Creating a longitudinal, data-driven 3D model of change over time in a postindustrial landscape using GIS and CityEngine

2018 ◽  
Vol 8 (4) ◽  
pp. 434-447 ◽  
Author(s):  
John David McEwen Arnold ◽  
Don Lafreniere
Keyword(s):  
Big Data ◽  
Longitudinal Data ◽  
3D Modeling ◽  
Heritage Management ◽  
Data Driven ◽  
Change Over Time ◽  
Content Type ◽  
Modeling Software ◽  
Copper Country ◽  
Over Time

Purpose The purpose of this paper is to create a longitudinal data-driven model of change over time in a postindustrial landscape, using the “Copper Country” of Michigan’s Upper Peninsula as a case study. The models resulting from this project will support the heritage management and public education goals of the contemporary communities and Keweenaw National Historical Park that administer this nationally significant mining region through accessible, engaging, and interpretable digital heritage. Design/methodology/approach The paper applies Esri’s CityEngine procedural modeling software to an existing historical big data set. The Copper Country Historical Spatial Data Infrastructure, previously created by the HESA lab, contains over 120,000 spatiotemporally specific building footprints and other built environment variables. This project constructed a pair of 3D digital landscapes comparing the built environments of 1917 and 1949, reflecting the formal and functional evolution of several of the most important copper mining, milling, and smelting districts of Michigan’s Keweenaw Peninsula. Findings This research discovered that CityEngine, while intended for rapid 3D modeling of the contemporary urban landscape, was sufficiently robust and flexible to be applied to modeling serial historic industrial landscapes. While this novel application required some additional coding and finish work, by harnessing this software to existing big data sets, 48,000 individual buildings were rapidly visualized using several key variables. Originality/value This paper presents a new and useful application of an existing 3D modeling software, helping to further illuminate and inform the management and conservation of the rich heritage of this still-evolving postindustrial landscape.

Big data-driven supply chain and performance: a resource-based view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shampy Kamboj ◽  
Shruti Rana
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Small And Medium Enterprises ◽  
Data Driven ◽  
Equation Modeling ◽  
Firm Age ◽  
Content Type ◽  
Sustainable Performance ◽  
Medium Enterprises

PurposeThe main objective of this paper is to study the role of supply chain performance (SCP) as a mediator between big data-driven supply chain (BDDSC) and firm sustainable performance. In addition, the role of firm age as a moderator between BDDSC and SCP as well as between SCP and firm sustainable performance has also been explored.Design/methodology/approachThe 200 managers of medium or senior level positions in micro, small and medium enterprises (MSMEs) located at Delhi-NCR have been contacted. Further, collected data have been confirmed with confirmatory factor analysis (CFA). In this paper, structure equation modeling (SEM) has been employed to empirically check the proposed hypotheses and their relationships.FindingsThe findings confirmed that SCP mediates the link between BDDSC and firm sustainable performance. Additionally, firm age moderates the association between BDDSC and SCP as well as between SCP and firm sustainable performance.Research limitations/implicationsThe role of SCP and firm age between BDDSC and sustainable performance have been examined in the context of MSMEs in Delhi-NCR and thereby limit the generalization of results to other industries and country contexts.Originality/valueThe present study adds to the existing literature via recognizing the blackbox using SCP and firm age to comprehend BDDSC and firm sustainable performance relationship.

Big data-driven strategic orientation in international marketing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shahriar Akter ◽  
Md Afnan Hossain ◽  
Qiang (Steven) Lu ◽  
S.M. Riad Shams
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
International Marketing ◽  
Strategic Orientation ◽  
Data Driven ◽  
Future Research ◽  
Content Type ◽  
Strategic Orientations ◽  
Marketing Activities

PurposeBig data is one of the most demanding topics in contemporary marketing research. Despite its importance, the big data-based strategic orientation in international marketing is yet to be formed conceptually. Thus, the purpose of this study is to systematically review and propose a holistic framework on big data-based strategic orientation for firms in international markets to attain a sustained firm performance.Design/methodology/approachThe study employed a systematic literature review to synthesize research rigorously. Initially, 2,242 articles were identified from the selective databases, and 45 papers were finally reported as most relevant to propose an integrative conceptual framework.FindingsThe findings of the systematic literature review revealed data-evolving, and data-driven strategic orientations are essential for performing international marketing activities that contain three primary orientations such as (1) international digital platform orientation, (2) international market orientation and (3) international innovation and entrepreneurial orientation. Eleven distinct sub-dimensions reflect these three primary orientations. These strategic orientations of international firms may lead to advanced analytics orientation to attain sustained firm performance by generating and capturing value from the marketplace.Research limitations/implicationsThe study minimizes the literature gap by forming knowledge on big data-based strategic orientation and framing a multidimensional framework for guiding managers in the context of strategic orientation for international business and international marketing activities. The current study was conducted by following only a systematic literature review exclusively in firms' overall big data-based strategic orientation concept in international marketing. Future research may extend the domain by introducing firms' category wise systematic literature review.Originality/valueThe study has proposed a holistic conceptual framework for big data-driven strategic orientation in international marketing literature through a systematic review for the first time. It has also illuminated a future research agenda that raises questions for the scholars to develop or extend theory in this area or other related disciplines.

Superficial temporal-middle cerebral artery bypass

1979 ◽  
Vol 51 (4) ◽  
pp. 455-465 ◽  
Author(s):  
Richard E. Latchaw ◽  
James I. Ausman ◽  
Myoung C. Lee
Keyword(s):  
Middle Cerebral Artery ◽  
Cerebral Artery ◽  
Collateral Circulation ◽  
Bypass Surgery ◽  
Artery Bypass ◽  
Change Over Time ◽  
Content Type ◽  
Over Time ◽  
Fine Print ◽  
Temporal Middle

✓ Pre- and postoperative angiograms on 40 patients undergoing superficial temporal-middle cerebral artery (STA-MCA) bypass surgery have been examined in detail. Multiple postoperative angiograms have been obtained to evaluate the change in both the bypass circuit and the intracranial circulation over time. A reproducible system for evaluating the degree of intracranial vascular filling via the bypass is introduced. The study shows that the STA and its anastomotic branch increase in size over time, measured in months, in the majority of patients. This is paralleled by a progressive increase in the degree of intracranial vascular filling. These changes are proportional to the severity of the vascular disease before surgery. The pattern of preoperative collateral circulation may change over time following the addition of the bypass circuit. The progressive change over time suggests that a static analysis at one time may belie the true effect of the surgery. The change of collateral circulation, with augmentation of blood supply to areas of the brain other than those affected by the recent ischemic event, means that a total cerebral evaluation including neuropsychological testing may be necessary for adequate evaluation of the effect of the bypass surgery.

Data-driven marketing for growth and profitability

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Balakrishna Grandhi ◽  
Nitin Patwa ◽  
Kashaf Saleem
Keyword(s):  
Big Data ◽  
Online Survey ◽  
Shareholder Value ◽  
Business Environment ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Data Driven ◽  
Perceived Ease Of Use ◽  
Content Type ◽  
Customer Centricity

PurposeIn the current business environment, more uncertain than ever before, understanding consumer behavior is an integral part of an organization's strategic planning and execution process. It is the key driver for becoming a market leader. Therefore, it is important that all processes in business are customer centric. Marketers need to harness big data by engaging in data driven-marketing (DDM) to help organizations choose the “right” customers, to “keep” and “grow” them and to sustain “growth” and “profitability”. This research examines DDM adoption practices and how companies can aim to enhance shareholder value by bringing about “customer centricity”.Design/methodology/approachAn online survey conducted in 2016 received 180 responses from junior, middle and senior executives. Of the total responses, 26% were from senior management, 39% from middle management and the remaining 35% from junior management. Industries represented in the survey included retail, BFSI, healthcare and government, automobile, telecommunication, transport and logistics and IT. Other industries represented were aviation, marketing research and consulting, hospitality, advertising and media and human resource.FindingsSuccess of DDM depends upon how well an organization embraces the practice. The first and foremost indicator of an organization's commitment is the extent of resources invested for DDM. Respondents were divided into four categories; Laggards, Dabblers, Contenders and Leaders based on their “current level of investments” and “willingness to enhance investments” soon.Research limitations/implicationsWith storming digital age and the development of analytics, the process of decision-making has gained significant importance. Judgment and intuition too are critical to the process. Choosing an appropriate action cannot be done strictly on a rational basis.Practical implicationsThe results of the study offer interesting implications for managing the growing sea of data. An iterative and incremental approach is the need of the hour, even if it has to start with baby steps, to invest in and reap the fruits of DDM. The intention to use any system is always dependent on two primary belief factors: perceived usefulness and perceived ease of use; however, attitudes and social factors are equally important.Originality/valueThere is a dearth of knowledge with regards to who is and is not adopting DDM, and how best big data can be harnessed for enhancing effectiveness and efficiency of marketing budget. It is, therefore, imperative to build a knowledge base on DDM practices, challenges and opportunities. Better use of data can help companies enhance shareholder value by bringing about “customer centricity”.

scholarly journals Do stereotypes about older workers change? A panel study on changing attitudes of managers

2019 ◽  
Vol 41 (5) ◽  
pp. 535-550 ◽  
Author(s):  
Hendrik P. van Dalen ◽  
Kène Henkens
Keyword(s):  
Older Workers ◽  
Soft Skills ◽  
Panel Study ◽  
Change Over Time ◽  
Content Type ◽  
Hard Skills ◽  
Positive Assessment ◽  
Changing Attitudes ◽  
Over Time

Purpose The purpose of this paper is to see whether attitudes toward older workers by managers change over time and what might explain development over time. Design/methodology/approach A unique panel study of Dutch managers is used to track the development of their attitudes toward older workers over time (2010–2013) by focusing on a set of qualities of older workers aged 50 and older. A conditional change model is used to explain the variation in changes by focusing on characteristics of the manager (age, education, gender, tenure and contact with older workers) and of the firm (composition staff, type of work and sector, size). Findings Managers have significantly adjusted their views on the so-called “soft skills” of older workers, like reliability and loyalty. Attitudes toward “hard skills” – like physical stamina, new tech skills and willingness to train – have not changed. Important drivers behind these changes are the age of the manager – the older the manager, the more likely a positive change in attitude toward older workers can be observed – and the change in the quality of contact with older workers. A deterioration of the managers’ relationship with older workers tends to correspond with a decline in their assessment of soft and hard skills. Social implications Attitudes are not very susceptible to change but this study shows that a significant change can be expected simply from the fact that managers age: older managers tend to have a more positive assessment of the hard and soft skills of older workers than young managers. Originality/value This paper offers novel insights into the question whether stereotypes of managers change over time.

scholarly journals Go, girls! longitudinal data on gender differences in Internet use in Brazil

2021 ◽  
Vol 30 (1) ◽  
Author(s):  
Gilda Olinto ◽  
Sonoe Sugahara Pinheiro ◽  
Nadia Bernuci dos Santos
Keyword(s):  
Gender Differences ◽  
Longitudinal Data ◽  
Internet Use ◽  
Social Conditions ◽  
Change Over Time ◽  
Theoretical Approaches ◽  
Positive Results ◽  
Over Time

This article focuses on gender differences in Internet use in Brazil and how it is changing over time, considering its interplay with other environmental and social conditions. Initially, we consider evidence and theoretical approaches of women’s detachment from technology. We then look at data obtained from the 2005 and 2015 Brazilian Bureau of Census Annual Surveys. The results indicate that Internet use grew substantially in the country, but a large portion of the population is still segregated from it. The results also show that some social conditions for Internet use seem to have decreased their impact; however, in 2015 these factors still show a strong effect on the use of this technology. Insofar as gender is concerned, the analyses of its interplay with environmental and social conditions, and its change over time, bring about intriguing, albeit positive results: Women seemed to have transitioned from a slightly inferior to a somewhat better position relative to men.

scholarly journals A big-data analysis of political rhetoric relating the developments of the United States, China, and global powers

2020 ◽  
Vol 23 (3) ◽  
pp. 227-243
Author(s):  
Patrick Carter ◽  
Jeffrie Wang ◽  
Davis Chau
Keyword(s):  
United States ◽  
Big Data ◽  
Data Analysis ◽  
Political Rhetoric ◽  
The United States ◽  
Big Data Analysis ◽  
Content Type ◽  
World Power ◽  
The U.S ◽  
Over Time

PurposeThe similarities between the developments of the United States (U.S.) and China into global powers (countries with global economic, military, and political influence) can be analyzed through big data analysis from both countries. The purpose of this paper is to examine whether or not China is on the same path to becoming a world power like what the U.S. did one hundred years ago.Design/methodology/approachThe data of this study is drawn from political rhetoric and linguistic analysis by using “big data” technology to identify the most common words and political trends over time from speeches made by the U.S. and Chinese leaders from three periods, including 1905-1945 in U.S., 1977-2017 in U.S. and 1977-2017 in China.FindingsRhetoric relating to national identity was most common amongst Chinese and the U.S. leaders over time. The differences between the early-modern U.S. and the current U.S. showed the behavioral changes of countries as they become powerful. It is concluded that China is not a world power at this stage. Yet, it is currently on the path towards becoming one, and is already reflecting characteristics of present-day U.S., a current world power.Originality/valueThis paper presents a novel approach to analyze historical documents through big data text mining, a methodology scarcely used in historical studies. It highlights how China as of now is most likely in a transitionary stage of becoming a world power.

The dual drivetrain model of digital transformation: role of industrial big-data-based affordance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Liu ◽  
Wei Wang ◽  
Zuopeng (Justin) Zhang
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Data Driven ◽  
Content Type ◽  
Industrial Big Data ◽  
Technology Affordance ◽  
Effective Use ◽  
Practical Implications

PurposeTo better understand the role of industrial big data in promoting digital transformation, the authors propose a theoretical framework of industrial big-data-based affordance in the form of an illustrative metaphor – what the authors call the “organizational drivetrain.”Design/methodology/approachThis study investigates the effective use of industrial big data in the process of digital transformation based on the technology affordance–actualization theoretical lens. A software platform and services provider with more than 4,000 industrial enterprise clients in China was selected as the case study object for analyzing the digital affordance and actualization driven by industrial big data.FindingsDrawing on a revelatory case study, the authors identify three affordances of industrial big data in the organization, namely developing data-driven customized projects, provisioning equipment-data-driven life cycle services, establishing data-based trust and determining affordance actualization actions driven by technology and market. In addition, the authors reveal the underlying drivetrain mechanisms to advance industrial big data affordance and actualization: stabilizing, enriching and pioneering.Originality/valueThis study builds a drivetrain model on digital transformation by industrial big data affordance actualization. The authors also provide practical implications that can help practitioners to implement digital transformation effectively and extract value from their investment.

Understanding big data-driven supply chain and performance measures for customer satisfaction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramadas Thekkoote
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Customer Satisfaction ◽  
Performance Measures ◽  
Data Driven ◽  
Equation Modeling ◽  
Supply Chain Performance ◽  
Content Type ◽  
Different Dimensions ◽  
The Impact

PurposeSupply chain analytics with big data capability are now growing to the next frontier in transforming the supply chain. However, very few studies have identified its different dimensions and overall effects on supply chain performance measures and customer satisfaction. The aim of this paper to design the data-driven supply chain model to evaluate the impact on supply chain performance and customer satisfaction.Design/methodology/approachThis research uses the resource-based view, emerging literature on big data, supply chain performance measures and customer satisfaction theory to develop the big data-driven supply chain (BDDSC) model. The model tested using questionnaire data collected from supply chain managers and supply chain analysts. To prove the research model, the study uses the structural equation modeling technique.FindingsThe results of the study identify the supply chain performance measures (integration, innovation, flexibility, efficiency, quality and market performance) and customer satisfaction (cost, flexibility, quality and delivery) positively associated with the BDDSC model.Originality/valueThis paper fills the significant gap in the BDDSC on the different dimensions of supply chain performance measures and their impacts on customer satisfaction.

The marketing organization’s journey to become data-driven

2019 ◽  
Vol 13 (2) ◽  
pp. 162-178 ◽  
Author(s):  
Devon S. Johnson ◽  
Laurent Muzellec ◽  
Debika Sihi ◽  
Debra Zahay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Dynamic Learning ◽  
Marketing Practice ◽  
Content Type ◽  
Marketing Function ◽  
The Usa ◽  
Research Questions

Purpose This paper aims to improve understanding of data-driven marketing by examining the experiences of managers implementing big data analytics in the marketing function. Through a series of research questions, this exploratory study seeks to define what big data analytics means in marketing practice. It also seeks to uncover the challenges and identifiable stages of big data analytics implementation. Design/methodology/approach A total of 15 open-ended in-depth interviews were conducted with marketing and analytics executives in a variety of industries in Ireland and the USA. Interview transcripts were subjected to open coding and axial coding to address the research questions. Findings The study reveals that managers consider marketing big data analytics to be a series of tools and capabilities used to inform product innovation and marketing strategy-making processes and to defend the brand against emerging risks. Additionally, the study reveals that big data analytics implementation is championed at different organizational levels using different types of dynamic learning capabilities, contingent on the champion’s stature within the organization. Originality/value From the qualitative analysis, it is proposed that marketing departments undergo five stages of big data analytics implementation: sprouting, recognition, commitment, culture shift and data-driven marketing. Each stage identifies the key characteristics and potential pitfalls to be avoided and provides advice to marketing managers on how to implement big data analytics.

Sign in / Sign up

Export Citation Format

Share Document