical techniques were used: hierarchical cluster analysis, K-means cluster analysis, machine learning, support vector machines, random forest and logistic regression.

Visitors that travel to the Rocha Department from Montevideo can be classified into four distinct clusters. Clusters are labelled as “entertainment seekers”, “Rocha followers”, “relax and activities seekers” and “active tourists”. The support vector machine model achieved the best classification results.

Implications for destination marketers who cater to young visitors are discussed. Destination marketers should determine an optimal level of resource allocation and destination management activities that compare both present costs and discounted potential future income of the different target markets. Surveying non-residents was not possible. Future work should sample tourists from abroad.

The combination of market segmentation of Rocha Department’s visitors from the city of Montevideo and classification of sampled individuals training various machine learning classifiers would allow Rocha’s destination marketers determine the belonging of an unsampled individual into one of the already obtained four clusters, enhancing marketing promotion for targeted offers.