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Book part
Publication date: 13 March 2023

Jochen Hartmann and Oded Netzer

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing…

Abstract

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

Book part
Publication date: 31 January 2024

Georg Grossmann, Alice Beale, Harkaran Singh, Ben Smith and Julie Nichols

Cultural heritage archiving is experiencing an increase in digitalisations of artefacts in the last 15 years. The reason behind this trend is a demand for providing information…

Abstract

Cultural heritage archiving is experiencing an increase in digitalisations of artefacts in the last 15 years. The reason behind this trend is a demand for providing information about the artefact in a more accessible way to the audience, for example, through online delivery or virtual reality. Other reasons might be to simplify and automate the management of artefacts. Having a ‘digital copy’ of artefacts, allows one to search an archive and plan its storage and dissemination in a comprehensive manner. With the increased digitalisation comes an increased use of artificial intelligence [AI] applications. AI can be very beneficial in classifying artefacts automatically through machine learning [ML] and natural language processing [NLP]. For example, an algorithm can identify the source and age of artefacts based on an image and can do this much faster for a large collection of photos than a human. Although AI provides many benefits, it also presents challenges: Sophisticated AI techniques require certain insights on how they work, need specialists to customise a solution, and require an existing large dataset to train an algorithm. Another challenge is that typical AI techniques are regarded as black boxes, which means they decide, but it is not obvious why a decision has been made. This chapter describes a project in collaboration with the South Australian Museum [SAM] on the application of AI to extract material lists from a description of artefacts. A large dataset to train an algorithm did not exist, and hence, a customised approach was required. The outcome of the project was the application of NLP in combination with easy-to-customise rules that can be applied by non-IT specialists. The resulting prototype achieved the extraction of materials from a large list of artefacts within seconds and a flexible solution that can be applied on other collections in the future.

Details

Data Curation and Information Systems Design from Australasia: Implications for Cataloguing of Vernacular Knowledge in Galleries, Libraries, Archives, and Museums
Type: Book
ISBN: 978-1-80455-615-3

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Book part
Publication date: 26 November 2020

Timothy R. Hannigan and Guillermo Casasnovas

Field emergence poses an intriguing problem for institutional theorists. New issue fields often arise at the intersection of different sectors, amidst extant structures of…

Abstract

Field emergence poses an intriguing problem for institutional theorists. New issue fields often arise at the intersection of different sectors, amidst extant structures of meanings and actors. Such nascent fields are fragmented and lack clear guides for action; making it unclear how they ever coalesce. The authors propose that provisional social structures provide actors with macrosocial presuppositions that shape ongoing field-configuration; bootstrapping the field. The authors explore this empirically in the context of social impact investing in the UK, 2000–2013, a period in which this field moved from clear fragmentation to relative alignment. The authors combine different computational text analysis methods, and data from an extensive field-level study, to uncover meaningful patterns of interaction and structuration. Our results show that across various periods, different types of actors were linked together in discourse through “actor–meaning couplets.” These emergent couplings of actors and meanings provided actors with social cues, or macrofoundations, which guided their local activities. The authors thus theorize a recursive, co-constitutive process: as punctuated moments of interaction generate provisional structures of actor–meaning couplets, which then cue actors as they navigate and constitute the emerging field. Our model re-energizes the core tenets of new structuralism and contributes to current debates about institutional emergence and change.

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Macrofoundations: Exploring the Institutionally Situated Nature of Activity
Type: Book
ISBN: 978-1-83909-160-5

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Book part
Publication date: 26 November 2020

Abstract

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Macrofoundations: Exploring the Institutionally Situated Nature of Activity
Type: Book
ISBN: 978-1-83909-160-5

Book part
Publication date: 18 January 2023

Shane W. Reid, Aaron F. McKenny and Jeremy C. Short

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational…

Abstract

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.

Book part
Publication date: 29 May 2023

R. Dhanalakshmi, Monica Benjamin, Arunkumar Sivaraman, Kiran Sood and S. S. Sreedeep

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing…

Abstract

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

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Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy
Type: Book
ISBN: 978-1-80382-555-7

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Abstract

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The Spatial Grasp Model
Type: Book
ISBN: 978-1-80455-574-3

Book part
Publication date: 24 November 2015

Allison Jai O’Dell

This chapter helps us to understand the staffing and workflow ramifications of Linked Data. A survey of the current state of metadata work, compared to the possibilities and…

Abstract

This chapter helps us to understand the staffing and workflow ramifications of Linked Data. A survey of the current state of metadata work, compared to the possibilities and intentions of Linked Data modeling and technology, allows us to make a needs assessment for future planning. Findings are that current trends in metadata work – distributed production alongside centralized management, iterative and collaborative resource description – are appropriate in a Linked Data environment, and should be further cultivated. A plan for training staff on the conceptual modeling of Linked Data is also outlined, together providing a launching pad to begin organizational planning for Linked Data.

Details

Library Staffing for the Future
Type: Book
ISBN: 978-1-78560-499-7

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Book part
Publication date: 12 July 2023

Brayden G King and Laura K. Nelson

Social movement scholars use protest events as a way to quantify social movements and have most often used large, national newspapers to identify those events. This has introduced…

Abstract

Social movement scholars use protest events as a way to quantify social movements and have most often used large, national newspapers to identify those events. This has introduced known and unknown biases into our measurement of social movements. We know that national newspapers tend to cover larger and more contentious events and organizations. Protest events are furthermore a small part of what social movements actually do. Without other readily available options to quantify social movements, however, big-N studies have continued to focus on protest events via a few large newspapers. With advances in digitized data and computational methods, we now no longer have to rely on large newspapers or focus only on protests to quantify important aspects of social movements. In this paper, we use the environmental movement as a case study, analyzing data from a wide range of local, regional, and national newspapers in the United States to quantify multiple facets of social movements. We argue that the incorporation of more data and new methods to quantify information in text has the potential to transform the way we both conceive of and measure social movements in three ways: (1) the type of focal social movement organization included, (2) the type of tactics and issues covered, and (3) the ability to go beyond protest events as the primary unit of analysis. In addition to demonstrating ways that the focus on counting protest events has introduced specific biases in the type of tactics, issues, and organizations covered in social movement research, we argue that computational methods can help us extract and count meaningful aspects of social movements well beyond event counts. In short, the infusion of new data and methods into social movements, peace, and conflict studies could lead us to a substantial shift in the way we quantify social movements, from protest events to everything that occurs outside of them.

Details

Methodological Advances in Research on Social Movements, Conflict, and Change
Type: Book
ISBN: 978-1-80117-887-7

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