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This study aims to draw the attention of business process management (BPM) research and practice to the textual data generated in the processes and the potential of meaningful insights extraction. The authors apply standard natural language processing (NLP) approaches to gain valuable knowledge in the form of business process (BP) complexity concept suggested in the study. It is built on the objective, subjective and meta-knowledge extracted from the BP textual data and encompassing semantics, syntax and stylistics. As a result, the authors aim to create awareness about cognitive, attention and reading efforts forming the textual data-based BP complexity. The concept serves as a basis for the development of various decision-support solutions for BP workers.

The starting point is an investigation of the complexity concept in the BPM literature to develop an understanding of the related complexity research and to put the textual data-based BP complexity in its context. Afterward, utilizing the linguistic foundations and the theory of situation awareness (SA), the concept is empirically developed and evaluated in a real-world application case using qualitative interview-based and quantitative data-based methods.

In the practical, real-world application, the authors confirmed that BP textual data could be used to predict BP complexity from the semantic, syntactic and stylistic viewpoints. The authors were able to prove the value of this knowledge about the BP complexity formed based on the (1) professional contextual experience of the BP worker enriched by the awareness of cognitive efforts required for BP execution (objective knowledge), (2) business emotions enriched by attention efforts (subjective knowledge) and (3) quality of the text, i.e. professionalism, expertise and stress level of the text author, enriched by reading efforts (meta-knowledge). In particular, the BP complexity concept has been applied to an industrial example of Information Technology Infrastructure Library (ITIL) change management (CHM) Information Technology (IT) ticket processing. The authors used IT ticket texts from two samples of 28,157 and 4,625 tickets as the basis for the analysis. The authors evaluated the concept with the help of manually labeled tickets and a rule-based approach using historical ticket execution data. Having a recommendation character, the results showed to be useful in creating awareness regarding cognitive, attention and reading efforts for ITIL CHM BP workers coordinating the IT ticket processing.

While aiming to draw attention to those valuable insights inherent in BP textual data, the authors propose an unconventional approach to BP complexity definition through the lens of textual data. Hereby, the authors address the challenges specified by BPM researchers, i.e. focus on semantics in the development of vocabularies and organization- and sector-specific adaptation of standard NLP techniques.

The purpose of this paper is to provide insightful evidence of phenomena in organization and management theory. Textual data sets consist of two different elements, namely qualitative and quantitative aspects. Researchers often combine methods to harness both aspects. However, they frequently do this in a comparative, convergent, or sequential way.

The paper illustrates and discusses a hybrid textual data analysis approach employing the qualitative software application GABEK-WinRelan in a case study of an Austrian retail bank.

The paper argues that a hybrid analysis method, fully intertwining qualitative and quantitative analysis simultaneously on the same textual data set, can deliver new insight into more facets of a data set.

A hybrid approach is not a universally applicable solution to approaching research and management problems. Rather, this paper aims at triggering and intensifying scientific discussion about stronger integration of qualitative and quantitative data and analysis methods in management research.

To provide guidance for the formalised analysis of qualitative data and observations, to raise awareness about systematic analysis and illustrate promising avenues for the application of qualitative methodologies in international marketing research.

Conceptually, the nature of qualitative research, globalisation and its implications for the research landscape, text‐data as a source for international research and equivalence issues in international qualitative research are discussed. The methodology section applies these concepts and analysis challenges to a real‐world example using N*Vivo software.

A 14‐step analytic design is developed, introducing procedures of data analysis and interpretation which help to formalise qualitative research of textual data.

The use of software programs (e.g. N*Vivo) helps to substantiate the analysis and interpretation process of textual data.

Step‐by‐step guidance on performing qualitative analysis of textual data and documenting findings.

The paper is valuable for researchers and practitioners looking for guidance in analysing and interpreting textual data from interviews. Specific support is given for N*Vivo software and its application.

The purpose of this paper is to extract appropriate terms to summarize the current results in terms of the contents of textual facets. Faceted search on XML data helps users find necessary information from XML data by giving attribute–content pairs (called facet-value pair) about the current search results. However, if most of the contents of a facet have longer texts in average (such facets are called textual facets), it is not easy to overview the current results.

The proposed approach is based upon subsumption relationships of terms among the contents of a facet. The subsumption relationship can be extracted using co-occurrences of terms among a number of documents (in this paper, a content of a facet is considered as a document). Subsumption relationships compose hierarchies, and the authors utilize the hierarchies to extract facet-values from textual facets. In the faceted search context, users have ambiguous search demands, they expect broader terms. Thus, we extract high-level terms in the hierarchies as facet-values.

The main findings of this paper are the extracted terms improve users’ search experiences, especially in cases when the search demands are ambiguous.

An originality of this paper is the way to utilize the textual contents of XML data for improving users’ search experiences on faceted search. The other originality is how to design the tasks to evaluate exploratory search like faceted search.

The purpose of this paper is to describe the work being done at The German National Library of Science and Technology (TIB) to make non‐textual information, for example three dimensional objects, more easily accessible. The goal is to create workflows and develop tools that allow academic libraries to treat this data in the same way as textual documents within the library processing chain. This implies content‐based indexing and the offering of new kinds of interfaces for searching and displaying results.

The work of TIB on non textual information is described as well as DataCite and its launch in December 2009.

That the launch of Datacite ensures that this agency will take global leadership for promoting the use of persistent identifiers for datasets, to satisfy the needs of scientists. It will, through its members, establish and promote common methods, best practices, and guidance.

The work of TIB and the launch of Datacite will ensure that non textual data will become more easily available to researchers.

The value of this work has been underlined recently with the controversy over the accessibility of climate change data.

There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing extensive textual data. To bridge this knowledge gap, this paper introduces a new methodology that combines ML techniques with traditional qualitative approaches, aiming to reconstruct knowledge from existing publications.

In this pragmatist-rooted abductive method where human-machine interactions analyse big data, the authors employ topic modelling (TM), an ML technique, to enable constructivist grounded theory (CGT). A four-step coding process (Raw coding, expert coding, focused coding and theory building) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, the authors collected data from an open-source professional project management (PM) website and illustrated their research design and data analysis leading to theory development.

The results show that TM significantly improves the ability of researchers to systematically investigate and interpret codes generated from large textual data, thus contributing to theory building.

This paper presents a novel approach that integrates an ML-based technique with human hermeneutic methods for empirical studies in OM. Using grounded theory, this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden from published data, offering a new way for academics to develop potential theories for business and management studies.

This paper aims to assess internal audit quality (IAQ) by using automated textual analysis of disclosures of internal audit mechanisms in annual reports.

This paper uses seven text mining techniques to construct classification models that predict whether companies listed on the Athens Stock Exchange are audited by a Big 4 firm, an auditor selection that prior research finds is associated with higher IAQ. The classification accuracy of the models is compared to predictions based on financial indicators.

The results show that classification models developed using text analysis can be a promising alternative proxy in assessing IAQ. Terms, N-Grams and financial indicators of a company, as they are presented in the annual reports, can provide information on the IAQ.

This study offers a novel approach to assessing the IAQ by applying textual analysis techniques. These findings are important for those who oversee internal audit activities, assess internal audit performance or want to improve or evaluate internal audit systems, such as managers or audit committees. Practitioners, regulators and investors may also extract useful information on internal audit and internal auditors by using textual analysis. The insights are also relevant for external auditors who are required to consider various aspects of corporate governance, including IAQ.

IAQ has been the subject of thorough examination. However, this study is the first attempt, to the authors’ knowledge, to introduce an innovative text mining approach utilizing unstructured textual disclosure from annual reports to develop a proxy for IAQ. It contributes to the internal audit field literature by further exploring concerns relevant to IAQ.

For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis.

This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach.

The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students.

The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.

A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.

The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.

The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.

Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

The aim of this research is to develop an eigenspace-based fuzzy c-means method for scalable topic detection.

The eigenspace-based fuzzy c-means (EFCM) combines representation learning and clustering. The textual data are transformed into a lower-dimensional eigenspace using truncated singular value decomposition. Fuzzy c-means is performed on the eigenspace to identify the centroids of each cluster. The topics are provided by transforming back the centroids into the nonnegative subspace of the original space. In this paper, we extend the EFCM method for scalability by using the two approaches, i.e. single-pass and online. We call the developed topic detection methods as oEFCM and spEFCM.

Our simulation shows that both oEFCM and spEFCM methods provide faster running times than EFCM for data sets that do not fit in memory. However, there is a decrease in the average coherence score. For both data sets that fit and do not fit into memory, the oEFCM method provides a tradeoff between running time and coherence score, which is better than spEFCM.

This research produces a scalable topic detection method. Besides this scalability capability, the developed method also provides a faster running time for the data set that fits in memory.