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The current study aims to predict consumer complaint status (complainers or non-complainers) based on socio-demographic and psychographic factors and further to discern the differences in behavior disposition of consumer groups concerning determinants of consumer's tendency to exit (TE).

The research used survey-based data of 600 Indian consumers of three service sectors (hotel and hospitality, automobile service centers and organized retail stores). Chi-square automatic interaction detector (CHAID) decision tree analysis was used to profile consumers.

The results indicated that occupation; income; education; industry and attitude toward complaining were significant factors in profiling consumers as complainers or non-complainers. Further, determinants of TE (discouraging subjective norms, perceived likelihood of successful complaint, lower perceived switching cost, poor employee response, negative past experience and ease of complaint process) vary significantly across the groups of complainers and non-complainers.

The research questions in this study were tested with three service sectors consumers in India, so due care should be exercised in generalizing these findings to other sectors and countries. Study replication across other service sectors and countries is recommended to improve the generalizability of these findings with wider socio-demographic samples.

Firms striving for consumer retention and aim to extend their consumer life cycle can greatly benefit from the results of this study to understand the customer complaint behavior (CCB) specific to non-complaining (exit) behavior. The future researcher may benefit from replicating and extending the model in different industries for further contribution to the CCB literature.

To the best of the author's knowledge, there is no evidence of consumer segmentation based on their complaining behavior or socio-demographic and psychographic factors by employing CHAID decision tree analysis. In addition to illustrating the use of data mining techniques such as CHAID in the field of CCB, it also contributes to the extant literature by researching in a non-Western setting like India.

Entrepreneurial trait and behaviour approaches are used to identify differing entrepreneurial profiles. Specifically, this study aims to determine which entrepreneurial competencies (ECs) can predict entrepreneurial action (EA) for distinct profiles, such as male versus female, start-up versus established and for entrepreneurs within different age groups and educational levels.

The research was conducted using a survey method on a large sample of 1,150 South African entrepreneurs. Chi-squared automatic interaction detection (CHAID) algorithms were used to build decision trees to illustrate distinct entrepreneurial profiles.

Each profile has a different set of ECs that predict EA, with a growth mindset being the most significant predictor of action. Therefore, this study confirms that a “one-size-fits-all” approach cannot be applied when profiling entrepreneurs.

From a pedagogical standpoint, different combinations of these ECs for each profile provide priority information for identification of appropriate candidates (e.g. the highest potential for success) and training initiatives, effective pedagogies and programme design (e.g. which individual ECs should be trained and how should they be trained).

Previous work has mostly focused on demographic variables and included a single sample to profile entrepreneurs. This study maintains much wider applicability in terms of examining profiles in a systematic way. The large sample size supports quantitative analysis of the comparisons between different entrepreneurial profiles using unconventional analyses. Furthermore, as far as can be determined, this represents the first CHAID conducted in a developing country context, especially South Africa, focusing on individual ECs predicting EA.

The purpose of this paper is to compare influential factors of entrepreneurial activities over time in China and to compare China with other selected countries. The data are collected from Global Entrepreneurship Monitor (GEM). The method used is decision trees and chi-square automatic interaction detector (CHAID) analysis, which isolates important factors and examines entrepreneurship predictor importance.

The method used is decision trees and CHAID analysis which isolate important factors and examine entrepreneurship predictor importance. The original contribution of this paper is that this is the first time where artificial decision trees are applied to data to isolate factors that influence business startups and used across countries for comparative purposes. It is also the first application of this model to Chinese GEM. CHAID trees and predictor importance show the value of motivations of people who have already started businesses and shed light on how public policy can be influential in promoting entrepreneurship.

Results indicate that solid knowledge and skills of how to start a business and knowing someone who has already started a business are the most important factors in China and in most of the selected countries. Fear of failure is becoming less important for Chinese entrepreneurs over the years from 2003 to 2012. Results show that countries, including China, have to enhance skill and knowledge education if they want to promote small business entrepreneurship as a policy. The findings support human capital theory.

The limitations of this study are due to using aggregated data from GEM surveys, which do not allow the authors to examine individual or household behavior. The authors do not know the variance and the distribution of responses to the questions asked and the locations in which the surveys were conducted. Another limitation is that GEM data do not report regional variations which can be modeled. For future work, the authors suggest more detailed data availability which will lead to isolating entrepreneurial problems and highlighting relevant attitudes important to entrepreneurs.

Better data collection is needed at household and regional levels to understand business starts and to promote entrepreneurship.

Social implication of this research is to find out effective ways to increase entrepreneurial activities, therefore creating job opportunities and boosting economic growth. Educational programs will also decrease disparity of opportunity and incomes between different geographical regions in the country. The original contribution of this paper is that this is the first time artificial decision trees are applied to data to isolate factors that influence business startups across countries.

The original contribution of this paper is that this is the first time where artificial decision trees are applied to data to isolate factors that influence business startups and used across countries for comparative purposes. It is also the first application of this model to Chinese GEM. CHAID trees and predictor importance show the value of motivations of people who have already started businesses and shed light on how public policy can be influential in promoting entrepreneurship. This research modeled the breakdown of reasons people would start a business by using GEM data surveys.

This investigation studied a sample of 900 prospective international tourists to Norway with respect to their attitudes towards environmental sustainability on holiday. Sustainability was measured using a single item according to which the sample was first divided into highly sustainable tourists and low-sustainable tourists. Later, using a CHAID analysis, predictors of sustainability were identified. These included gender, nationality, being a nature-based tourist or not, and being interested in authenticity or not. The results showed that people who considered authenticity as important on holiday had a higher probability of being highly sustainable tourists. Further, different segments were identified. For instance, the segment that had the greatest likelihood of being highly sustainable tourists was represented by German tourists who were highly interested in authenticity on holiday.

The authors aim to investigate which social media posts' characteristics result in higher stakeholder engagement in the European context.

The authors apply chi-square automatic interaction detection (CHAID) method to categorize social media posts based on their engagement levels. The authors conduct analysis on the tweets published in 2019 by companies that draw up and publish a sustainability report on Global Reporting (www.globalreporting.org). The final sample is based on 3,045 tweets from 173 firms of different industries and countries.

The authors find that tweet characteristics, such as content type, industry type and country of a firm, significantly influence stakeholder engagement rate.

The findings are useful for practitioners and stakeholders. Firm management should consider the most important Sustainable Development Goals (SDGs) concerning their business activities for engaging stakeholders and contributing to sustainable development.

The authors consider stakeholder theory and contribute to stakeholder engagement and sustainability debate. For this purpose, the authors focus on firms' communication of SDGs through social media. To the best of the authors’ knowledge, this is the first study which considers the SDGs’ communication to determine the most significant SDGs resulting in higher stakeholder engagement.

One key issue to be addressed in multichannel retailing strategies has to do with ensuring the consistency of the retailer offerings between the brick-and-mortar and the online stores to offer their customers a seamless experience. This study assesses whether perceived congruence may be relevant to identify segments of heterogeneous based on their online loyalty levels as well as other constructs and variables related to the customer relationship with the retailer for two different product categories.

From the responses of apparel and electronics multichannel shoppers to an online survey, a CHAID algorithm was performed to identify the most relevant congruence attribute(s) perceived by customers for predicting their loyalty levels toward the online store.

The results have allowed the identification of five segments of online shoppers, both for apparel and for electronics retailing, so that customers showing the highest scores in all congruence attributes also showed a higher loyalty toward the online store.

This study presents a first insight into the link between perceived congruence and online loyalty in retailing using a CHAID segmentation-based approach by differentiating various dimensions of perceived congruence for two product categories.

The results obtained allow for inferring a series of strategies and actions that retailers can adopt for improving perceived congruence between physical and online stores along with a series of dimensions, and ultimately, increase online loyalty.

As the interest of the literature on perceived congruence between offline and online stores is relatively recent, this exploratory research contributes to shedding light on the implications of specific congruence dimensions between the offline and the online store in terms of consumer online loyalty in all multicategory setting.

This study proposes a hierarchic segmentation that develops a tree-based classification model and classifies the cases into groups. This allows for the definition of e-commerce user profiles for each of the groups. Additionally, it facilitates the development of actions to improve the adoption of the online channel that is in such high demand in the current pandemic COVID-19 context.

Regarding the created segments, two extreme segments stand out due to their marked differences and high volume. Segment 3 with 23% of the sample is the group with the most predisposition to use the online channel and is characterised by a high level of trust, more habitual use in comparison with other groups and the belief that its use implies high performance, which indicates they believe it to be useful, quick and helpful for more an effective shopping experience. The other extreme is found in segment 7. This group makes up 17.7% of the total and is the most reluctant to use the online channel. These users are characterised by the complete opposite: they have a low level of trust in this channel. However, the effort expectancy is low, i.e. they consider that the adoption of the online channel does not involve many difficulties in its learning and use. Nevertheless, they use it less regularly than the others.

Based on the conclusions reached in this study, in the current pandemic context in which consumer demand for online shopping channels for all types of products is on the rise, it is recommended that companies focus on the following aspects. It is essential to build trust with the user and show them the real benefits of e-commerce, how it would improve their life and why they should use it. Additionally, it is vital that the user perceives it as an easy procedure that does not require a significant learning curve. Other fundamental aspects would be to reduce any uncertainty the user might have about the online shopping process, to make it as easy as possible, and to design a simple, intuitive and user-friendly interface. It is also recommendable to manage data usage efficiently. To do so, the authors recommend asking the user for the least amount of information possible, offering a data protection policy and assuring them that their information will not be misused nor shared with third parties. All of this provides a series of facilities to modify the online shopping habits of users.

As in most of the research, this study presents a series of limitations that should be debated and that could open future lines of investigation. Firstly, regarding the sample used that was limited to two neighbouring countries with similar profiles a priori; it would be necessary to compare their possible cultural differences according to Hofstede's dimensions as well as increase the number of European countries being analysed to reach a more generalised conclusions. Secondly, the variables used are a combination of those derived from the UTAUT2 model and others suggested in the literature as decisive in technology adoption by users, in this sense other theories and variables could be incorporated to complete a more holistic model.

This work contributes in a general way to (1) analysing the intention to use e-commerce platforms from a set of antecedents previously defined by their importance, after a period of economic and social restrictions derived from the pandemic; (2) determination of customer segments from the classification made by the CHAID analysis; (3) characterisation of the previously defined segments through the successive divisions that were proposed in the analysis carried out.

Other fundamental aspects would be to reduce any uncertainty the user might have about the online shopping process to make it as easy as possible, and to design a simple, intuitive, and user-friendly interface. It is also recommended to manage data usage efficiently. To do so, the authors recommend asking the user for the least amount of information possible, offering a data protection policy, and assuring them that their information will not be misused or shared with third parties.

The results obtained have allowed us to establish predictive and explanatory models of the behaviour of the segments and profiles created, which will help companies to improve their relationships with online customers in the coming years.

本研究擬提出一個會發展基於樹的分類模型、以及會把案例歸入不同的類別的層次細分。這讓我們能為每個類別考慮到電子商務用戶輪廓的定義和解釋；這亦促進我們優化採用在線渠道的發展工作,而在線渠道於現時2019冠狀病毒病肆虐的情況下,實在供不應求。

就創設的細分而言,兩個極端的細分因其明顯的差別和大批量而顯得突出。佔樣本百分之二十三的細分3是擁有最大使用在線渠道傾向的細分,而細分3的特徵包括他們對在線渠道呈高信任度,比其他類別更習慣地使用,以及其相信使用在線渠道會帶來更高的績效,這表示他們相信使用在線渠道是有效的,是快捷的,是可幫助帶來成功的購物體驗的。另外的極端在細分7內發現。這類別佔整體的百分之十七點七,而他們是最不願意使用在線渠道的類別。這類別的特徵和前述的剛剛相反：他們對在線渠道的信任程度是低的,唯其努力期望是低的,也就是說,他們認為使用在線渠道是不會涉及很多在學習上或在實際應用上的困難。即使是這樣,他們較其他人卻較少使用在線渠道。

基於研究的結論,我們的建議是：於目前大流行肆虐期間,消費者對於以在線渠道網購各類商品的需求不斷增加,企業應聚焦以下的範疇：企業必須建立消費者對電子商務的信心,並為他們展示電子商務的真正好處；企業也必須使消費者明瞭電子商務如何能改善其生活,以及他們為何要使用電子商務。更重要的是使消費者覺得使用電子商務是輕而易舉的,又不涉及陡峭的學習曲線。凡此種種,就成為消費者改變其網上購物習慣的動力和誘因。至於其他基本的考慮,包括減輕消費者對使用電子商務的不確定情緒,使電子商務易於使用,以及設計一個簡易的、憑直覺能知曉的、方便使用的介面。另外,值得推薦的是、數據使用情況須有效地管理。為此,我們建議應儘量向使用者索取最低限度的資料,為他們提供資料保護政策,保證他們的資料不會被濫用或與第三者分享。

與其他大多數的研究一樣,本研究展現了一系列值得辯論的局限,而這些局限或許會開展未來研究的領域。首先,考慮到使用了一個局限於兩個以因及果演繹而成的、概況相似的相鄰國家為樣本,我們或許需要根據霍夫斯泰德文化維度理論對這兩個國家進行比較,以瞭解它們的文化差異；另外,為求能達致可普遍適用的結論,我們也需把被分析的歐洲國家的數目增加。其次,被使用的變數是兩組變數的組合,他們是從UTAUT2模型中取得的變數,以及在有關的文獻裡,就技術採用而言、使用者認為是重要的變數。就此而言,若其他的理論和變數能被包含其中,則達致的模型將會是一個更為整體的模型。

本研究就一般而言有以下的貢獻：(一) 、 在因大流行病而引起的經濟和社會限制實施時期後,研究人員分析人們如何從一套過去被認定是電子商務平台的重要前身而選擇使用電子商務平台,本研究對這方面的分析作出了貢獻；(二) 、本研究幫助確定從透過CHAID分析而來的分類中得到的顧客細分；(三) 、本研究透過進行連續分解、幫助歸納過去被認定的細分的特徵。

企業必須建立消費者對電子商務的信心,並為他們展示電子商務的真正好處；企業也必須使消費者明瞭電子商務如何能改善其生活,以及他們為何要使用電子商務。更重要的是使消費者覺得使用電子商務是輕而易舉的,又不涉及陡峭的學習曲線。凡此種種,就成為消費者改變其網上購物習慣的動力和誘因。至於其他基本的考慮,包括減輕消費者對使用電子商務的不確定情緒,使電子商務易於使用,以及設計一個簡易的、憑直覺能知曉的、方便使用的介面。另外,值得推薦的是、數據使用情況須有效地管理。為此,我們建議應儘量向使用者索取最低限度的資料,為他們提供資料保護政策,保證他們的資料不會被濫用或與第三者分享。

本研究所得的結果,讓我們可以建立多個模型、以預測並解說有關的市場部分的行為和被創建的消費者簡介,這會幫助企業改善它們今後與網上顧客的關係。

The sustainability of higher education institutions is affected various things, particularly by student satisfaction and the financial stability of the institution. An analysis of student attrition and retention plays a vital role in assessing the sustainability of a higher education institution. Using suitable analysis techniques to do success profiling of prospective and current students is crucial for students and institutions alike. Success/risk estimation is essential for any higher education institution (HEI), because determining such factors can assist higher education institutions in fulfilling their obligation to provide support, guidance and interventions for their prospective and existing students. Strategic decisions in this regard, by both students and institutions, should be based on valid and reliable profiles. The validation of established profiles increases the accuracy of existing profiles and increases the efficiency of institutional strategic planning. This article reports on the validation of a success profile for first year Accounting students established by the authors of this article in an earlier study in an open and distance learning (ODL) environment at the University of South Africa.

Catering services play important role in the Spanish economy, accounting for 6.2% of GDP in 2021. To overcome the adverse economic impacts of COVID-19, catering services are considered one of the drivers to stimulate economic growth. Hence, the main aim of this paper is to analyse the sociodemographic profile of the family's main breadwinner who allocates most of his expenditure budget on different catering services before and during the pandemic caused by the COVID-19 in Spain.

The official Family Budget Survey in Spain was used. This offers information on expenditure by families in 2019 and 2020. CHAID multivariate analysis was employed. This has proved a valuable tool in predicting expenditure, as well as determining the cause–effect relationship of this expenditure.

Findings establish the main breadwinner's expenditure on catering services based on predictors such as “year” affected by the pandemic; “type of employment contract”; “gender”; and “age”. A gender “pub-gap” in consumption in bars and cafes has been revealed, and families with a male breadwinner, on a permanent contract, between the age of 40 and 60 spent the most on catering services.

This research presents a new interdisciplinary approach to family breadwinners as a company whose spend on catering is shaping the economic recovery and leading to new answers for hospitality management. Identified factors can lead to improved decision-making and contextualisation of economic models for food service providers in a post-pandemic future.

The purpose of this study is to identify groups of employees based on their motivations, detecting the main barriers that may influence their willingness to participate in the pro-environmental initiatives proposed by their employer.

To identify the different groups of employees, an online survey was conducted, and the Chi-square automatic interaction detection algorithm segmentation technique was used with a sample of 483 employees from 9 Latin American universities.

The results allowed us to identify various segments, in which the main obstacle linked to intrinsic motivation is the university culture and, to a lesser extent, the lack of equipment, while for extrinsic motivation, the lack of infrastructure is the most determining factor. Likewise, the results reflect that, compared to the less motivated employees, those who show greater motivation (both intrinsic and extrinsic) are the ones who encounter the greatest barriers, so that the perceptions of the most motivated, as expert observers, help to identify the main obstacles that organisations must remove to promote pro-environmental behaviours among staff members.

The results obtained help to guide the representatives or organisational leaders on the actions that generate the greatest impact in the mitigation of climate change from a motivational approach of behavioural prediction.

This study contributes to a more sustainable society by developing an understanding of how employees react to issues related to climate change. Knowing the perceptions of employees can be a turning point so that other members of society can get involved in pro-environmental behaviours.

Many studies have analysed the intrinsic and extrinsic motivations of employees to engage in pro-environmental behaviours; however, as far as the authors are aware, this has not been analysed from the perspective of barriers to motivation.