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    <title>NOPR Collection:</title>
    <link>http://nopr.niscpr.res.in/handle/123456789/26163</link>
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        <rdf:li rdf:resource="http://nopr.niscpr.res.in/handle/123456789/26614" />
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    <dc:date>2026-10-10T20:39:42Z</dc:date>
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  <item rdf:about="http://nopr.niscpr.res.in/handle/123456789/26614">
    <title>Attitude to Collaboration With Industry: A Latent Class Typology of Academic Scientists in India</title>
    <link>http://nopr.niscpr.res.in/handle/123456789/26614</link>
    <description>Title: Attitude to Collaboration With Industry: A Latent Class Typology of Academic Scientists in India
Authors: Nagpaul, P S; Roy, Santanu
Abstract: There is a dearth of empirical studies on&#xD;
identifying barriers to initiating collaboration between the academia and the corporate&#xD;
world. This paper attempts to map the attitude of academic scientists towards cooperation&#xD;
with industry in the context of status of the academic scientist, field of specialization,&#xD;
and the type of institution where employed. The data were collected through a sample&#xD;
survey in which about 1100 scientists in twenty universities in India had participated.&#xD;
The population of a university was classified into four categories - periphery,&#xD;
center, semicentre, and semiperiphery. Attitude to collaboration was tapped through&#xD;
six items - mission incompatibility, lack of challenge, constraint on academic freedom,&#xD;
lack of response, cultural incompatibility, and ethical incompatibility and measured&#xD;
on a 5-point Likert scale (1-strongly disagree to 5-strongly agree). Latent class&#xD;
analysis was used as a methodology of typological analysis for classifying the respondents&#xD;
into clusters based on similarities of their attitude to collaboration with&#xD;
industry. The distribution of contextual variables in the derived clusters was&#xD;
subsequently probed. The structure of multivariate relationships between the classification&#xD;
categories and the categories of the contextual variables was analyzed through&#xD;
multiple correspondence analysis to provide a synoptic view of the global structure&#xD;
of the data. The fine-grained structure of the relationships of typology categories&#xD;
and the categories of contextual variables was analyzed through a series of simple&#xD;
correspondence analysis. About 33 per cent of respondents constituted a group characterized&#xD;
by a positive orientation towards collaboration with industry and they came from&#xD;
applied science area. Only 13 per cent constituted a group characterized by the&#xD;
ivory tower attitude towards collaboration with industry while a large number of&#xD;
respondents (53 per cent) formed a group which appeared to be neutral to the collaboration&#xD;
with industry. The results have implications for the management of collaborative&#xD;
projects among the universities and industries.
Page(s): 753-764</description>
    <dc:date>2000-08-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="http://nopr.niscpr.res.in/handle/123456789/26613">
    <title>Managing Customer Relationships in the e-Business Economy</title>
    <link>http://nopr.niscpr.res.in/handle/123456789/26613</link>
    <description>Title: Managing Customer Relationships in the e-Business Economy
Authors: Chatterjee, Jayanta
Abstract: Old business practices built around&#xD;
traditional product marketing and sales are being rapidly modified or replaced by&#xD;
customer centric relationship marketing in which share of customer is the economic&#xD;
driver. Technology has reached a state where quantum leaps can&#xD;
be made toward the ultimate competitive form of relationship building, one- to -one&#xD;
marketing. In this new paradigm, customer data is both a strategic asset and&#xD;
critical success factor. Relationship marketing in its best competitive expression,&#xD;
one-to-one marketing, rests on three very simple ideas about reducing costs and&#xD;
increasing long-term profitability: customer loyalty, scope and efficiency. &lt;b&gt;ECRM&lt;/b&gt;,&#xD;
i.e., Customer Relationship Management at the convergence of e-business is a&#xD;
discipline as well as a set of discrete software and technologies which focuses&#xD;
on automating and improving the business processes associated with managing&#xD;
customer relationships in the areas of sales, marketing , customer service, and&#xD;
support, &lt;b style="mso-bidi-font-weight:normal"&gt;eCRM&lt;/b&gt; software applications not&#xD;
only facilitate the coordination of multiple business functions (sales, marketing,&#xD;
customer service, and support ) but also coordinate multiple channels of&#xD;
communication with the customer —face to face, call center and&#xD;
the web—so that organisation, can accommodate their customers’ preferred channels&#xD;
of interaction.
Page(s): 749-752</description>
    <dc:date>2000-08-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="http://nopr.niscpr.res.in/handle/123456789/26612">
    <title>Knowledge Management in A Consulting Engineering Company in the Process Industry</title>
    <link>http://nopr.niscpr.res.in/handle/123456789/26612</link>
    <description>Title: Knowledge Management in A Consulting Engineering Company in the Process Industry
Authors: Datta, Amalendu
Abstract: In the competitive market of process industry,&#xD;
the process contractors face difficulty in knowledge management with respect to&#xD;
knowledge sharing and continuous updating of knowledge base. A balanced&#xD;
approach to knowledge sharing as a part of marketing strategy appears to be the&#xD;
best. Each contractor has its own way of development, maintenance, use and improvement&#xD;
of knowledge base. The strong potential of information networking in sourcing&#xD;
the necessary knowledge base throughout the world for bidding and execution of projects&#xD;
has enhanced its competitive edge. Most contractors have their own design standards,&#xD;
engineering manual and procedures that are constantly up-dated. The complete&#xD;
knowledge base of process design and engineering covering first principle to&#xD;
black box category are now simulated through mathematical models and high level&#xD;
control strategies are used to exploit the optimal performance of the plant. These&#xD;
technical challenges are met through rigorous models and sophisticated computer&#xD;
software. Security, maintenance, uses and upgradation of software is another&#xD;
area of knowledge management. Multidisciplinary contributions are necessary for&#xD;
the development and execution of total technology solution along with good teamwork.&#xD;
Hence, in addition to knowledge management, personal management also plays an&#xD;
equally critical role in all these projects.
Page(s): 741-748</description>
    <dc:date>2000-08-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="http://nopr.niscpr.res.in/handle/123456789/26611">
    <title>Knowledge Management — Converting Expertise Into Processes and Systems: Lessons from the Mature Industry</title>
    <link>http://nopr.niscpr.res.in/handle/123456789/26611</link>
    <description>Title: Knowledge Management — Converting Expertise Into Processes and Systems: Lessons from the Mature Industry
Authors: Phatak, Ajay
Abstract: Knowledge, on the face of it is an intangible&#xD;
asset for the organization. The need of managing knowledge is paramount in the software&#xD;
industry where ad-hocism is still at its highest. Software engineering attempts&#xD;
to systematize at least some amount of expertise (an intangible) into a process&#xD;
(which is more tangible). Practical experience suggests that dependence on people&#xD;
is a&#xD;
norm rather than exception. However, with systematic efforts of documenting the&#xD;
process and the expertise, it is many a time possible to reduce this dependence&#xD;
substantially. Process, then becomes an asset, which can produce products of substantial&#xD;
value. Lessons can be learnt from process evolution and process maturing of the&#xD;
conventional engineering industry. Rigor of process implementation has two sides&#xD;
as usual. These are acceptability of the specified rigor for process implementation&#xD;
and predictability of the outcome of defined processes. More the rigor, lesser is&#xD;
likelihood of acceptability by the people. This refinement of process of production&#xD;
than the product itself has created the ability to perform the same task at a much&#xD;
lower skill and knowledge level. The mechanism to institutionalize knowledge should&#xD;
not depend on the ‘kind’ of knowledge but should be more general purpose than that.&#xD;
Converting knowledge into easy to follow process is the proposed answer to the knowledge&#xD;
management.
Page(s): 738-740</description>
    <dc:date>2000-08-01T00:00:00Z</dc:date>
  </item>
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