Are There Differences between Consumers’ and Marketers’ Privacy Expectations? A Segment- and Technology-Level Analysis

2010 ◽  
Vol 29 (1) ◽  
pp. 138-149 ◽  
Author(s):  
George R. Milne ◽  
Shalini Bahl
Keyword(s):  
Technology Level ◽  
Level Analysis

Energy Efficiency Potential for China’s Cement Industry: A Bottom-Up Technology-Level Analysis

2012 ◽  
pp. 91-96
Author(s):  
Ali Hasanbeigi ◽  
William Morrow ◽  
David Fridley ◽  
Eric Masanet ◽  
Tengfang Xu ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Cement Industry ◽  
Bottom Up ◽  
Energy Efficiency Potential ◽  
Technology Level ◽  
Level Analysis

scholarly journals Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach

2019 ◽  
Vol 11 (22) ◽  
pp. 6381
Author(s):  
Xuan Shi ◽  
Lingfei Cai ◽  
Hongfang Song
Keyword(s):  
Fuel Cell ◽  
Latent Dirichlet Allocation ◽  
Fuel Cell Vehicle ◽  
Target Firm ◽  
New Energy ◽  
Multi Level ◽  
Fierce Competition ◽  
Technology Level ◽  
Patent Portfolio ◽  
Level Analysis

Technology opportunity discovery (TOD) is an important technique to help fuel cell vehicle (FCV) firms keep market advantage and sustainable development. Under fierce competition in the new energy industry, there is an urgent necessity for innovative TOD methods to effectively identify technology opportunities for FCV firms. This study proposes a structured TOD framework with a multi-level identification process. Based on technology portfolio analysis, it fully integrates the firm’s technology level analysis, technology potential analysis and patent novelty analysis. A series of techniques such as LDA (latent Dirichlet allocation), MDS (multidimensional scaling) and LOF (local outlier factor) are also applied in the framework. A total of 14,858 granted patent data of the FCV industry containing 798 patents of the target firm were extracted from the Derwent Innovation Index database as the input data of the empirical study. The result shows that the framework can provide a more profound analysis for identifying technology opportunities, which offer more appropriate insights in both strategic and operational level technological decisions for technology-oriented firms.

İstanbul İlçelerinin Nüfus Değişimi ve Kütük Bilgilerinin Analizi

2019 ◽  
Vol 6 (1) ◽  
pp. 37-72
Author(s):  
Ulaş Sunata ◽  
Dila Ergül
Keyword(s):  
Population Growth ◽  
Population Growth Rate ◽  
Population Change ◽  
Urban Migration ◽  
Specific Population ◽  
Related Factors ◽  
Internal Migrant ◽  
Level Analysis ◽  
Study District ◽  
Demographic Features

39 ilçesiyle Türkiye’nin en büyük nüfusuna sahip ili İstanbul aynı zamanda Türkiye’nin en çok iç göç alan şehridir. Özellikle kırdan kente göç bağlamında sosyo-ekonomik ve demografik özellikleriyle birçok araştırmaya konu olmuştur. Fakat İstanbul yerleşik nüfusunun Türkiye’nin diğer şehirlerine kayıtlı olma yoğunluğu da önemlidir. Bu çalışmanın amacı 2012 ve 2017 yıllarındaki nüfus değişimini göz önünde bulundurarak İstanbul ilçelerinin ayrıntılı nüfus yoğunluğu ve büyüme analizini yapmak, ilgili faktörleri değerlendirmek, hemşehri ağlarını okumak adına yerleşik nüfus kütük bilgileri bakımından inceleyerek elde edilen örüntüler doğrultusunda ilçe tipolojileri oluşturmaktır. Çalışmanın birinci bölümünde ilgili beş yıllık nüfus değişimlerine göre İstanbul ilçe nüfusları analiz edilmiştir. Ardından her bir ilçe için nüfusa kayıtlı olunan kente göre nüfus büyüme hızlarına bakılarak ilçelerin ağırlıklı olarak barındırdığı hemşehri ağları belirlenmiştir. İkinci bölümde ise ilçeler nüfus değişim özelliklerine göre belirli kategorilere ayrılmış ve bu kategoriler doğrultusunda ilçe tipolojileri oluşturulmuştur..ABSTRACT IN ENGLISHA District Level Analysis of Istanbul’s Population Change (2012-2017)Istanbul having the largest population of Turkey with its 39 districts is the most internal-migrant-receiving city in Turkey. Particularly in the context of rural-to-urban migration, Istanbul has been became a subject of various researches with its socio-economic and demographic features. However, the density of Istanbul’s settled population who registered other cities of Turkey is important. The main aim of this study is to analyse population growth of all districts considering the population change between 2012 and 2017, to evaluate the related factors and to develop a district typology by using the data of settled population according to their family registration in the name of reading the current countryman networks. In the first section of the study, district populations of Istanbul are examined regarding the related five-year change. Afterwards, most repeated countryman networks of all Istanbul’s districts are specified regarding the population growth rate of the registered cities. In the latter section of the study, districts were divided into categories regarding the specific population change features which help to create district typology.

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