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Journal articles on the topic 'Productivity measures'

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Published: 11 June 2021
Last updated: 1 February 2022

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1

Chambers, Robert G., and Rulon D. Pope. "Aggregate Productivity Measures." American Journal of Agricultural Economics 78, no. 5 (December 1996): 1360–65. http://dx.doi.org/10.2307/1243522.

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2

Boyle, Ed. "Measures of productivity." Nature 362, no. 6415 (March 1993): 21–22. http://dx.doi.org/10.1038/362021a0.

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3

Jurison, Jaak. "REEVALUATING PRODUCTIVITY MEASURES." Information Systems Management 14, no. 1 (January 1997): 30–34. http://dx.doi.org/10.1080/10580539708907027.

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4

THALMAN, J., and R. FORD. "Labor and productivity measures." Respiratory Care Clinics 10, no. 2 (June 2004): 211–21. http://dx.doi.org/10.1016/j.rcc.2004.01.005.

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5

Mukundan, Govind, and David Seidenwurm. "Performance Measures, Efficiency, Productivity." Neuroimaging Clinics of North America 22, no. 3 (August 2012): 451–56. http://dx.doi.org/10.1016/j.nic.2012.05.009.

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6

Barkin, Solomon. "Productivity Measures in Collective Bargaining." Relations industrielles 36, no. 2 (April 12, 2005): 361–70. http://dx.doi.org/10.7202/029155ar.

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7

Marks, Catherine. "Market sector GVA productivity measures." Economic & Labour Market Review 1, no. 3 (March 2007): 47–53. http://dx.doi.org/10.1057/palgrave.elmr.1410044.

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8

Camus, Dawn. "Publishing productivity measures in ONS." Economic & Labour Market Review 1, no. 7 (July 20, 2007): 19–21. http://dx.doi.org/10.1057/palgrave.elmr.1410103.

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9

Scudder, Richard A., and A. Ronald Kucic. "Productivity measures for information systems." Information & Management 20, no. 5 (May 1991): 343–54. http://dx.doi.org/10.1016/0378-7206(91)90033-x.

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10

Vergés-Jaime, Joaquim. "The Misinterpretation of Productivity Measures." Challenge 64, no. 2 (January 11, 2021): 156–71. http://dx.doi.org/10.1080/05775132.2020.1866907.

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11

Burtseva, T. A. "Measures of Regional Labour Productivity." Voprosy statistiki 28, no. 1 (February 19, 2021): 18–27. http://dx.doi.org/10.34023/2313-6383-2021-28-1-18-27.

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In this article, in the form of publication, are presented key study findings that cover both methodological issues of measurement and comprehensive analysis of regional labor productivity, along with extended formulations of study results. Based on the experience of domestic and foreign statisticians, the author substantiates the system of regional labour productivity indicators required for its integrated assessment. In the author’s opinion, this system is needed in a formation of a single coordinated approach to the calculation of labour productivity from the perspective of branches, enterprises, and constituent entities of the Russian Federation.The introduction argues for the relevance of the issues related to measuring regional labour productivity, which is necessary to evaluate the progress of the Federal Project “Systemic Measures to Improve Labor Productivity” implemented under the national project “Labor Productivity and Employment Support”.The author justifies the solution to the problem of clustering regions by regional labour productivity indicator using a combined approach, which explains the trends in the economic development of the regions of Russia in the selected clusters.The final part of the article concludes that the study is relevant in assessing the factors of regional labour productivity needed to identify growth reserves or to prevent stagnation in economic development, measured, in particular, by means of a basic indicator of the economic production efficiency in constituent entities of the Russian Federation.
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12

Bartholomew, Patricia. "Beyond Research Productivity: Matching Productivity Measures to Institutional Mission." Journal for the Study of Postsecondary and Tertiary Education 2 (2017): 001–11. http://dx.doi.org/10.28945/3559.

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Aim/Purpose: The aim of this paper is to develop a unified methodology inclusive of the three primary areas of faculty responsibility (teaching, research, and service) to calculate departmental productivity that fills the gap in methodological bench-marking tools for overall faculty productivity. Background: A disproportionate number of departmental and faculty productivity indices in higher education rely solely on research. Productivity in other areas of faculty workload areas, like teaching and institutional and community service, are either measured separately or ignored all together – even when those activities are institutionally mandated. This does a disservice to those who work in those institutions and skews incentives. Methodology: This paper utilizes a unified methodology inclusive of the three primary areas of faculty responsibility (teaching, research, and service) to calculate depart-mental productivity in five disparate departments (English, Biology, Mathematics, Sociology, and Computer Science) common to two universities with differing missions (teaching and service). Findings: The results reveal the bias inherent in relying solely on research as a proxy for overall productivity in institutions that have differing missions. Recommendations for Practitioners: Utilizing better metrics informs higher education administrators, promotes better decision-making, and allows incentives to re-align with desired outcomes. Recommendation for Researchers: This paper recommends combing all aspects of faculty workload into a single benchmark index to better measure departmental productivity. Future Research: Further research into improving this simple index is warranted and would include how to account for quality and other facets of productivity.
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13

Schwartz, Steven M., and John Riedel. "Productivity and Health: Best Practices for Better Measures of Productivity." Journal of Occupational and Environmental Medicine 52, no. 9 (September 2010): 865–71. http://dx.doi.org/10.1097/jom.0b013e3181ed8686.

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14

BEATON, DORCAS, CLAIRE BOMBARDIER, REUBEN ESCORPIZO, WEI ZHANG, DIANE LACAILLE, ANNELIES BOONEN, RICHARD H. OSBORNE, ASLAM H. ANIS, C. VIBEKE STRAND, and PETER S. TUGWELL. "Measuring Worker Productivity: Frameworks and Measures." Journal of Rheumatology 36, no. 9 (September 2009): 2100–2109. http://dx.doi.org/10.3899/jrheum.090366.

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Worker productivity is a combination of time off work (absenteeism) due to an illness and time at work but with reduced levels of productivity while at work (also known as presenteeism). Both can be gathered with a focus on application as a cost indicator and/or as an outcome state for intervention studies. We review the OMERACT worker productivity groups’ progress in evaluating measures of worker productivity for use in arthritis using the OMERACT filter. Attendees at OMERACT 9 strongly endorsed the importance of work as an outcome in arthritis. Consensus was reached (94% endorsement) for fielding a broader array of indicators of absenteeism. Twenty-one measures of at-work productivity loss, ranging from single item indicators to multidimensional scales, were reviewed for measurement properties. No set of at-work productivity measures was endorsed because of variability in the concepts captured, and the need for a better framework for the measurement of worker productivity that also incorporates contextual issues such as job demands and other paid and unpaid life responsibilities. Progress has been made in this area, revealing an ambivalent set of results that directed us back to the need to further define and then contextualize the measurement of worker productivity.
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15

Rushdi, Ali Ahmed. "Total factor productivity measures for Telstra." Telecommunications Policy 24, no. 2 (March 2000): 143–54. http://dx.doi.org/10.1016/s0308-5961(99)00078-6.

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16

Bröchner, Jan, and Thomas Olofsson. "Construction Productivity Measures for Innovation Projects." Journal of Construction Engineering and Management 138, no. 5 (May 2012): 670–77. http://dx.doi.org/10.1061/(asce)co.1943-7862.0000481.

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17

Middaugh, Michael F., and David E. Hollowell. "Examining academic and administrative productivity measures." New Directions for Institutional Research 1992, no. 75 (1992): 61–75. http://dx.doi.org/10.1002/ir.37019927507.

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18

Färe, R. "Comments on “Incentives and productivity measures”." International Journal of Production Economics 39, no. 1-2 (April 1995): 80. http://dx.doi.org/10.1016/0925-5273(95)94409-w.

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19

N. Seshadri, N. Seshadri, Dr D. Pradeep Kumar, and Dr T. Narayana Reddy. "A Study on Efficiency and Productivity of Commercial Banks Using Accounting Measures." Indian Journal of Applied Research 4, no. 1 (October 1, 2011): 335–39. http://dx.doi.org/10.15373/2249555x/jan2014/98.

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20

Mittal, Sanjiv, and Sujata Khandai. "Measuring Marketing Productivity: Marketers' Imperative." Management and Labour Studies 34, no. 2 (May 2009): 227–48. http://dx.doi.org/10.1177/0258042x0903400204.

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The 21st century has thrown up a lot of challenges for the present day marketers. Today, marketers are faced with an imperative to make marketing accountable. This is where it becomes important to assess the productivity of marketing. Measuring the productivity of marketing is tantamount to assessing the performance of marketing which necessarily impacts upon the overall organizational performance. Marketing productivity is measured as the ratio of marketing input over output. Broadly, two measures may be used to measure the productivity of marketing. These two measures include (a) Return on Marketing Investment (ROMI), and (b) Customer Satisfaction; the latter measured in terms of increase in shareholder value. This article attempts to understand the various issues connected with measuring the productivity of marketing. It aims to identify measures which would be effective in the measurement of productivity of marketing.
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21

Long, J. Scott. "Measures of Sex Differences in Scientific Productivity." Social Forces 71, no. 1 (September 1992): 159. http://dx.doi.org/10.2307/2579971.

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22

Dhuyvetter, Kevin C., and J. L. Parcell. "Trends in the swine industry: productivity measures." Kansas Agricultural Experiment Station Research Reports, no. 10 (January 1, 1997): 14–18. http://dx.doi.org/10.4148/2378-5977.6540.

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23

Worrell, Ernst, John A. Laitner, Michael Ruth, and Hodayah Finman. "Productivity benefits of industrial energy efficiency measures." Energy 28, no. 11 (September 2003): 1081–98. http://dx.doi.org/10.1016/s0360-5442(03)00091-4.

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24

Jääskeläinen, Aki, and Antti Lönnqvist. "Designing operative productivity measures in public services." VINE 39, no. 1 (April 10, 2009): 55–67. http://dx.doi.org/10.1108/03055720910962443.

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25

Huettner, David A., and William Clark. "Comparative Research Productivity Measures for Economics Departments." Journal of Economic Education 28, no. 3 (January 1997): 272–78. http://dx.doi.org/10.1080/00220489709596749.

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26

Eilon, Samuel. "A Framework for Profitability and Productivity Measures." Interfaces 15, no. 3 (June 1985): 31–40. http://dx.doi.org/10.1287/inte.15.3.31.

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27

Tilstra, Janet, and Kristen McMaster. "Productivity, Fluency, and Grammaticality Measures From Narratives." Communication Disorders Quarterly 29, no. 1 (November 2007): 43–53. http://dx.doi.org/10.1177/1525740108314866.

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The purpose of this study was to identify potential general outcome indicators (GOIs) of language proficiency. Brief narratives were elicited from 45 kindergarten, first-grade, and third-grade children using single-picture scenes and a standardized protocol. Measures of language productivity, verbal fluency, and grammaticality were examined for alternate-form reliability and criterion validity. Two verbal fluency measures, total productive words per minute and total number of words per minute, were reliably obtained for students in all grades, demonstrated moderate criterion validity with a standardized oral language measure, and differentiated third-grade students from those in kindergarten and first grade. Additional standards for GOIs are discussed.
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28

Huettner, David A., and William Clark. "Comparative Research Productivity Measures for Economics Departments." Journal of Economic Education 28, no. 3 (1997): 272. http://dx.doi.org/10.2307/1183203.

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29

Briskin, Lawrence E. "Productivity and competitivity: Measures of industrial efficiency." National Productivity Review 6, no. 2 (1987): 177–79. http://dx.doi.org/10.1002/npr.4040060210.

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30

Moody, Roseanne C. "Nurse Productivity Measures for the 21st Century." Health Care Management Review 29, no. 2 (April 2004): 98–106. http://dx.doi.org/10.1097/00004010-200404000-00003.

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31

Kitchenham, B., and E. Mendes. "Software productivity measurement using multiple size measures." IEEE Transactions on Software Engineering 30, no. 12 (December 2004): 1023–35. http://dx.doi.org/10.1109/tse.2004.104.

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32

Long, J. S. "Measures of Sex Differences in Scientific Productivity." Social Forces 71, no. 1 (September 1, 1992): 159–78. http://dx.doi.org/10.1093/sf/71.1.159.

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33

Salminen, Simo, and Jorma Saari. "Measures to improve safety and productivity simultaneously." International Journal of Industrial Ergonomics 15, no. 4 (April 1995): 261–69. http://dx.doi.org/10.1016/0169-8141(94)00042-2.

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34

Shiraishi, Y. "Measures to Improve Productivity in Concrete Products." Concrete Journal 58, no. 6 (2020): 433. http://dx.doi.org/10.3151/coj.58.6_433.

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35

Gokhan Kocer, Ruya. "Can productivity be socially embedded? Reflections on some productivity measures of 2000s." International Journal of Productivity and Performance Management 63, no. 3 (April 8, 2014): 354–69. http://dx.doi.org/10.1108/ijppm-04-2013-0063.

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Purpose – The purpose of this paper is to give an overview of items that are included in recent productivity programmes in various countries and integrate these items with the ideas of virtuous circuit of productivity and socially embedded productivity. Design/methodology/approach – In depth analysis of an illustrative set of productivity measures. The analysis reveals how each measure/programme could make sense to the industrial relations actors. A new conceptual reformulation of analysed measures and programmes. Findings – Bilateral productivity deals between trade unions and employers are not necessarily good from the perspective of national policy making. For this kind of uncoordinated productivity measures, though may be good for individual enterprises, may increase unemployment by pushing other domestic competitors out of the market or compelling them to lay-off workers in a reactive fashion. By drawing on Polanyi, one may argue that it is possible to strike a good balance between productivity concerns and societal interests by embedding the former into the latter. Research limitations/implications – The purpose is to introduce a new conceptualization, thus the empirical material is meant to be illustrative rather than extensive. Originality/value – By using graphical displays and empirical argumentation two new concepts are introduced: first, virtuous circuit of productivity and second, socially embedded productivity.
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36

Scerri, Moira, and Renu Agarwal. "Service enterprise productivity in action: measuring service productivity." Journal of Service Theory and Practice 28, no. 4 (July 9, 2018): 524–51. http://dx.doi.org/10.1108/jstp-06-2017-0104.

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Purpose The purpose of this paper is to measure service productivity using the Service Enterprise Productivity in Action (SEPIA) model. The research operationalises only one of the five stakeholder groups, the customer interface which incorporates service complexity (SC), customer interactions, customer channel, customer loyalty (CL) (new) as inputs, and CL (referred and repeat) and willingness to pay as output measures. Design/methodology/approach The research extends our understanding of existing service productivity models with the development of the SEPIA model. Data were collected from 14 organisations operating in the Australian travel and tourism industry, which was analysed using a data envelopment analysis input oriented variable return to scale method as applied to the SEPIA model customer interface. Findings Four key findings from the research include: customer choice and their ability to pay is a determinant of service productivity; service productivity is a two stage process when measured; SC is not categorical; and quality business systems do impact service productivity. Research limitations/implications A limitation of this research is that only one (customer) of the five key stakeholders, customer, employee, manager, supplier and shareholder, was operationalised in this research paper. Practical implications The operationalisation of the SEPIA customer interface using transactional data and measuring non-financial, intangible factors of productivity provide managers with insights on what services to offer, when to invest in or promote the use of technology and whether to spend marketing effort on customer acquisition or customer retention. Originality/value The SEPIA model positions service firms within a social and service value network and provides a range of customer measures that extend the current capital (K), labour (L), energy (E), materials (M) and service (S), KLEMS measure of productivity and can be used to show the impact customers have on service productivity.
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37

Agrawal, Aman, and Srijeet Halder. "Identifying factors affecting construction labour productivity in India and measures to improve productivity." Asian Journal of Civil Engineering 21, no. 4 (November 22, 2019): 569–79. http://dx.doi.org/10.1007/s42107-019-00212-3.

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38

Weeks, Stephen. "Productivity is far too complex for simple measures." Nursing Standard 25, no. 31 (April 6, 2011): 33. http://dx.doi.org/10.7748/ns.25.31.33.s46.

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39

Wardle, Margaret, Katherine Cederbaum, and Harriet de Wit. "Quantifying talk: developing reliable measures of verbal productivity." Behavior Research Methods 43, no. 1 (November 18, 2010): 168–78. http://dx.doi.org/10.3758/s13428-010-0019-y.

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40

Abad, Arnaud, Rabaozafy Louisa Andriamasy, and Walter Briec. "Surplus measures and luenberger Hicks–Moorsteen productivity indicator." Journal of Economics 125, no. 3 (May 15, 2018): 279–308. http://dx.doi.org/10.1007/s00712-018-0611-7.

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41

Lam, Pun-Lee, and Teresa Lam. "Total factor productivity measures for Hong Kong telephone." Telecommunications Policy 29, no. 1 (February 2005): 53–68. http://dx.doi.org/10.1016/j.telpol.2003.12.009.

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42

Gable, William H., Theodore N. Pappas, Danny O. Jacobs, Desmond A. Cutler, and Paul C. Kuo. "Productivity Measures Associated With a Patient Access Initiative." Annals of Surgery 243, no. 5 (May 2006): 604–11. http://dx.doi.org/10.1097/01.sla.0000216305.57298.93.

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43

Miller, Jack L., and Johan M. Gowdy. "VERTICALLY INTEGRATED PRODUCTIVITY MEASURES: TESTS OF STANDARD ASSUMPTIONS." Review of Income and Wealth 38, no. 4 (December 1992): 445–53. http://dx.doi.org/10.1111/j.1475-4991.1992.tb00454.x.

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44

Reardon, James, and Irena Vida. "Measuring retail productivity: monetary vs physical input measures." International Review of Retail, Distribution and Consumer Research 8, no. 4 (January 1998): 399–413. http://dx.doi.org/10.1080/095939698342733.

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45

Klassen, Kenneth J., Randolph M. Russell, and James J. Chrisman. "Efficiency and Productivity Measures for High Contact Services." Service Industries Journal 18, no. 4 (October 1998): 1–18. http://dx.doi.org/10.1080/02642069800000038.

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46

Carlos Arnade and Keithly Jones. "Crops and Livestock Productivity Measures for Selected Countries." Journal of Developing Areas 44, no. 2 (2011): 329–48. http://dx.doi.org/10.1353/jda.0.0109.

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47

Reynolds, Dennis, and David Biel. "Incorporating satisfaction measures into a restaurant productivity index." International Journal of Hospitality Management 26, no. 2 (June 2007): 352–61. http://dx.doi.org/10.1016/j.ijhm.2006.01.003.

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48

Gauffriau, Marianne, Peder Olesen Larsen, Isabelle Maye, Anne Roulin-Perriard, and Markus von Ins. "Publication, cooperation and productivity measures in scientific research." Scientometrics 73, no. 2 (August 3, 2007): 175–214. http://dx.doi.org/10.1007/s11192-007-1800-2.

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49

Balassiano, Victor, and Steve Bullough. "Measures of academy productivity in English championship clubs." Team Performance Management: An International Journal 27, no. 3/4 (May 6, 2021): 332–50. http://dx.doi.org/10.1108/tpm-09-2020-0083.

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Purpose This study quantifies academy productivity within English football clubs that have competed in the second tier (Championship) between 2017 and 2020. Previous research has outlined that clubs situated underneath the top leagues have an important role in the development of elite professional players. This study aims to examine that level of the pathway further in England. Design/methodology/approach The utilisation of academy players was conducted with data from 33 eligible clubs, from 2017/2018 to 2019/2020. Two measures of productivity are defined for comparison: “Utilisation” (the total minutes played by academy graduates) and “Starts” (the number of times an academy player started for the first team). To quantify these measures, players and clubs’ indices were also defined through two perspectives: “global” (proportion of all games played from 2017/2018 to 2019/2020) and “local” (proportion of games the player featured only). Nationality and position were also included. Findings Headline findings demonstrate large differences between clubs for the type and proportion of playing opportunities created. The data outlines that academy graduates have greater utilisation and starts in cup competitions, particularly the English Football League cup. Clubs in the sample being relegated from the Premier League into the Championship recorded weaker “utilisation” and “starts” compared to those that competed in the lower divisions. Academies are producing and using a greater proportion of defensive players (goalkeepers, defenders, defensive midfielders) compared to more attacking sectors of the pitch. Originality/value This offers useful insight for academy managers, allowing comparisons between clubs. It has implications for future strategies around the role of the academy and approaches to generating player opportunity.
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50

Forsgren, Nicole, Margaret-Anne Storey, Chandra Maddila, Thomas Zimmermann, Brian Houck, and Jenna Butler. "The SPACE of Developer Productivity." Queue 19, no. 1 (February 28, 2021): 20–48. http://dx.doi.org/10.1145/3454122.3454124.

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Developer productivity is about more than an individual's activity levels or the efficiency of the engineering systems relied on to ship software, and it cannot be measured by a single metric or dimension. The SPACE framework captures different dimensions of productivity, and here we demonstrate how this framework can be used to understand productivity in practice and why using it will help teams better understand developer productivity and create better measures to inform their work and teams.
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