Infoculture

2020 ◽  
pp. 178-227
Keyword(s):  
Organizational Culture ◽  
Big Data ◽  
Occupational Group ◽  
Insight Into

The chapter examines stable beliefs, behaviors, and artifacts that revolve around organizational informing agents—culture of informing (infoculture). This concept deepens the insight into some well-known artifacts of organizational culture. The argument deconstructs the literature on organizational culture to expose such infocultural aspects. It is argued that different infocultures can exist in the same company, based on the occupational group, profession, department, and other grounds. Six types of infoculture are described, including newly introduced the team and knowledge infocultures. Case evidence on infocultures in three companies studied is used to illustrate these categories. Both a method of categorizing infocultures grounded on the idea of metaphor and the associated research inquiry are explained. The discussion also addresses the impacts of big data on infoculture. The chapter ends by presenting a case of colliding infocultures contributing to deadly air accidents.

Infoculture

2014 ◽  
pp. 142-181
Keyword(s):  
Organizational Culture ◽  
Information Technologies ◽  
Occupational Group ◽  
Digital Information ◽  
Cultural Perspective ◽  
It Industry ◽  
Beliefs And Practices ◽  
Cultural Approach ◽  
Business Departments ◽  
Insight Into

This chapter examines the stable beliefs, behaviors, and artifacts that revolve around organizational informing agents—culture of informing (infoculture). By putting on the lenses of infoculture, one can get a deeper insight into some well-known artifacts of organizational culture. While electronic digital information technologies (IT) play key roles in infocultures in the IT industry and e-commerce enterprises, any organization indeed exhibits beliefs and behaviors that refer to methods of manipulating data, managing knowledge, and to the technical means deployed to these ends. The argument deconstructs the literature on organizational culture to expose such infocultural aspects. The chapter defines components of infoculture and illustrates them with examples. Contributions to the cultural perspective are in emphasizing the behavioral component as well as in focusing on IT in their physical manifestations. It is furthermore argued that different infocultures can exist in the same company, based on the occupational group, profession, department, and other grounds. More often than not, IS departments and professionals nurture different beliefs and practices involving IT than do business departments. The second part of the chapter is devoted to categorizing infocultures. Combining relevant literatures with new insights yields in a six member taxonomy: the role/bureaucracy, matrix, clan/power, family, fiefdom/person, team, and knowledge infoculture. The last two categories advance the cultural approach to organization. Case evidence on infocultures in three case companies is used to illustrate these categories. The chapter also supplies a method of categorizing infocultures grounded on the idea of metaphor and an inquiry driven by the questions of who, what, when, why, and how.

scholarly journals The Performativity of Organizational Culture in a No Excuses Charter School

2018 ◽  
Author(s):  
Jason Radford
Keyword(s):  
Organizational Culture ◽  
Charter School ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Successful Performance ◽  
High Performing ◽  
Clear Insight ◽  
Cyclical Process ◽  
Insight Into ◽  
Successes And Failures

Theories developed by academics influence those they study, in some cases fundamentally shaping the world we study. This influential relationship, often called performativity, has gone largely unnoticed and uncommented on in organizational theory and research. The few studies investigating performativity in organizations or other fields typically focus on cases in which the ultimate success of theory's implementation is known. In this paper, I examine how one high-performing charter school sought to turn a prescribed organizational culture into reality. I find that path to successful performance is very narrow and ambiguous. The school succeeded and failed in many steps of the process, making it difficult to assess whether the initiative was successful and to attribute their successes and failures to the theory or their implementation. I conclude that performativity is a cyclical process occurring at multiple time scales. During these cycles, organizations iteratively test new implementations of the theory, seeking to gain clear insight into the success of their strategy and correctly attribute their successes and failures to decide whether the theory actually works or not.

A Visual Insight Into Tweeting Activity Before and During Natural Disasters

2021 ◽  
Vol 12 (3) ◽  
pp. 19-33
Author(s):  
Shadi Maleki ◽  
Milad Mohammadalizadehkorde
Keyword(s):  
Social Media ◽  
Big Data ◽  
Spatial Distribution ◽  
Emergency Management ◽  
Data Visualization ◽  
High Dimensionality ◽  
High Complexity ◽  
Big Data Visualization ◽  
Disaster Studies ◽  
Insight Into

Big data provided by social media has been increasingly used in various fields of research including disaster studies and emergency management. Effective data visualization plays a central role in generating meaningful insight from big data. However, big data visualization has been a challenge due to the high complexity and high dimensionality of it. The purpose of this study is to examine how the number and spatial distribution of tweets changed on the day Hurricane Harvey made landfall near Houston, Texas. For this purpose, this study analyzed the change in tweeting activity between the Friday of Hurricane Harvey and a typical Friday before the event.

Big Data Analytics in Action

2019 ◽  
pp. 266-294 ◽  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Insight Into

Have you ever wondered how companies that adopt big data and analytics have generated value? Which algorithm are they using for which situation? And what was the result? These points will be discussed in this chapter in order to highlight the importance of big data analytics. To this end, and in order to give a quick introduction to what is being done in data analytics applications and to trigger the reader's interest, the author introduces some applications examples. This will allow you, in more detail, to gain more insight into the types and uses of algorithms for data analysis. So, enjoy the examples.

An Insight into Big Data and Its Pertinence

2021 ◽  
pp. 137-156
Author(s):  
Lopamudra Hota ◽  
Prasant Kumar Dash
Keyword(s):  
Big Data ◽  
Insight Into

scholarly journals What Do Big Data Tell Us about Why People Take Gig Economy Jobs?

2019 ◽  
Vol 109 ◽  
pp. 367-371 ◽  
Author(s):  
Dmitri K. Koustas
Keyword(s):  
Big Data ◽  
Economic Activity ◽  
External Validity ◽  
Personal Finance ◽  
Simple Analysis ◽  
Balance Sheets ◽  
Participation Decision ◽  
Gig Economy ◽  
Using Data ◽  
Insight Into

The gig economy is widely regarded to be a source of secondary or temporary income, but little is known about economic activity outside of the gig economy. Using data from a large, online personal finance application, I document the evolution of non-gig income and household balance sheets surrounding the participation decision for gig economy jobs. This simple analysis reveals striking pretrends in income and assets. In addition to providing insight into the reasons why households enter the gig economy, these findings have potentially important implications for the external validity of previous studies focusing on gig economy activity only.

A Multinomial Logistic Regression Approach for Arrhythmia Detection

2017 ◽  
Vol 8 (4) ◽  
pp. 17-33 ◽  
Author(s):  
Omar Behadada ◽  
Marcello Trovati ◽  
Georgios Kontonatsios ◽  
Yannis Korkontzelos
Keyword(s):  
Logistic Regression ◽  
Big Data ◽  
Multinomial Logistic Regression ◽  
Vascular Diseases ◽  
Arrhythmia Detection ◽  
Medical Sector ◽  
The World ◽  
Regression Approach ◽  
Insight Into ◽  
Tools And Methods

Cardiovascular diseases are the leading causes on mortality in the world. Consequently, tools and methods providing useful and applicable insights into their assessment play a crucial role in the prediction and managements of specific heart conditions. In this article, we introduce a method based on multi-class Logistic Regression as a classifier to provide a powerful and accurate insight into cardiac arrhythmia, which is one of the predictors of serious vascular diseases. As suggested by our evaluation, this provides a robust, scalable, and accurate system, which can successfully tackle the challenges posed by the utilisation of big data in the medical sector.

Exploring Organizational Culture Through Artifacts in a Community Figure Skating Club

2013 ◽  
Vol 27 (6) ◽  
pp. 482-496 ◽  
Author(s):  
Cathy Mills ◽  
Larena Hoeber
Keyword(s):  
Organizational Culture ◽  
Outer Layer ◽  
Figure Skating ◽  
Sport Organizations ◽  
Community Sport ◽  
Insight Into

Although some elements of community sport organizations (CSOs) are welcoming and shared across all members, others may be contested. Organizational culture provides a conceptual lens through which to understand the meaning and experiences associated with CSOs. As the outer layer of organizational culture (Schein, 1985), artifacts can give further insight into participant experiences. The purpose of this study is to examine members’ perceptions of artifacts in a local figure skating club. We used Martin’s (1992, 2002) three perspectives to illuminate integrated, differentiated, and fragmented perspectives of The Club’s organizational culture. Eight skaters and seven adults from a midsize figure skating club in Canada participated in photo-elicited interviews. We found integration in participants’ discussion of the unique figure skating facility, differentiated perspectives of achievement-oriented artifacts, and fragmented perspectives of the skaters’ dressing rooms. Our research demonstrates the importance of examining the meanings associated with artifacts in sport organizations.

Data “big” and “small” – Examples from the Australian lexical database

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Claire Bowern
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Qualitative Data ◽  
Language Change ◽  
First Century ◽  
Lexical Database ◽  
Traditional Methods ◽  
New Methods ◽  
Statistical Approaches ◽  
Insight Into

AbstractThe twenty-first Century has been billed the era of “big data”, and linguists are participating in this trend. We are seeing an increased reliance on statistical and quantitative arguments in most fields of linguistics, including the oldest parts of the field, such as the study of language change. The increased use of statistical methods changes the types of questions we can ask of our data, as well as how we evaluate the answers. But this all has the prerequisite of certain types of data, coded in certain ways. We cannot make powerful statistical arguments from the qualitative data that historical linguists are used to working with. In this paper I survey a few types of work based on a lexical database of Pama-Nyungan languages, the largest family in Aboriginal Australia. I highlight the flexibility with which large-scale databases can be deployed, especially when combined with traditional methods. “Big” data may require new methods, but the combination of statistical approaches and traditional methods is necessary for us to gain new insight into old problems.

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