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Journal articles on the topic 'Knowledge generation'

To see the other types of publications on this topic, follow the link: Knowledge generation.

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2023

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1

Popov, Evgeny, Maxim Vlasov, and Hanusch Horst. "Resource Potential of Knowledge Generation." MONTENEGRIN JOURNAL OF ECONOMICS 12, no. 3 (October 20, 2016): 101–14. http://dx.doi.org/10.14254/1800-5845.2016/12-3/7.

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2

van der Walt, Mariana. "Knowledge management and scientific knowledge generation." Knowledge Management Research & Practice 4, no. 4 (November 2006): 319–30. http://dx.doi.org/10.1057/palgrave.kmrp.8500115.

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3

Wang, Jian, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, and Min Yang. "Improving Knowledge-Aware Dialogue Generation via Knowledge Base Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9169–76. http://dx.doi.org/10.1609/aaai.v34i05.6453.

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Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. In addition, we propose a response guiding attention and a multi-step decoding strategy to steer our model to focus on relevant features for response generation. Experiments on two benchmark datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues. Our code is available at https://github.com/siat-nlp/TransDG.

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4

Panikarova, S. V., and M. V. Vlasov. "Knowledge Generation Risk Management." Economic Analysis: Theory and Practice 16, no. 9 (September 28, 2017): 1696–707. http://dx.doi.org/10.24891/ea.16.9.1696.

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5

Davies, John, Alistair Duke, Nick Kings, Dunja Mladenić, Kalina Bontcheva, Miha Grčar, Richard Benjamins, Jesus Contreras, Mercedes Blazquez Civico, and Tim Glover. "Next generation knowledge access." Journal of Knowledge Management 9, no. 5 (October 2005): 64–84. http://dx.doi.org/10.1108/13673270510622465.

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6

Dettmar, Harvey, Xiaohui Liu, Roger Johnson, and Alan Payne. "Knowledge-based data generation." Knowledge-Based Systems 11, no. 3-4 (November 1998): 167–77. http://dx.doi.org/10.1016/s0950-7051(98)00031-8.

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7

Schofield, M. J. "Knowledge-based test generation." IEE Proceedings G (Electronic Circuits and Systems) 132, no. 3 (1985): 108. http://dx.doi.org/10.1049/ip-g-1.1985.0023.

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8

Davies, J., R. Studer, Y. Sure, and P. W. Warren. "Next generation knowledge management." BT Technology Journal 23, no. 3 (July 2005): 175–90. http://dx.doi.org/10.1007/s10550-005-0040-3.

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9

van Deventer, Marko. "African Generation Y students’ personal finance behavior and knowledge." Investment Management and Financial Innovations 17, no. 4 (November 26, 2020): 136–44. http://dx.doi.org/10.21511/imfi.17(4).2020.13.

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Personal financial management is important, given uncertainties in both financial and economic environment. However, published research on African Generation Y students’ personal finance behavior and knowledge is limited. This study aimed to evaluate African Generation Y students’ personal finance behavior in terms of their attitudes towards financial planning and whether this cohort believes that they have the skills to manage their finances successfully. In addition, this study sought to evaluate African Generation Y students’ knowledge regarding personal finance. A convenience sample of 500 African students across the campuses of two South African public higher education institutions situated in the Gauteng province was surveyed using structured, self-administered questionnaires. The t-test results indicate that the sample deems the process of planning personal finances and managing credit, insurance, investment, and estate, as important. Moreover, the students scored low in the broad personal finance knowledge areas of basic finance, saving, spending, and debt, suggesting that this cohort is financially illiterate. The results also indicated that the students think they have the financial skillset to manage their personal finances. A high Pearson’s correlation coefficient was noted between sampled participants’ personal finance behavior and their observed personal finance management skillset regarding the relationship between the constructs. However, an insignificant relationship was found between attitudes towards personal finance and financial knowledge and between financial knowledge and African Generation Y students’ apparent finance skills. Understanding African Generation Y students’ personal finance behavior and knowledge, universities and financial institutions can more effectively identify gaps and deficiencies in students’ personal finance endeavors.

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10

B, Yeslando J. Gonzalez. "Investigate from the knowledge of the peoples." Sociology International Journal 2, no. 6 (December 27, 2018): 726–28. http://dx.doi.org/10.15406/sij.2018.02.00128.

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The following paper addresses from a critical point of view the way on how Venezuelan universities approach popular knowledge and how it would be transmitted to future generations. For the author institutions cannot become mere instruments generating ideologies or become Universities for the Generation of Communes and less in particularities ... "believe that you can replace a research university by a university in the commune ... "1 As our current policy in Venezuela intends, it is to try to apply policies that pretend to relegate us to the past.

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11

Vingilis, Evelyn, Kathleen Hartford, Ted Schrecker, Beth Mitchell, Barbara Lent, and Joan Bishop. "Integrating Knowledge Generation with Knowledge Diffusion and Utilization." Canadian Journal of Public Health 94, no. 6 (November 2003): 468–71. http://dx.doi.org/10.1007/bf03405087.

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12

Burger-Helmchen, Thierry. "Innovation Studies and Knowledge Generation." Journal of Innovation Economics & Management 30, no. 3 (2019): 245. http://dx.doi.org/10.3917/jie.030.0245.

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13

Bayer, Alan E., George M. Beal, Wimal Dissanayake, and Sumiye Konoshima. "Knowledge Generation, Exchange, and Utilization." Contemporary Sociology 16, no. 5 (September 1987): 713. http://dx.doi.org/10.2307/2069814.

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14

Freire, José Roberto de Souza, Isabel Cristina dos Santos, and Leandro Sauer. "Knowledge generation in agricultura research." Ciência Rural 46, no. 7 (July 2016): 1301–7. http://dx.doi.org/10.1590/0103-8478cr20150745.

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ABSTRACT: Over the past decades, Brazilian agriculture has played an important role in the international market, in response to growing global demand for products, services and food security. This achievement was in a large extent powered by the ability to generate knowledge and the actions promoted by science and technology institutes. This article aims to describe the model of knowledge generation in agriculture, assuming that the knowledge cycle is responsible for the capture, identification, selection and share of informal and formal information, through practices in the workplace and outside it, in personal and institutional networks. Based on a comprehensive literature review, this research deals with a multi-case study on three Brazilian science and technology institutes dedicated to agricultural research. Using both, qualitative and quantitative approaches, and collecting data through semi-structured interviews applied to seniors researchers, as well as questionnaires answered by 410 scientists holding master's and doctoral degrees in natural sciences. Results indicate the existence of a knowledge generation model in agriculture research focused innovation, whose process starts from capturing ideas on how to solve a problem using the technological competence developed, through formal research projects.

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15

Merrem, F. H. "Automatic generation of knowledge structures." ACM SIGART Bulletin, no. 108 (April 1989): 160–62. http://dx.doi.org/10.1145/63266.63295.

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16

Cornelissen, Joep P. "Metaphorical Reasoning and Knowledge Generation." Journal of Political Marketing 1, no. 1 (January 2002): 193–208. http://dx.doi.org/10.1300/j199v01n01_09.

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17

PIETRIKOVÁ, Emília, and Sergej CHODAREV. "Towards Programmer Knowledge Profile Generation." Acta Electrotechnica et Informatica 16, no. 1 (March 1, 2016): 15–19. http://dx.doi.org/10.15546/aeei-2016-0003.

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18

Jacobs, Paul S. "Knowledge-intensive natural language generation." Artificial Intelligence 33, no. 3 (November 1987): 325–78. http://dx.doi.org/10.1016/0004-3702(87)90042-7.

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19

Ermine, Jean-Louis. "Knowledge Crash and Knowledge Management." International Journal of Knowledge and Systems Science 1, no. 4 (October 2010): 79–95. http://dx.doi.org/10.4018/jkss.2010100105.

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Population ageing is a phenomenon that is quite new and irreversible in the history of mankind. Every country and every organisation is concerned while it is not certain that all the risks and challenges have been clearly identified. Clearly, there is a risk of massive knowledge loss, i.e., “Knowledge Crash”, due to massive retirements, but not exclusively for this reason. This risk is not evaluated at the right level, and in this regard, this article, by including the problem of “Knowledge Crash” in the more general framework of “Knowledge Management”, enlarges the concepts of knowledge, generation, and knowledge transfer. The author proposes a global approach, starting from a strategic analysis of a knowledge capital and ending in the implementation of socio-technical devices for inter-generational knowledge transfer.

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20

Cairó, Osvaldo, and Silvia Guardati. "The KAMET II methodology: Knowledge acquisition, knowledge modeling and knowledge generation." Expert Systems with Applications 39, no. 9 (July 2012): 8108–14. http://dx.doi.org/10.1016/j.eswa.2012.01.155.

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21

Davis, Santia M., Shahram Sarkani, and Thomas Mazzuchi. "A Systematic Approach to Identifying Engineering Generational Knowledge Transfer Boundaries." Journal of Information & Knowledge Management 11, no. 03 (September 2012): 1250015. http://dx.doi.org/10.1142/s0219649212500153.

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As organisations evaluate increasing retirement rates coupled with the rate of entering engineers, there is a need to address the presence of generational knowledge transfer boundaries. These unidentified boundaries impede the strategic transfer of engineering and architectural knowledge (STEAK) required for continued engineering growth. Using an interdisciplinary approach to define the engineering generational knowledge transfer boundaries provided useful knowledge sharing characteristics and procedures which all generations and disciplines can utilise. This research study provided a level of insight not previously explored and presented a model that can be used to evaluate how organisations will react to and leverage the knowledge across generations. Specifically, this study helps close the generational knowledge transfer boundary research gap with proper identification of the engineering generational knowledge transfer boundaries. This will allow management to propose strategies to attract and retain the next generation of knowledge workers. The article discusses these findings in detail and summarises the results in a proposed STEAK model and framework.

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22

Antonelli, Cristiano, and Alessandra Colombelli. "External and Internal Knowledge in the Knowledge Generation Function." Industry and Innovation 22, no. 4 (May 19, 2015): 273–98. http://dx.doi.org/10.1080/13662716.2015.1049864.

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23

WOODFIELD, PAUL J., and KENNETH HUSTED. "HOW DOES KNOWLEDGE SHARING ACROSS GENERATIONS IMPACT INNOVATION?" International Journal of Innovation Management 23, no. 08 (December 2019): 1940004. http://dx.doi.org/10.1142/s1363919619400048.

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We explore how knowledge sharing impacts innovation across generations of a family firm. We argue that each generation contributes to the knowledge pool differently, and there can be different levels of hostility towards sharing knowledge that can impact a family firm’s ability to innovate. We present two models distinguishing the source of knowledge from the receiver of knowledge for each generation. When the senior generation is the source of knowledge, business tends to be as per usual. Conversely, when the source of knowledge is the next generation, this can lead to new approaches to doing business being introduced, with potential for innovation activities and outcomes. We suggest that to minimise hoarding and rejection of knowledge, strategies need to be in place to avoid redundancy in the knowledge production and problem-solving processes.

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24

Sadovskaya, Ekaterina. "INTER- AND GENERATIONAL DISCOURSE AS THE CORNERSTONE OF THE EVERYDAY FROM THE PERSPECTIVE OF FICTION." CONTEMPORARY LITERARY STUDIES, no. 18 (December 13, 2021): 108–13. http://dx.doi.org/10.32589/2411-3883.18.2021.246987.

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Inter- and generational interaction takes place in everyday communication and serves as the basis of any society as it forms, regulates and maintains the existence of society by collecting, preserving and transferring knowledge and experience from generation to generation. The interaction of generations is practice based; it rationalizes the actions of individuals in society relying on common sense for the purpose of preserving the descendants and turning individual experience into objective knowledge for the following generations. This is done in everyday mundane interaction of generations. It is fiction that has accumulated this experience; it demonstrates not only the abundance of inter- and generational interaction but also its daily presence and vital character. The most vivid description of such generational or intergenerational interaction is seen in fictional works dealing with the relationships in the family, growing up of children and grandchildren and in novels of morals.

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25

Parapat, Ruth Gracia Dau Vanes, Vitria Melani, Yulia Wahyuni, Rachmanida Nuzrina, and Laras Sitoayu. "PERBEDAAN PENGETAHUAN GIZI, SIKAP DAN PERILAKU TENTANG VISUAL GIZI SEIMBANG ANTARA DUA GENERASI DI GEREJA GSKI REHOBOT KEBON JERUK JAKARTA." Journal of Nutrition College 10, no. 2 (June 30, 2021): 120–29. http://dx.doi.org/10.14710/jnc.v10i2.29140.

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Background: Generation Y (Millennial) and generation Z (IGeneration) are proven to be the two generations that dominate the use of information communication technology today. Living in a rapidly developing technology era and abundant information should make them have nutritional knowledge, attitudes and behavior towards a good visual balanced nutrition. However, Riskesdas 2018 shows an increase in the prevalence of obesity in adults by 21.8%. This high prevalence is said to be an indicator of low knowledge about balanced nutrition even though the two generations live in an era of abundant information.Objectives: Analyzing differences in nutritional knowledge, attitudes and behavior regarding visual balanced nutrition between generation Y and generation Z.Methods: This research is an observational analytic study with cross sectional design. Selection of research subjects with total sampling. A sample of 40 respondents was obtained from generation Y and 40 respondents from generation Z. Using the independent t-test statistical test to determine differences in nutritional knowledge, attitudes and behaviors regarding balanced nutrition visuals (balanced nutrition cone and my plate) in generation Y and generation Z.Results: There are differences in nutritional knowledge in generation Y and generation Z (p = 0.0001), there are differences in attitudes about visual balanced nutrition in generation and generation Z (p = 0.0001), and there are differences in nutritional behavior towards visual balanced nutrition of generation Y and generation Z (p = 0.029). From the above values, it can be said that the value of sampling bias on nutritional knowledge and attitudes is smaller than behavior.Conclusion: The three results show that there are differences in nutritional knowledge, attitudes and behavior between these two generations.

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26

Xu, Feifei, Xinpeng Wang, and Shanlin Zhou. "Story Generation Using Knowledge Graph under Psychological States." Wireless Communications and Mobile Computing 2021 (April 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/5530618.

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Story generation, aiming to generate a story that people could understand easily, captures increasing researchers’ attention in recent years. However, a good story usually requires interesting and emotional plots. Previous works only consider a specific or binary emotion like positive or negative. In our work, we propose a Knowledge-Aware Generation framework under Controllable CondItions (K-GuCCI). The model assigns a change line of psychological states to story characters, which makes the story develop following the setting. Besides, we incorporate the knowledge graph into the model to facilitate the coherence of the story. Moreover, we investigate a metric AGPS to evaluate the accuracy of generated stories’ psychological states. Experiments exhibit that the proposed model improves over standard benchmarks, while also generating stories reliable and valid.

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27

Li, Li, Haolin Yang, Yueming Jiao, and Kuo-Yi Lin. "Feature Generation Based on Knowledge Graph." IFAC-PapersOnLine 53, no. 5 (2020): 774–79. http://dx.doi.org/10.1016/j.ifacol.2021.04.172.

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28

Lloyd, Tom, Michele L. Shaffer, Christy Stetter, Mark D. Widome, John Repke, Michael R. Weitekamp, Paul J. Eslinger, Sandra S. Bargainnier, and Ian M. Paul. "Health knowledge among the millennial generation." Journal of Public Health Research 2, no. 1 (July 22, 2013): 8. http://dx.doi.org/10.4081/jphr.2013.e8.

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The Millennial Generation, also known as Generation Y, is the demographic cohort following Generation X, and is generally regarded to be composed of those individuals born between 1980 and 2000. They are the first to grow up in an environment where health-related information is widely available by internet, TV and other electronic media, yet we know very little about the scope of their health knowledge. This study was undertaken to quantify two domains of clinically relevant health knowledge: factual content and ability to solve health related questions (application) in nine clinically related medical areas. Study subjects correctly answered, on average, 75% of health application questions but only 54% of health content questions. Since students were better able to correctly answer questions dealing with applications compared to those on factual content contemporary US high school students may not use traditional hierarchical learning models in acquisition of their health knowledge.

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29

Kryukov, V. A., D. V. Milyaev, D. I. Dushenin, A. D. Savel’eva, and M. Yu Skuzovatov. "Knowledge Generation in Raw Material Industries." Studies on Russian Economic Development 33, no. 3 (June 2022): 257–66. http://dx.doi.org/10.1134/s1075700722030078.

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30

Li, Qintong, Piji Li, Zhaochun Ren, Pengjie Ren, and Zhumin Chen. "Knowledge Bridging for Empathetic Dialogue Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10993–1001. http://dx.doi.org/10.1609/aaai.v36i10.21347.

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Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. To address the above problems, we propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation. We first enrich the dialogue history by jointly interacting with external knowledge and construct an emotional context graph. Then we learn emotional context representations from the knowledge-enriched emotional context graph and distill emotional signals, which are the prerequisites to predicate emotions expressed in responses. Finally, to generate the empathetic response, we propose an emotional cross-attention mechanism to learn the emotional dependencies from the emotional context graph. Extensive experiments conducted on a benchmark dataset verify the effectiveness of the proposed method. In addition, we find the performance of our method can be further improved by integrating with a pre-trained model that works orthogonally.

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31

Sacha, Dominik, Andreas Stoffel, Florian Stoffel, Bum Chul Kwon, Geoffrey Ellis, and Daniel A. Keim. "Knowledge Generation Model for Visual Analytics." IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (December 31, 2014): 1604–13. http://dx.doi.org/10.1109/tvcg.2014.2346481.

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32

Les, Z., and M. Les. "Shape understanding: knowledge generation and learning." IEEE Transactions on Knowledge and Data Engineering 16, no. 3 (March 2004): 346–56. http://dx.doi.org/10.1109/tkde.2003.1262188.

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33

Spraggon, Martin, and Virginia Bodolica. "Collective tacit knowledge generation through play." Management Decision 55, no. 1 (February 13, 2017): 119–35. http://dx.doi.org/10.1108/md-05-2015-0173.

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Purpose The purpose of this paper is to contribute to the literature by examining the generation of collective tacit knowledge (CTK) in organizations through social ludic activities (SLAs) as a specific form of playful micro-practice carried out by employees. Design/methodology/approach This paper builds upon socially distributed cognition (SDC) and transactive memory systems (TMS) to analyze SLAs’ contribution to CTK creation in the workplace. These theories provide conceptual underpinnings for comprehending how workers self-organize shared activities to store, retrieve and use each other’s tacit knowledge within the collective. An example is provided to illustrate SLAs’ manifestation in an agile-based software development firm. Findings SLAs may facilitate collective members’ knowing and learning whereby different solutions are sought and potential tactics to cope with work issues are attuned to changing conditions by the collective members at work. Four moderating factors, namely physical proximity, psychological safety, richness of communication pathways and intensity of interactions, are identified and propositions to conceptualize their role in CTK generation through SLAs are formulated. Practical implications SLA players’ efforts to solve dysfunctionalities at work are not merely prompted by interdependencies among work-related tasks but also driven by high levels of social embeddedness and interaction among employees. Managers should become more supportive of collective playful activities in their organizations by building a propitious corporate climate for the mobilization of CTK in the workplace. Understanding SLAs as a soft group device where CTK resides, transits, is enacted and continuously metamorphosed represents an important complement to hard devices offered by information systems. Originality/value Relying on the notion of SLAs as a means to cope with work concerns, the authors integrate insights from organizational play, knowledge management, SDC and TMS literatures to advance the authors’ understanding of CTK creation through collective playful undertakings at work.

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34

Maunder, Colin, and Ben Bennetts. "HITEST—using knowledge in test generation." Computer-Aided Engineering Journal 2, no. 3 (1985): 75. http://dx.doi.org/10.1049/cae.1985.0016.

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35

Addis, T. R. "Knowledge for the New Generation Computers." Journal of Information Technology 1, no. 1 (February 1986): 38–45. http://dx.doi.org/10.1177/026839628600100107.

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36

TOWHIDNEJAD, M., H. R. MYLER, and A. J. GONZALEZ. "CONSTRAINT MECHANISMS IN AUTOMATED KNOWLEDGE GENERATION." Applied Artificial Intelligence 7, no. 2 (April 1993): 113–34. http://dx.doi.org/10.1080/08839519308949979.

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37

Addis, T. R. "Knowledge for the New Generation Computers." Journal of Information Technology 1, no. 1 (February 1986): 38–45. http://dx.doi.org/10.1057/jit.1986.7.

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38

Reiss, Thomas, and Sybille Hinze. "Autoimmune diseases - knowledge generation and utilisation." Expert Opinion on Therapeutic Patents 7, no. 9 (September 1997): 1005–13. http://dx.doi.org/10.1517/13543776.7.9.1005.

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39

van der Walt, Mariana, and Gideon de Wet. "A framework for scientific knowledge generation." Knowledge Management Research & Practice 6, no. 2 (June 2008): 141–54. http://dx.doi.org/10.1057/palgrave.kmrp.8500166.

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40

Arevalillo-Herráez, Miguel, Paloma Moreno-Clari, and Vicente Cerverón-Lleó. "Educational knowledge generation from administrative data." Educational Technology Research and Development 59, no. 4 (February 7, 2011): 511–27. http://dx.doi.org/10.1007/s11423-010-9185-y.

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41

Mikulakova, Eva, Markus König, Eike Tauscher, and Karl Beucke. "Knowledge-based schedule generation and evaluation." Advanced Engineering Informatics 24, no. 4 (November 2010): 389–403. http://dx.doi.org/10.1016/j.aei.2010.06.010.

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42

Hahn, Udo, and Ulrich Reimer. "Automatic generation of hypertext knowledge bases." ACM SIGOIS Bulletin 9, no. 2-3 (April 1988): 182–88. http://dx.doi.org/10.1145/966861.45429.

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43

Stange, Kurt C. "Refocusing Knowledge Generation, Application, and Education." American Journal of Preventive Medicine 41, no. 4 (October 2011): S164—S169. http://dx.doi.org/10.1016/j.amepre.2011.06.022.

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44

Tepandi, J. "KNOWLEDGE-BASED TEST GENERATION FROM SPECIFICATIONS." Proceedings of the Estonian Academy of Sciences. Engineering 3, no. 2 (1997): 127. http://dx.doi.org/10.3176/eng.1997.2.06.

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45

Records, Kathie. "Differentiating the Generation of Knowledge from the Application of Knowledge." Journal of Obstetric, Gynecologic & Neonatal Nursing 43, no. 4 (July 2014): 399–400. http://dx.doi.org/10.1111/1552-6909.12476.

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46

Lyons, Sean T., Linda Duxbury, and Christopher Higgins. "An Empirical Assessment of Generational Differences in Basic Human Values." Psychological Reports 101, no. 2 (October 2007): 339–52. http://dx.doi.org/10.2466/pr0.101.2.339-352.

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This study assessed generational differences in human values as measured by the Schwartz Value Survey. It was proposed that the two most recent generations, Millennials and Generation Xers, would value Sell-enhancement and Openness to Change more than the two older generations, Baby Boomers and Matures, while the two older generations would value Self-transcendence and Conservation more. The hypotheses were tested with a combined sample of Canadian knowledge workers and undergraduate business students ( N = 1,194). Two hypotheses were largely supported, although an unexpectedly large difference was observed between Millennials and Generation Xers with respect to Openness to Change and Self-enhancement. The findings suggest that generation is a useful variable in examining differences in social values.

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47

Bai, Jiaqi, Ze Yang, Xinnian Liang, Wei Wang, and Zhoujun Li. "Learning to Copy Coherent Knowledge for Response Generation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12535–43. http://dx.doi.org/10.1609/aaai.v35i14.17486.

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Knowledge-driven dialog has shown remarkable performance to alleviate the problem of generating uninformative responses in the dialog system. However, incorporating knowledge coherently and accurately into response generation is still far from being solved. Previous works dropped into the paradigm of non-goal-oriented knowledge-driven dialog, they are prone to ignore the effect of dialog goal, which has potential impacts on knowledge exploitation and response generation. To address this problem, this paper proposes a Goal-Oriented Knowledge Copy network, GOKC. Specifically, a goal-oriented knowledge discernment mechanism is designed to help the model discern the knowledge facts that are highly correlated to the dialog goal and the dialog context. Besides, a context manager is devised to copy facts not only from the discerned knowledge but also from the dialog goal and the dialog context, which allows the model to accurately restate the facts in the generated response. The empirical studies are conducted on two benchmarks of goal-oriented knowledge-driven dialog generation. The results show that our model can significantly outperform several state-of-the-art models in terms of both automatic evaluation and human judgments.

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48

Hernaus, Tomislav, and Nina Pološki Vokic. "Work design for different generational cohorts." Journal of Organizational Change Management 27, no. 4 (July 8, 2014): 615–41. http://dx.doi.org/10.1108/jocm-05-2014-0104.

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Purpose – The purpose of this paper is to uncover the nature of job characteristics related to different generational cohorts (Baby-boomers, Generation X and Generation Y). Significant differences between four task and four social job characteristics across generational cohorts have been revealed. Design/methodology/approach – The empirical research was conducted through a field study of employees from large-sized Croatian organizations. A cross-sectional and cross-occupational research design was applied. A total of 512 knowledge workers (139 managers and 373 professionals) participated in the research. Descriptive and inferential statistical methods were used to determine and compare work design across generations. Findings – The results indicate that job characteristics are not equally represented within different generational cohorts. While the nature of task job characteristics is mostly irrespective of generations, social job characteristics to some extent differ among generational cohorts. High task variety, reasonably high task identity, and a moderate level of both received interdependence and task significance are recognized as common job characteristics of knowledge workers across generations. However, jobs of Baby-boomers, Xers, and Yers are idiosyncratic for work autonomy, interaction with others, initiated interdependence, and teamwork. Additionally, the inclusion of the work type as a control variable revealed that interaction with others does differ but only among generations of professionals. Originality/value – The present study is the first research in which generational similarities and differences have been empirically examined through job characteristics. The authors focused on knowledge workers within an under-researched context (studies about knowledge workers, work design and generational differences are rare or non-existent in south-eastern European countries), making this systematic investigation unique and practically significant.

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Saputra, Teddy. "THE FIRST GENERATION'S TACIT KNOWLEDGE TRANSFER." Jurnal Entrepreneur dan Entrepreneurship 7, no. 2 (September 24, 2018): 95–104. http://dx.doi.org/10.37715/jee.v7i2.1109.

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This study aims to see and learn when, how and what are the transfer of tacit knowledge between the senior generation and the next generation of first generation family companies in Surabaya. This is qualitative research. The study found that the transfer of tacit knowledge was planned and initiated by the senior generation. Transfers are carried out by observation, experiencing and reflection methods where unique senior generation knowledge such as trust in certain value values is transferred to the next generation which is then studied by the next generation to become a separate value.

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Taylor, Melissa Kempf. "Xennials: a microgeneration in the workplace." Industrial and Commercial Training 50, no. 3 (March 5, 2018): 136–47. http://dx.doi.org/10.1108/ict-08-2017-0065.

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Purpose The purpose of this paper is to investigate the microgeneration between Generation X and the Millennial generation. The research question addressed was “What makes the Xennial generation unique and how might the knowledge of these differences be beneficial to organizations?” Design/methodology/approach The research question was analyzed by a review of literature through the lens of generational theory. This study reviewed current literature on generational theory, years encompassing specific generations, and differences between recent generations in the workplace. Findings It was discovered the exact years distinguishing generations were unclear because generations fade into one another without a distinct starting and stopping point. This overlap creates a cusp generation or microgeneration. The presented findings suggest microgeneration employees could aid in reducing workplace generational tensions. Practical implications Organizations can benefit from creating flexible workplaces accommodating the desires of multi-generational employees while still meeting the goals of the organization as a whole. It is important managers approach generational differences with a clear perspective of what information is valid and what may simply be popular. It is crucial to remember employees are individuals who never completely fit a stereotype, generational, or otherwise. Originality/value Very few academic articles on generational differences mention cusp generations and none address their unique opportunities for organizations in the workplace. Members of microgenerations between major generational cohorts may be a key to reducing workplace friction between employees of different generations.

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