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Insights about customers' processes of value creation are highly beneficial for companies striving to provide customers with added value in their processes. Customer processes (CPs) of a complex nature, like buying real estate, are highly heterogenous and comprise numerous activities. Existing data-gathering methods either overlook the variability of these processes or do not record their contents thoroughly. Drawing conclusions from analyzing such poor-quality data can result in the design of supposedly customer-centric offerings that fail to provide customers with value in their processes. This paper aims to introduce a method for gathering complex CP data of superior quality: the interactive questionnaire.

The design of the novel method is guided by requirements derived from the literature. In a field study, the method's performance is compared with that of existing methods.

The interactive questionnaire produces data quality gains by combining data gathering in large sample sizes with features enabling survey participants to interact with each other. A field study confirms that it outperforms all hitherto existing methods in terms of the quality of the obtained data.

This study contributes to CP management literature by introducing a method capable of gathering complex CPs in sample sizes sufficiently large to accurately reflect their variability and in broad scope including activities beyond the company's perception. Having such CP data available is the precondition for a joint optimization of CPs and aligned business processes.

The purpose of this study is to identify typical sample sizes and response rates in questionnaire research studies within the discipline of information systems, as well as the top statistical analyses utilized for questionnaire data in these studies.

A total of 842 articles published between the years of 2000 and 2019 were identified that met the criteria of using a questionnaire as the research method. These articles were analyzed based on the sample size, response rate (if applicable) and statistical analysis methods used.

The typical questionnaire study received between 136 (first quartile) and 374 (third quartile) respondents, with a median number of 217. Typical response rate ranged between 16.5% and 50.0%, with a median of 27.8%. it was found that articles published in journals included in the Social Science Citation Index had significantly larger numbers of respondents than those not included in the index, though no difference was found for response rate. Studies that utilized more advanced statistical methods (regression analysis, structural equation modeling) were found to have significantly larger sample sizes than those that utilized only descriptive statistics or t-tests. Structural equation modeling, including the partial least squares approach, was used in the largest number of studies.

This study is the first to broadly examine the typical sample size, response rates and methods of statistical analysis used in information systems questionnaire studies. The findings of this study may be useful for systems researchers in developing appropriate procedures for questionnaire-based research.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

Questionnaire studies of knowledge, attitude and practice (KAP) are effective research in the field of health, which have many shortcomings. The purpose of this research is to propose an automatic questionnaire-free method based on deep learning techniques to address the shortcomings of common methods. Next, the aim of this research is to use the proposed method with public comments on Twitter to get the gaps in KAP of people regarding COVID-19.

In this paper, two models are proposed to achieve the mentioned purposes, the first one for attitude and the other for people’s knowledge and practice. First, the authors collect some tweets from Twitter and label them. After that, the authors preprocess the collected textual data. Then, the text representation vector for each tweet is extracted using BERT-BiGRU or XLNet-GRU. Finally, for the knowledge and practice problem, a multi-label classifier with 16 classes representing health guidelines is proposed. Also, for the attitude problem, a multi-class classifier with three classes (positive, negative and neutral) is proposed.

Labeling quality has a direct relationship with the performance of the final model, the authors calculated the inter-rater reliability using the Krippendorf alpha coefficient, which shows the reliability of the assessment in both problems. In the problem of knowledge and practice, 87% and in the problem of people’s attitude, 95% agreement was reached. The high agreement obtained indicates the reliability of the dataset and warrants the assessment. The proposed models in both problems were evaluated with some metrics, which shows that both proposed models perform better than the common methods. Our analyses for KAP are more efficient than questionnaire methods. Our method has solved many shortcomings of questionnaires, the most important of which is increasing the speed of evaluation, increasing the studied population and receiving reliable opinions to get accurate results.

Our research is based on social network datasets. This data cannot provide the possibility to discover the public information of users definitively. Addressing this limitation can have a lot of complexity and little certainty, so in this research, the authors presented our final analysis independent of the public information of users.

Combining recurrent neural networks with methods based on the attention mechanism improves the performance of the model and solves the need for large training data. Also, using these methods is effective in the process of improving the implementation of KAP research and eliminating its shortcomings. These results can be used in other text processing tasks and cause their improvement. The results of the analysis on the attitude, practice and knowledge of people regarding the health guidelines lead to the effective planning and implementation of health decisions and interventions and required training by health institutions. The results of this research show the effective relationship between attitude, practice and knowledge. People are better at following health guidelines than being aware of COVID-19. Despite many tensions during the epidemic, most people still discuss the issue with a positive attitude.

To the best of our knowledge, so far, no text processing-based method has been proposed to perform KAP research. Also, our method benefits from the most valuable data of today’s era (i.e. social networks), which is the expression of people’s experiences, facts and free opinions. Therefore, our final analysis provides more realistic results.

With the development of the internet, huge numbers of reviews are generated, disseminated, and shared on e-commerce and social media websites by internet users. These reviews usually indicate users’ opinions about products or services directly, and are thus valuable for efficient marketing. The purpose of this paper is to mine online users’ attitudes from a huge pool of reviews via automatic question answering.

The authors make use of online reviews to complete an online investigation via automatic question answering (AQA). In the process of AQA, question generation and extraction of corresponding answers are conducted via sentiment computing. In order to verify the performance of AQA for online investigation, online reviews from a well-known travel website, namely Tuniu.com, are used as the experimental data set. Finally, the experimental results from AQA vs a traditional questionnaire are compared.

The experimental results show that results between the AQA-based automatic questionnaire and the traditional questionnaire are consistent. Hence, the AQA method is reliable in identifying users’ attitudes. Although this paper takes Chinese tourism reviews as the experimental data, the method is domain and language independent.

To the best of the authors’ knowledge, this is the first study to use the AQA method to mine users’ attitudes towards tourism services. Using online reviews may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

The aim of this paper is to compare the use of critical incident technique (CIT) for gathering student feedback in higher education (HE) with the more traditional and commonly used questionnaire survey method.

The investigation involved a critical evaluation of the standard self‐completion, multi‐question “tick‐box” quantitative survey questionnaire traditionally employed to collect student feedback in HE, against the more qualitative critical incident technique that was tested within the HE context. This evaluation was supported by a review of the extant literature to determine the advantages and disadvantages of both feedback methods and a comparison of the data gathered from university students using both survey instruments. Conclusions were then drawn regarding the value of both methods. The criteria used for the comparison were the design and administration of the survey instruments, analysis and quality of the data collected, and finally, the potential usefulness of the data to HE managers.

The main issue regarding suitability of approach is resource utilisation. The CIT questionnaire is much quicker and easier to design than the traditional questionnaire, asking only a small number of questions. However, completion, input and analysis of the CIT questionnaire take longer than the standard tick‐box questionnaire. The richness of the data more than compensates for these drawbacks. In principle, the qualitative critical incident technique should be used to complement the existing methods of gathering student feedback in order to find out what is significant to students. However, in practice, it is more likely that managers within HE will continue to use the more traditional survey questionnaire, because of the limited resources available to them.

Not only is CIT a method that can be used by researchers in the education sector nationally and internationally, to gather rich and useful data about the student experience but it may also be useful for gathering information from other stakeholders.

The paper is the first to use CIT to gather feedback from students on their university experience. It proposes that, in order to obtain valid and reliable data on which to base service provision decisions, university management should consider using this qualitative technique in combination with more traditional quantitative methods of gathering student feedback.

The purpose of this paper is to assess the reliability of questionnaires as a method of quantifying household food waste (FW), thus providing context regarding the validity of existing Italian estimates.

A total of 30 households were involved in a diary study that was conducted for one week. The participating households were first asked about their FW quantities in a questionnaire. Half of the households who filled their diaries properly were then audited through waste sorting analysis performed on their garbage. Non-parametric tests were used to test for differences in FW estimates between audited and non-audited households, as well as differences among estimates obtained through different quantification methodologies.

Edible FW was estimated to be 489 grams per week based on questionnaires, and 1,035 grams per week based on diaries. In the audited sub-sample of households, FW estimates were 334 grams per week based on questionnaires, 818 grams per week based on diaries and 1,058 grams per week based on waste sorting analysis.

Given the small sample size in the present study, future studies can utilize larger samples to assess whether the differences identified in estimates can be replicated. Future studies can also inquire into the behavioral biases that led consumers to underestimate their FW.

Results of the present study point against the use of questionnaires to quantify household FW, hence raising some doubt on the reliability of existent Italian estimates. Where waste sorting is unfeasible, the use of adjustment methods or diaries is suggested to better inform policies.

This study is one of the first on FW quantification that tests three different methodologies on the same sample, and is the first to do so in Italy, where estimates are still very poor.

The purpose of this paper is to describe the results of the construct validity by the known-groups method of a Brazilian cooking skills and healthy-eating questionnaire.

Responses obtained from university students (n=767) for Brazilian-Portuguese cooking skills and health eating questionnaire, surveyed online, were submitted to construct validity comparing two known groups. The t-test was used to compare differences between gender (male and female) and the level of cooking knowledge (high or low) in each measure of the questionnaire. Internal consistency was evaluated by obtaining the Cronbach’s coefficient.

Women showed significantly higher means than men in all scale measures, except in the self-efficacy for using basic cooking techniques (SECT), where no differences were found. Students classified as having high cooking knowledge and had higher score means in all scales compared to the students with low levels. Internal consistency was adequate for all scales (a>0.70), except for cooking attitude (CA) (a=0.33) and cooking behavior (CB) scales (a=0.59).

SECT likely depends on cooking knowledge, independent of gender, suggesting further examination. Items and structure of CA and CB constructs also need to be examined more deeply.

A validated cooking skills and health-eating questionnaire demonstrated its ability to detect differences between groups, useful to provide data for further interventions.

No available cooking skills questionnaires were found that have been validated by the known-groups method regarding differences between gender and individuals’ level of cooking knowledge, as conducted in this study.

This article describes two approaches to obtaining the views of users of health services: the self‐completion questionnaire and the focus group. The theoretical framework of each method will be given along with implications for their use in clinical audit. Practical examples of both are provided, in relation to the work of a district general hospital. The advantages and disadvantages of questionnaires and focus groups are set out as well as some suggested steps for the successful application of the two methods. In conclusion, it is argued that when used together, focus groups and questionnaires complement one another and allow users' views to be heard within the audit process.