Standardization Efforts: The Relationship between Knowledge Dimensions, Search Processes and Innovation Outcomes

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We explore how a standardization effort (i.e.,when a firm pursues standards to further innovation) involves different search processes for knowledge and innovation outcomes. Using an inductive case study of Vanke, a leading Chinese property developer,we show how varying degrees of knowledge complexity and codification combine to produce a typology of four types of search process: active, integrative, decentralized and passive, resulting in four types of innovation outcome: modular, radical,incremental and architectural. We argue that when the standardization effort in a firm involves highly codified knowledge,incremental and architectural innovation outcomes are fostered, while modular and radical innovations are hindered. We discuss how standardization efforts can result in a second-order innovation capability, and conclude by calling for comparative research in other settings to understand how standardization efforts can be suited to different types of search process in different industry contexts.

Business

Standardization Efforts:
The Relationship between Knowledge Dimensions,
Search Processes and Innovation Outcomes
Zongjie Xie, Jeremy Hall, Ian P. McCarthy, Martin Skitmore and Liyin Shen
Published in the journal:
Technovation and
available for free here

Standardization is the
process of developing
and implementing
specifications based on
the consensus of the
views of firms, users,
interest groups and
governments (Xie et al
2016)
Xie et al 2016. Standardization efforts: The relationship between knowledge dimensions, search processes
and innovation outcomes. Technovation. 48-49. 69-78

Standards can be
developed and governed by
Standards Development
Organizations (SDO) or
independently by firms who
have a first mover or
dominant position in the
market.

For:
Cost savings via ease of use and
optimal operations
Helps diffuse successful innovations
within and across markets
Against:
Constrains competing innovations
Postpones the gestation period
between invention and successful
commercialization

HOW TO USE STANDARDS AND
INNOVATE
A standardization effort is when a
firm pursues standards to
further innovation.
It involves different search
processes for different
knowledge and different
innovation outcomes.

STANDARDIZATION EFFORTS
Knowledge
dimensions
Search
processes
Types of innovation
outcome
A firm’s standardization effort
Xie et al 2016. Standardization efforts: The relationship between knowledge dimensions, search processes
and innovation outcomes. Technovation. 48-49. 69-78

STANDARDIZATION EFFORTS
Two types of knowledge dimension:
knowledge codification and
knowledge complexity
Four types of search: active, integrative,
decentralized and passive
Four types of innovations outcome:
modular, radical, incremental and
architectural.

Standardization
efforts: a typology of
search processes
and innovation
outcomes

Active search
Modular innovation
Integrative search
Radical innovation
Decentralized search
Incremental innovation
Passive search
Architectural
innovation
Low High
Knowledge complexity
LowHigh
Knowledgecodification
Xie et al 2016. Standardization efforts: The relationship between knowledge dimensions, search processes
and innovation outcomes. Technovation. 48-49. 69-78

Standardization efforts employ
different types of search to
acquire and assimilate
different dimensions of
knowledge and produce
different innovation outcomes.
A single standardization effort
can shift between different
search processes overtime.

Professor of Technology and
Operations Management
Beedie School of Business
Simon Fraser University
Ian McCarthy
Dr. Ian McCarthy
@toffeemen68

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Purpose – This article explores how employees can perceive and be impacted by the fakeness of their company slogans. Design/methodology/approach – This conceptual study draws on the established literature on company slogans, employee audiences, and fake news to create a framework through which to understand fake company slogans. Findings – Employees attend to two important dimensions of slogans: whether they accurately reflect a company’s (1) values and (2) value proposition. These dimensions combine to form a typology of four ways in which employees can perceive their company’s slogans: namely, authentic, narcissistic, foreign, or corrupt. Research limitations/implications – This paper outlines how the typology provides a theoretical basis for more refined empirical research on how company slogans influence a key stakeholder: their employees. Future research could test the arguments about how certain characteristics of slogans are more or less likely to cause employees to conclude that slogans are fake news. Those conclusions will, in turn, have implications for the morale and engagement of employees. The ideas herein can also enable a more comprehensive assessment of the impact of slogans. Practical implications – Employees can view three types of slogans as fake news (narcissistic, foreign, and corrupt slogans). This paper identifies the implications of each type and explains how companies can go about developing authentic slogans. Originality/value – This paper explores the impact of slogan fakeness on employees: an important audience that has been neglected by studies to date. Thus, the insights and implications specific to this internal stakeholder are novel.

Do your employees think your slogan is “fake news?” A framework for understan...

Ian McCarthy

Purpose – The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis. Design/methodology/approach – To illustrate the use of AI enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency. Findings – Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency. Research limitations/implications – This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches. Practical implications – For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines. Originality/value – To the best of the authors’ knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.

Making sense of text: artificial intelligence-enabled content analysis

Ian McCarthy

Abstract Many organizations are drowning in a flood of corporate bullshit, and this is particularly true of organizations in trouble, whose managers tend to make up stuff on the fly and with little regard for future consequences. Bullshitting and lying are not synonymous. While the liar knows the truth and wittingly bends it to suit their purpose, the bullshitter simply does not care about the truth. Managers can actually do something about organizational bullshit, and this Executive Digest provides a sequential framework that enables them to do so. They can comprehend it, they can recognize it for what it is, they can act against it, and they can take steps to prevent it from happening in the future. While it is unlikely that any organization will ever be able to rid itself of bullshit entirely, this article argues that by taking these steps, astute managers can work toward stemming its flood.

Confronting indifference toward truth: Dealing with workplace bullshit

Ian McCarthy

Despite significant academic and managerial interest in big data, there is a dearth of research on how big data impacts long-term firm performance. Reasons for this gap include a lack of objective indices to measure big data availability and its impact, and the tendency of studies to ignore the costs associated with collecting and analyzing big data, assuming that big data automatically delivers benefits to firms. Focusing on how firms create and capture value from big data about customers, we use the resource-based view (RBV) and three dimensions of big data (i.e., volume, variety and veracity) to understand when the benefits outweigh the costs. Relying on the number of downloads of mobile device applications, we find that volume of big data has a negative effect on firm performance. This result suggests that the ‘bigness’ of big data alone does not ensure value creation for a firm, and could even constitute a ‘dark side’ of big data. Because big data variety – measured as the number of types of information taken per each application – moderates the negative effects of big data volume, simultaneous high values of volume and variety allow firms to create value that positively affects their performance. In addition, high levels of veracity (i.e., a high percentage of employees devoted to big data analysis), are linked to firms benefiting from big data via value capture. These findings shed light on the circumstances in which big data can be beneficial for firms, contributing to a better theoretical understanding of the opportunities and challenges and providing useful indications to managers.

The Promise of Digitalization: Unpacking the Effects of Big Data Volume, Vari...

Ian McCarthy

Many organizations are drowning in a flood of corporate bullshit, and this is particularly true of organizations in trouble, whose managers tend to make up stuff on the fly and with little regard for future consequences. Bullshitting and lying are not synonymous. While the liar knows the truth and wittingly bends it to suit their purpose, the bullshitter simply does not care about the truth. Managers can actually do something about organizational bullshit, and this Executive Digest provides a sequential framework that enables them to do so. They can comprehend it, they can recognize it for what it is, they can act against it, and they can take steps to prevent it from happening in the future. While it is unlikely that any organization will ever be able to rid itself of bullshit entirely, this article argues that by taking these steps, astute managers can work toward stemming its flood.

Masterclass: Confronting indifference to truth

Ian McCarthy

Confronting indifference toward truth: Dealing with workplace bullshit

Ian McCarthy

Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by latest technological advances in AI and machine learning, they offer automated procedures to create fake content that is harder and harder to detect to human observers. The possibilities to deceive are endless, including manipulated pictures, videos and audio, that will have large societal impact. Because of this, organizations need to understand the inner workings of the underlying techniques, as well as their strengths and limitations. This article provides a working definition of deepfakes together with an overview of the underlying technology. We classify different deepfake types: photo (face- and body-swapping), audio (voice-swapping, text to speech), video (face-swapping, face-morphing, full body puppetry) and audio & video (lip-synching), and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to assure deniability, Expose deepfakes early, Advocate for legal protection and Leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter the deepfake tricks as we appreciate its treats.

Deepfakes: Trick or Treat?

Ian McCarthy

Research and practice have mostly focused on the “bright side” of social media, aiming to understand and help in leveraging the manifold opportunities afforded by this technology. However, it is increasingly observable that social media present enormous risks for individuals, communities, firms, and even the whole of society. Examples for this “dark side” of social media include cyberbullying, addictive use, trolling, online witch hunts, fake news, and privacy abuse. In this article, we aim to illustrate the multidimensionality of the dark side of social media and describe the related various undesirable outcomes. To do this, we adapt the established social media honeycomb framework to explain the dark side implications of each of the seven functional building blocks: conversations, sharing, presence, relationships, reputation, groups, and identity. On the basis of these reflections, we present a number of avenues for future research, so as to facilitate a better understanding and use of social media.

Social media? It’s serious! Understanding the dark side of social media

Ian McCarthy

Licensing speed: There has been much research interest in the speed of innovation, although few consistent findings have emerged. In this study, we unpack the innovation process and focus on the commercialization stage to examine two questions: Which licensor and patent characteristics determine the speed of licensing? How does the speed of licensing impact the royalties and lumpsum payments to licensors? We addressed these questions by proposing that licensing speed is influenced by variables for licensor prominence (size and experience), licensor knowledge structuration (technological depth, technological breadth and experience), and patent appeal (forward citations, scope and complexity). We predict and find that these variables work to increase the size, complexity and duration of the licensing-out task, while also allowing licensors to take their time to review, negotiate and select agreements with higher royalty rates. These findings are counter to arguments for a fast-paced innovation strategy, as it suggests that for the commercialization stage of the innovation process the relationship between licensing speed and licensor royalty rates rewards a ‘less haste, greater payoff approach.

The propensity and speed of technology licensing: at LUISS Guido Carli Univer...

Ian McCarthy

I use the steps in this presentation to: (i) test research ideas for research papers, (ii) shape research papers, and (iii) help draft the Introduction section of a research paper. For each step I draft one or two concise paragraphs. I then present and share these with co-authors, collaborators and colleagues to test the ideas and get feedback on how interesting and valid they are. I consider and work through these steps several times during the life of a research paper framed.

Seven steps for framing and testing a research paper

Ian McCarthy

Being a Business School Professor

Ian McCarthy

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