Eye-Tracking Methodology in Organizational Research

[We’re pleased to welcome authors Martin Meißner University of the Southern Denmark and Josua Oll of the University of Hamburg. They recently published an article in Organizational Research Methods entitled “The Promise of Eye-Tracking Methodology in Organizational Research: A Taxonomy, Review, and Future Avenues,” which is currently free to read for a limited time. Below, Dr. Meißner recounts the events that led to the research and the significance it has to the field:]

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What motivated you to pursue this research?

Self-report methods continue to be widely used by organizational scholars, although their limitations are well-documented. Explicit calls have therefore been made for more frequent utilization of behavioral data and building on multi-method data sources. In this context, eye tracking (ET) represents one promising source of behavioral data. ET is widely employed in disciplines such as psychology and marketing, but only rarely used in organizational research. The paucity of ET studies in organizational research is surprising as other disciplines have used ET in areas of high relevance to organizational research, such as information search and decision-making, learning, training, and expertise. Furthermore, technological advances in recent years have greatly lowered the barriers for using eye tracking (ET) as a research tool in laboratory and field settings. Given that the costs for ET equipment are on a steady decline and that data quality and ease of use have also improved considerably over the years, we argue that the time is right to expand the standard methodological tool kit of organizational scholars by bringing ET to their minds and hands.

What has been the most challenging aspect of conducting your research? Were there any surprising findings?

The most challenging aspect was the development of our integrative taxonomy for eye tracking research. Several ET taxonomies already circulate in the literature but these usually approach ET from a very specific and quite narrow angle. The challenging part was thus to bring those different perspectives together and integrate them in such a way that the full methodological scope of ET comes across clearly.

In what ways is your research innovative, and how do you think it will impact the field?

Our research is innovative in the sense that we introduce ET, and thus a new mode of behavioral data, to the field of organizational science. We further offer a novel taxonomy for ET research that integrates the more specific perspectives on ET as presented in prior work. Our paper serves as a knowledge brokering paper that reviews and synthesizes past research, and provides future avenues for the application of ET in organizational research. We therefore hope that our work will stimulate the organizational reader’s imagination and motivation for using ET and thereby contribute to the method’s future dissemination and to the advancement of organizational science alike.

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Business and Society Special Issues on Digital Technology and Business Responsibilities

ball-63527_1920Business and Society just recently published its new special issue titled “The Governance of Digital Technology, Big Data, and the Internet: New Roles and Responsibilities for Business.” This Issue features a collection of articles that explore how new technologies and innovations have changed the social responsibilities of businesses. What does the digital age hold for corporate social responsibility?

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Business & Society aims to be the leading, peer-reviewed outlet for scholarly work dealing specifically with the intersection of business and society. They publish research that develops, tests and refines theory, and which enhances our understanding of important societal issues and their relation to business. It is the official journal of the International Association of Business and Society.

To read more about the issue click here.

 

 

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Data photo attributed to geralt. (CC)

Text Mining in Organizational Research

text-mining-1476780_1920[We’re pleased to welcome authors Dr. Stefan Mol, Vladimer B. Kobayashi, Hannah A. Berkers, Gabor Kismihok, and Deanne N. Den Hartog of the University of Amsterdam. They recently published an article in Organizational Research Methods entitled “Text Mining in Organizational Research,” which is currently free to read for a limited time. Below, Dr. Mol recounts the events that led to the research and the significance it has to the field:]

07ORM13_Covers.inddWere there any specific external events that influence your decision to pursue this research?
One critical on-going event that lead us to pursue this research is the revolution and promise brought by the rise of big data to understand and enhance organizational processes. A large proportion of these data are comprised of texts that are generated every day at rates that imply that manual analysis of all of this data is no longer possible. The abundance of untapped text data suggest the existence of information with the promise of generating new knowledge that may be used to enhance both individual and organizational level outcomes.
Although, organizations already collect and store text data, many do not fully take advantage of the knowledge that can be gleaned from analyzing text. This may be due to a lack of expertise in conducting automatic text analysis or text mining. The mission of our work here is to empower organizational researchers by raising awareness of the possibilities afforded by text mining, helping them see how text mining might help them answer their research questions, and helping them to understand and use the text mining process and tools.
In what ways is your research innovative, and how do you think it will impact the field?
With this article we hope to contribute by facilitating dialogue between data scientists and organizational researchers about the opportunities afforded by text mining. As an example, we illustrate the role that text mining of vacancies might play in job analysis. Previous approaches to job analysis rely on time consuming collection and analysis of survey and observation based data the results of which soon become outdated due to the fast changing nature of jobs. Using text mining we demonstrate how one can take advantage of other data sources such as online job vacancies to understand the requirements and skill demands of different types of jobs. Our goal is to not only apply text mining to the field of job analysis but more importantly to inform organizational researchers about the wide-ranging uses text mining could have in organizational research. We hope that this will spark an increase in the use of text data and machine learning in organizational research.
What advice would you give to new scholars and incoming researchers in this particular field of study?
Existing text mining solutions are technique and tool-oriented because most techniques and Big Data tools are currently primarily shaped by technical fields, such as statistics and computer science, that put greater emphasis on the computational and technological aspects. However, applying these in the field of organizational research holds great promise. Organizational researchers bring with them a repertoire of organizational theories. These theories provide domain specific information and requirements that can influence the selection of techniques and analytical strategy, and the way to evaluate the success of the particular application. Our advice for incoming organizational researchers wanting to explore text mining is to draw on their own theoretical expertise and from there start selecting the appropriate techniques and approaches to text mining. Also, as with using other analytical tools, we do need to pay careful attention to rigor in evaluation and validation of text mining based results.

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The Influence of Textual Cues on First Impressions of an Email Sender

[We’re pleased to welcome authors Shannon L. Marlow of Rice University, Christina N. Lacernza of University of Colorado Boulder, and Chelsea Iwig of the NASA Johnston Space Center. They recently published an article in the Business and Professional Communication Quarterly entitled “The Influence of Textual Cues on First Impressions of an Email Sender,” which is currently free to read for a limited time. Below, Marlow reveals the motivation for conducting this research:]

BPCQ.indd“Our motivation in pursuing the present research was to uncover practical implications regarding how to compose an email and explore facets of virtual communication. Specifically, we were interested in emails within a business context and how subtle cues within the email would influence perceptions of the email sender. We assessed whether closing salutations would impact the email receiver’s perception of the sender. As the cues within an email are limited, we believed that such cues would have an impact. We manipulated closing salutations (i.e., no salutation, “Thanks!,” “Best,” or “Thank you”), gender of the email sender, and sending method (i.e., email sent via desktop computer/laptop as compared to email sent via a mobile device). We assessed how these manipulations influenced perceptions of positive affect, negative affect, professionalism, and competence.

We were surprised to find that, on the whole, study participants rated women senders as more professional across the majority of conditions; however, women were rated as less competent when they used the “Thanks!”salutation. It appears that women are penalized for using this particular salutation whereas men are perceived similarly, in regards to competence, across all closing salutations examined in this study. We were further surprised to find that there were no differences in regards to perceptions of senders using different sending methods. It appears individuals perceive mobile devices as a professional method of communication for business exchanges. Finally, and in line with similar findings from the literature, we found that positive affect can be conveyed through using an exclamation point (i.e., using “Thanks!” as a closing salutation) and thus punctuation may be used to convey excitement and enthusiasm about work-related matters. On the whole, our findings indicate that subtle cues within emails are capable of influencing perceptions and individuals should reflect carefully when composing an email to ensure they are doing so in a way that promotes desired perceptions.”

 

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Text Classification for Organizational Researchers: A Tutorial

baby-84626_1920[We’re pleased to welcome author Dr. Stefan Mol of the University of Amsterdam. Dr. Mol recently published an article in Organizational Research Methods entitled “Text Classification for Organizational Researchers: A Tutorial,” which is currently free to read for a limited time. Below, Dr. Mol reflects on the inspiration for conducting this research:]

07ORM13_Covers.inddWhat motivated you to pursue this research?
Machine Learning assisted text analysis is still uncommon in organizational research, although its use holds promise. Most manual text analysis procedures conducted by researchers in this field are about the assignment of text to categories such as in thematic and template analyses. However, manual classification of text becomes laborious and time consuming (and sometimes subject to reliability issues) when one needs to do this for a sizeable amount (hundreds of thousands or millions) of pieces of text. An alternative is to use automatic text classification systems that can be constructed by researchers, which allow them to speed up the process of labeling or coding large sets of textual data. The design and building of text classifiers could be of use for various areas of organizational research. Our aim was to illustrate how this could be done and provide a tutorial. We used the example of building a text classifier to automatically sort job type information contained in job vacancies. The importance of validating the results of text classification was demonstrated through data triangulation, using expert input. We believe that the use of this procedure among organizational researchers can improve reliability and efficiency in analysis that involves classification.
What has been the most challenging aspect of conducting your research? Were there any surprising findings?
Building classifiers involves several rounds of training, testing, and validation before they can be deployed in practice and the most challenging aspect is training the classifier and choosing the parameters in such a way that the results are valid from the standpoint of application. The classifier we built for the job analysis task was able to recover job task sentences with high precision as assessed by an expert in the field, although the classifier was initially trained with minimum expert input. Our results thus suggest that job vacancies are a reliable alternative source of job information that can augment existing approaches to job analysis. More generally, we believe this also suggests that wider use of text classification holds promise for organizational research in a broader sense.
What did not make it into your published manuscript that you would like to share with us?
One class of techniques that are now increasingly applied in the area of text classification are word embeddings. Word embeddings map each word to vectors of real numbers. The similarities among word vectors can be used to quantify and categorize the meaning of words in specific contexts. We initially planned to include a short discussion about this but we decided not to because these techniques warrant more in depth discussion which go beyond the scope of our current article. However, organizational researchers interested in recovering context specific meaning of words may benefit from the specific approach taken with word embeddings and we recommend them to get to know these techniques as well.

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Highlights from California Management Review’s Latest Issue

Calicmra_59_2.cover.pngfornia Management Review has served as a bridge of communication between academia and management practice for sixty years. The newest issue of CMR is now online to view, and features articles covering various topics such as managing technology through outsourcing, managing customer relations, and analyzing sustainability in big corporations.

One article in particular, “Decentralization and Localization of Production: The Organizational and Economic Consequences of Additive Manufacturing (3D Printing),” co-authored by Avner Ben-Ner and Enno Siemsen, provides a glimpse into the research behind 3D printing, and how the phenomenon will likely become a local practice faster than you think.  The article is currently free to read for a limited time. Please find the abstract for the article below:

The future organizational landscape may change drastically by mid-century as a result of widespread implementation of 3D printing. This article argues that global will turn local; mega (factories, ships, malls) will become mini; long supply chains will shrink; many jobs will be broadened to combine design, consulting, sales, and production roles; and large organizations will make room for smaller ones. “A once-shuttered warehouse is now a state-of-the art lab where new workers are mastering the 3D printing that has the potential to revolutionize the way we make almost everything.” [President Obama, State of the Union Address, 2013].

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Want to submit to CMR? Visit https://mc.manuscriptcentral.com/uc-cmr to begin your submission!

Does using clickers in class help students engage and succeed?

With the growing technology advances and integration of new technology into classrooms, professors across the nation have adopted clickers as a means of participation in lectures. Of course, with new engagement strategies comes pros and cons, including how students must remember to bring the clickers, and if lost, will have to pay to replace the unit. The clickers can also prompt students to pay more attention in class, sinJME(D)_72ppiRGB_powerpoint.jpgce clickers can be used to take quizzes, and in turn, keep an online record of attendance.

A recent article in the Journal of Marketing Education entitled “Using Clickers in a Large Business Class: Examining Use Behavior and Satisfaction,” analyzes the use of clickers in the classroom which yields overall positive responses in content engagement. Authors Nripendra P. Rana and Yogesh K. Dwivedi also provide data on the behavioral intentions of the students in their study. The abstract for their article is below:

As more and more institutions are integrating new technologies (e.g., audience response systems such as clickers) into their teaching and learning systems, it is becoming increasingly necessary to have a detailed understanding of the underlying mechanisms of these advanced technologies and their outcomes on student learning perceptions. We proposed a conceptual model based on the technology acceptance model to understand students’ use behavior and satisfaction with clickers. The valid response from 138 second-year business students of Digital Marketing module taught in a British university, where clickers are extensively used in the teaching and learning process, made the basis for data analysis. The results provided a strong support for the proposed model with a reasonably adequate variance (i.e., adjusted R2) of 67% on behavioral intentions and sufficiently high variance on use behavior (i.e., 86%) and user satisfaction (i.e., 89%).

The article is free to read for a limited time, and don’t forget to sign up for email alerts through the homepage so you never miss a new issue.