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Generative Artificial Intelligence is a powerful tool that can help boost productivity in the workplace.
AI can help interpret complex data sets, identify trends and even predict future outcomes based on historical data.
However, it is important to note that Generative AI is not a replacement for human workers, because IA cannot operate itself.
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I am performing a Stochastic Frontier Analysis on unbalanced panel data in R with the package frontier (based on Battese & Coelli 95). I wonder how can we check for autocorrelation / endogeneity. The residuals() function returns the residuals, which consist of both the noise term and the inefficiency term, i.e. residual = y - f(x) = v - u. I am not sure if one can use these residuals to test for heteroscedasticity (Breush-Pagan test) or autocorrelation (LM test). Have you seen this in the literature?
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Not sure if this helps but there is a Stata package called xtsfkk that handles endogeneity in panel SFA models. It was only released in 2022, I am using it for my current research and it it a great routine.
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A Latin American company has National and International projects. National ones have high productivity (in terms of hours spent and hours billed to the customer), but the profit is low (compared to the International projects) because the incomes and the costs of the projects are in Pesos (a weak currency). On the other hand, the International projects have a productivity of around 60%, but since the incomes are in Dollars and the costs are in Pesos, they are highly profitable, even though they are not productive.
This has bias the perception and had led the company to believe that the 60% of productivity is fine because the profit is enough. On the other hand, is very difficult to compare one type of project with the other.
How would you recommend to assess the cost-benefit analysis in an agnostic point of view, that make both types of projects comparable?
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Balanced score card model can be a way out. There are 4 dimensions: financial, customer, inernal process, learning and growth. Weightage can vary according to your priority. Hope that it helps.
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I know that using non-discretionary input could be an issue in the original, input-oriented DEA models where input can be expanded and/or contracted. The concern, I think, was that we don't want to produce efficiency scores suggesting efficiency could be improved by reducing uncontrollable (non-discretionary) inputs. However, I wonder if the inclusion of non-discretionary input is as consequential for output-oriented VRS DEA models? Most of the literature is based on input-oriented DEAs. Any comments will be appreciated, Thanks.
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good question
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I have got the time series data (results) of mi, tc, pech and sech. How to elucidate these? How to correlate these with real world meanings?
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@Ahmed Mohamed Habib Sir, thank you.
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The focus of this discussion is software for Football. According Chang (2018), mentioning Carling (2005), generally, performance analysis can be classified into two main categories: notational analysis and motion analysis. The two systems have different focuses. Notational analysis provides factual record about the position of the ball, the players involved, the action concerned, the time and the outcome of the activity, etc. Motion analysis focuses on raw features of an individual’s activity and movement, for example, identifying fatigue and measuring of work rate.
The two systems contribute for the performance analysis which has three main aims:
  1. Observing one’s own team’s performance to identify strengths and weakness
  2. Analysing opposition performance by using data and trying to counter opposing strengths and exploit weaknesses
  3. To evaluate whether a training programme has been effective in improving match performance
Performance analysis is not just about analysing matches and games. It is essential in the training programme to help coaches improve players’ performance. The following figure shows the coaching cycle. Performance analysis plays a key role in this cycle. Starting from the top, “Performance” means the performance in the game or training. “Observation” can be from the coach or video camera. Since research indicates that coaches are able to recall fewer than half of the key incidents that arise during the game, video camera is a better way which can record all the key events (actions and movements) for further analysis. In “Analysis”, it means analysis of data which include data management. For example, using performance analysis software to code the game, editing footages from the camera, extraction of data from data provider, etc. These are the areas in which the performance analyst spent most of the time. The product of this “analysis” stage can be statistical analysis and video recordings. In “Interpretation”, it can be put in two ways according to my experience. It could be done by coach or performance analyst. Some analysts have the authorisation from coach to interpret the data and then write a report or make a presentation to the coach or team. Some coaches just want the data from performance analysts and the coaches will interpret the data by themselves. It really depends on the coaches’ preference and the partnership between the analyst and the coach. After that, “planning” means the coach plan what to do after knowing what went wrong or which part the team did well. The coaches have to evaluate the performance prior to this planning stage. Otherwise, he doesn’t know how to improve the team’s performance in the next match. In most of the cases, it means the planning of the coaching session using the result of the performance analysis. “Preparation” means the execution of those coaching session in the training so prepare the team for the coming game. It will go back to the “Performance” stage and the whole cycle keep going.
What kind of Software or App are you using for Performance Analysis in football? Can you share with us the positives and negatives aspects according your experience?
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Sportscode, Dartfish.
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I've already estimated the technical efficiency model, technical inefficiency effects model, and got the values of log likelihood, σ2, γ, and I just want to test the hypotheses concerning the model parameters.
Attached is an example of a table containing the results of a stochastic frontier model (Cobb-Douglas production function), in addition to the results of the technical inefficiency effects model (10 business environment variables (Zi)). Could you please, given the estimated parameters in table (1) tell me how to test the following hypothesis:
1. Cobb-Douglas does not the appropriate production function form
(H0: β3 = β4 = β5 = 0)
2. No Technical inefficiency effects
(H0: γ = δ1 = δ2 = …= δ10 = 0)
3. No stochastic inefficiency
(H0: γ = 0)
4. No joint inefficiency variables
(H0: δ1 = δ2 = …= δ10 = 0)
Thank you very much in advance for this valuable help.
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Hi,
welcome for your reply..
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Dear collegues, researchers and practicioners on DEA.
In the 16th International Conference on Data Envelopment Analysis we presented deaR, a new R package for DEA. Now, it is a pleasure for me to announce that deaR is available to be installed and used.
deaR has been designed and built so that non-R users can easily use it. This package allows to run a wide variety of models based on DEA:
  • Conventional DEA models: CCR, BCC, directional distance function multiplier, additive, non radial, sbm, radial super efficiency, additive super efficiency, sbm super efficiency, cross efficiency, bootstrapping, etc.
  • Malmquist index
  • Fuzzy DEA models: Kao and Liu´s model, some possibilistic DEA models, fuzzy cross efficiency.
You can get a brief introduction tutorial to install and use deaR (in English, Spanish, and Chinese) by clicking on the following link:
deaR is not only oriented to research but also teaching. For that reason, deaR provides some datasets that come from articles already published. These datasets are used in the deaR function examples to replicate the results of the articles.
We would really appreciate if you could spread the information about deaR among your colleagues and students.
We will release a deaR shiny version (an interactive web app) son. We will inform you when it is available.
Any comments and suggestions to improve deaR will be really appreciated. We also accept suggestions of DEA models to be considered for being programmed in new versions of deaR.
Best regards.
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deaR has been updated. The new version (1.1.0) includes:
- several decompositions of the malquist index (sequential, global, generalized, etc.)
- cost, revenue and profit models
- undesirable (and separable) inputs/outputs (according to Tone 2003) in sbmeff model
- fixed some bugs
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A Primary Author Citation Index (PACI) or p-index, proposed here, would not count citations of published works by scientists, but would instead determine the proportion (percentage) of all published works [with at least 1 citation] by a scientist for which the scientist is senior (first) author on the publication, regardless of the number of authors on each individual publication (included in the p-index calculation). The intended primary purpose of the p-index is to determine the proportion of a scientist's career publications that the scientist actually contributed to writing the majority portion of the publication (for which the first or primary author is most responsible and credited). The p-index value attempts to quantify a scientist's career accomplishments in terms of completing the entire research cycle, culminating with the actual primary (substantial) writing of all final research and related products (study results and other types of publications). Please do not respond by addressing the many problems associated with undue credit given to scientists in positions of power (over subordinate scientists) or different conventions, policies, or ethics used in determining authorship order (such as which scientists got the grant, did most of the research, or collected most of the data presented in a paper); and please refrain from giving unconstructive opinions.
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This is quite un scientific and biased.
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Some studies have found managerial quality to be significantly associated with employee productivity and firm performance (Cirera & Maloney 2017 Innovation Paradox; Haldane 2017 Productivity puzzle; Bloom Sadun & van Reenen 2016 Management as technology). Yet despite these findings, there has been no apparent elevation of this factor in the intervention and treatment of poor productivity organisational environments. The question becomes; why is this so ? Why is the message not getting translated into practice? Is there a need for a wider and richer set of empirical evidence to prove the significance ? Are there strategies to catalyse the process of transformation?
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"quality of management" is quite broad... what specific dimensions of quality are you focused on?
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Hello. I was reading a literature on the estimation of a (firm-level) production function.
y = a0 + a1x + e
where e is an error
To avoid the endogeneity issue, I found that the econometricians divide the error term into one which is observable by the firm, but not by the econometrician (u) and the other i.i.d error term not observable by both (v) as below
y = a0 + a1x + u + v
Then, they control for 'u' using a proxy (because it is the source of endogeneity). My questions is that, after obtaining consistent estimates for a0 and a1, they use the residual `u_hat' as the measure of the level of TFP as
TFP = u_hat = y - a0_hat - a1_hat * x 
However, isn't it true that the above TFP also contains v_hat such that
TFP = u_hat + v_hat = a0_hat - a1_hat *x?
Is there any particular reason that the current literature use the former and dismiss v_hat?
Thank you so much in advance!
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We have to subtract v-hat as well.
TFP=y-aohat-a1hat*x-vhat
The production function equation is
Y=a0+a1*x+u+v
Hope I am not wrong...
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We work on dates and we need to do volatile product analysis on top, and we do not have head space nor spme,
And we were not able to make extractions by traditional methods, then, what are the appropriate solvents and what are the effective methods for making these extractions?
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According to my experience, we generally use hexane for our extraction. after powdering samples in liquid nitrogen, we add hexane, and put the sample in cooled sonification bath. after that by centrifugation and filtration, we separate plant particles. the sample is analyzed by GC-MS
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I am trying to build a Panel data for Norwegian oil companies to analyse their productivity as a function of Capital, Labour and R&D expenses. I was only able to find relevant data on 5 companies where in the industry there are 60 licensed to operate, the sample is indicative of around 10% population. 2 companies are industry leaders. remainder are med/small sized. For capital, I shall be using change in logs of market cap over the year. For Labour, change in logs of number of permanent employees. I am converting all the values to 2016 USD equivalent to account for inflation.
VA = Sales - Intermediate purchases
DlnVA = a*DlnK+b*DlnL+c*(realR&Dspend/realVA)
The issues I am facing-
1)I am stuck trying to take DlnK for the respective values. If you could show mw how to take those it'd solve a major problem for me. I want to know whether to use market capitalisation or values of Total assets - Current Liab from the balancesheets and then discount them accordingly for measuring K. Please note, K will exclude all R&D investments.
2) What kind of errors/problems I might face comparing companies like Statoil (International Oil Comp) and Petoro (Local Norwegian Comp) on the basis of the R&D exp which might not have necessarily been carried out in Norway itself.
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What is factor productivity ?
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Thank you all of you. these details also helpful me.
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How can productivity be measured for a generic pharmaceutical company?
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Divya,
some ideas shared below
The Managerial Quality variable (aka Managerial Capital or Quality of Management) has been found to be a very significant factor influencing productivity
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I have two questions. If anybody helps me, I shall be highly obliged. Thanks in advance.
(i) Can anybody provide me the generalized primal version (i.e. maximization problem) of input-oriented VRS model?
I have tried it myself with an example. Actually, I tried to convert an input-oriented VRS model from dual version (i.e. minimization problem) to primal version (i.e. maximization problem). I found in case of objective function and constraints, an additional variable will come that does not exist in case of CRS model primal form (maximization problem). Additionally, the denominator part that is equal to 1 (constraint) in CRS primal form, it becomes less than or equal to 1 in VRS primal form. Am I right?
(ii) I know that in input-oriented VRS model, pure technical efficiency is calculated, scale efficiency is eliminated. I also know that in dual form it is done by adding a constraint summation of lambda or dual weights is equal to 1. However, I cannot relate these things.
Additionally, how this (i.e. pure technical efficiency without scale efficiency) can be explained with the primal form of VRS model?
Any kind of suggestion will help me a lot.
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Dear Saeed. I suggest you to read DEA,models and applications written by William cooper and et all.
You can find your questions about DEA on that.
Regards,
Amir
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I'm able to perform the discrete version with summations, but am having a hard time doing it with integrals. In my case firm TFP has a continuous distribution and shares are a function of productivity as well.
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Please refer to the article
"Idiosyncratic Productivity Shocks and Plant-Level Heterogeneity"
by
Raphael Bergoeing, Andrés Hernando, Andrea Repetto
at
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does anyone know articles who talks about cognitive, affective and personality correlates
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can I get information on the scoring of the 12 item pass scale for academic procastination
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To choose between group-based incentive programmes or individual based incentive plan
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Dear Joseph,
Economics can be defined by just the word “incentive”. Usually it applies to the individual trying to better its condition through competition in Adam Smith’s world. In group situations, members have the incentive to freeride, and sometimes in teams such as in two persons loading bags onto a truck, you cannot separate whether each is carrying its fair share of weights in the work. To some extent by encouraging esprit de corps in a group productivity will be enhanced. But it does not appear that a group will be able to optimize productivity.
According to Herbert A. Simon of Carnegie-Mellon University, manager of the group activities may be only able to “satisfice” to reach their goals. For example, by the use of Goal Programming, managers may seek to minimize the deviation of the actual and achieved goals of a project, thereby satisficing rather than optimizing. If you peek into the literature, you may find that many ways were tried such as giving bonuses, holding a tournament etc. to enhance incentives.
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Day today life products small to big
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Thanks very much madam
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I've found EWPS, but it is designed for subjects with/without mental disorders.I'm looking for an instrument to be used in a self-report format. Anyone have ideas?
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Check out this systematic review
Both the Stanford Presenteeism Scale and the Health and Work Questionnaire work well.
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Would expanding the levels of flexibility for movements of talent between firms and within firms result in a positive productivity impact (refer section 1, page 5 on Policy Document attached). On a scale of Very Low, Low, Neither Low nor High, High, Very High, what productivity impact would most likely be the result ? Are there empirical data to support your assertion ?
Policy perspectives aimed in favour of unshackling SME’s of unnecessary strictures and empowering SME's to achieve talent-driven, outwardly-focused global competitiveness should  feature flexibility to respond to market dynamism.
Flexibility covers a wide range of issues including:
a)     Labour force training should be flexible in its delivery and not be rigidly tied to historical sectors and industries.  Training content and packages should rather be dynamically adjustable and malleable to suit emergence of new sectors and industries.  Responsive repackaging should be a key feature
b)    The picking of winners and locking in / redirecting training resources to those ends is to be discouraged.  The identification of areas of comparative advantage and repurposing  training resources to those ends is also to be discouraged.  Preference is for flexibility to be able to match market demands.  Where appropriate, then time-limited Tax Incentives may be dynamically deployed to encourage training in emergent new sectors.  However, there is a danger of having these incentives becoming institutionalised and remaining on the books way past their useful and value-enhancing period.  Constant policy and legislative re-calibration would therefore be required.
c)     Certification and training of the labour force should seek to produce an outcome where human talent is flexible and have the trained / certified individual be imbued with the capacity and capability to respond in a dynamic way to the varying job opportunities that will emerge over their lifelong working cycle.
d)    Hire and Fire practices should be reviewed to eliminate rigidity and so adjusted to build in higher levels of flexibility in order to
(1) allow for easy and smooth movement of talent dynamically between firms and sectors, reducing stickiness and enhancing responsiveness as market demands change;
(2) allow for smooth movement within firms.  As staff transition through their individual life cycles, job cycles and task cycles, Personal Productivity Performance changes and impacts their work output.; in some cases, upwardly and in some cases, downwardly.  As individuals yearn for differing work-life balance states, then the SME firm needs an ability to flexibly treat with these employee desires in order to retain talent, or attract talent.   Both individuals and firms need the capability and flexibility to adjust the form of engagement in order to align to these changing conditions.  Where, on the other hand, the firm faces declines due to market conditions they will need flexibility to change talent engagement from one form to another (eg from flat-fee compensation base to a performance-fee base). Further, as the skillset of the talent becomes mismatched with market needs, then flexibility will be needed to enable enhanced responsiveness through training and development but also through job and task modifications.  Rules for engagement, disengagement and modification of engagement would need to support innovativeness, productivity and competitiveness.
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It depends (as a favourite answer of the economists)...
Rostislav Kapelyushnikov, Andrei Kuznetsov, Olga Kuznetsova, (2011),"Diversity within capitalism: the Russian labour market model", Employee Relations, 33(4): 395 - 412.
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Is there a positive benefit to aligning compensation systems to firm productivity ? (refer section 3, page 8 on Policy Document attached). On a scale of Very Low, Low, Neither Low nor High, High, Very High, what productivity impact would most likely be the result ?
Are there empirical data to support your assertion ?
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An empirical work on performance related pay (PRP)and its effect on firm productivity was carried out by (Federica Origo) where he found that the introduction of
collective performance related pay significantly increased productivity by around 3-5 per cent, but such effect varied greatly by firm size, industry and union density. He also showed  that the design of the PRP scheme – in terms of number and type of parameters used – were also relevant for firm productivity.
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Regarding Positive and Negative Spillover in Work Life Blance
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I have reviewed various theories like social capital, connectivism, metcalfe's law, already but they don't seem to capture the essence of my study. 
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Dear researchers
I'm evaluating the effects of mobbing on organizational productivity, and I  want to know whether there are any  researches about this topic?  especially quantitative ones.
I would like to thank you in advance for your answers.Best Regards
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Hello Mohammed,
I can suggest of my research on mobbing, "Hotel Employees' mobbing, burnout, job satisfaction and perceived organizational
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There are many studies found in the literature regarding  influence of organization culture on the productivity of employees. Can anyone give me some insight on the reverse fact (i.e., influence of productive employees on organization culture) ?
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Sayoni,
Good productive employees are the face of the organization and they portray a positive image about the organization and develop a positive organizational culture in the organization.
In the first place, the employees are productive, because of the organizational culture that prevails at that point of time. They become more productive when the organizational culture is positive and vice versa,
For an example, in an organization, if an employee is counter productive and talks bad about the company all the time, then over a period of time, the fellow employees will also follow suit and they will be less productive as well. On the contrary, if the employee is good and highly productive, then the fellow employees will also try to achieve the same level boosting the organizational culture in the company.
Guess, this will help.
Regards.
Sekar Gopal.
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I try to assess team-level proactive traits (not behaviors). Can anyone suggest a good measure or source to develop a good measure?
or anyone can suggest a good measure for team proactive climate?
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Need of team-level proactive traits (not behaviors) and their assessment (e.g., good, same, or bad) depends upon where these team-based resources are being applied and what for (e.g., purpose)?
I have studied the required traits for such a team-based organization in the context of product design & development for end-manufacturing.
However, these traits may not be limited to only manufacturing.
Take  a look....
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for research purpose 
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Usually we measure the change in labor productivity. For example, before invention a worker produced 10 details per hour, and now 12. Then his productivity has grown by 20%. The source of productivity growth can be in using machines. For example, a farmer can cultivate 100 sq.meters of land per day with primitive instruments, but 10000 sq. meters with a tractor. Then productivity grows by factor 100.
Cobb-Douglas function (if it is known) is a good instrument to define the share of labor and capital in the output. However, this is not always balanced. For example, tax, or profit of intermediary can make the sum of shares less than one. It can be argued that land rent and resource or energy input can also contribute to production function.
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What are the main and other efficient and developed methods as ANN or Multiple regression analysis that can be used on measuring the Climate and Geography factor influences to the construction company productivity by regions ?  I am looking for suitable and flexible methods that can be used on my research work. I am going to publish SCI Journals asap and looking for benefit modeling tools or methods. ... 
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Have you tried DEA- Data Envelopment Analysis:
It has been used to measure productivity of countries, cities, bank branches, etc.
For some examples see:
Charnes, A., Cooper, W. W., & Li, S. (1989). Using data envelopment analysis to evaluate efficiency in the economic performance of Chinese cities. Socio-Economic Planning Sciences, 23(6), 325-344.
Despotis, D. K. (2005). Measuring human development via data envelopment analysis: the case of Asia and the Pacific. Omega, 33(5), 385-390.
Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41(1), 61-79.
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I am looking for any kind of measurement of KPI´s related with productivity in an environment of business intelligence, specially manufacturer.
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A close look at Norton and Kaplan balanced scorecard; and SCOR models may help
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I would like to assess the productivity change across a 72 DMUs over a 5 year period in a health system. I have 2 inputs and 3 outputs.
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Dear Tom,
MaxDEA Pro supports the bootstrap DEA models. Please find the following link:
(The 24th sentence- Features of MaxDEA Pro).
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Yearly partial productivity of capital of a company is to be calculated.
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We may use the Capital employed as Capital Input.
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The critical positive measure of productivity in the government sector is the improvement in the ratio of the output of the public service to the costs for the delivery of that service; the higher the ratio the better the productivity.  How does one compare the productivity of the government service delivery in one jurisdiction against the productivity of another ?  Does a global index exist for reporting on this type of comparison ?
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Productivity and competitiveness of the sector.
Government needs to promote Technology Up gradation in these unorganized units through local level associations. Incentives in terms of financial assistance for procurement of Machinery, Credit at special rates, technical advice on productivity improvement and selection of equipment & machinery may be provided to units to enhance the productivity and competitiveness of food processing industry. 
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I am currently working on Project Management in Indian Pharmaceutical Industry and through my study would like to show that project management helps in improving productivity of an organization.
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 Thank you Mr Santanu for your valuable response.
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I am interested to estimate technical efficiency based on Battese and Coelli (1992) model. I have tried many times for the estimation both using the command prompt as well the use of instruction file in Frontier 4.1. But the dialog box immediately disappear once I end up the process by giving the command through running the FR0NT41 .EXE. Could anyone guide me where is the problem and how could i estimate it.
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Dear Auro,
I think that there might be an inconsistency between your ins file and the txt file used by the program in order to read the data. If you are not already using R and still want to try on Frontier, send me the .ins file and the .txt file containing the first two or three rows of your data in order to check it on my computer.
Regards
Spyros
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I am keenly interested in what the parameters classified as input and output factors/ variables that could be used to measure performances/productivities of exploration and production activities in oil and gas sectors. Where can I find similar research irrespective of the methodology or method deployed?
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Lulia
Thanks a lot, just reading your inputs
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Hello. I performed ROS production analysis on A549 cells exposed to nanoparticles, I know that primary mechanism to be investigated for NP toxicity is the ROS production or oxidation stress and many papers co-relate this analysis. In my experiments the cell viability decreases to 25% or lesser. Does the co-relation exist still? The cell death would cause the DCF to leak out and there are not enough cells for the conversion of DCFDA to DCF for long exposure times. What do you all think about this? I need feedback
Thank you
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Cell viability is affected mainly due to the following reasons:
1) Cell death by (a) Apoptosis (b) Necrosis (c) Autophagy (d) Senescence
2) Inhibition/mitigation of cell proliferation (slower proliferation gives you a smaller no. of viable cells in MTT/XTT etc.)
Coming to ROS, these species of free radicals are produced copiously in the cell, particularly in the mitochondria, where oxidative processes (such as OX-PHOS) occur. Several antioxidant enzymes (eg: superoxide dismutase, catalase etc.) present in the cell are meant to encounter and neutralize oxidative stress. However, in cellular compartments like the mitochondria, due to a vicious cycle of ROS production, the antioxidant machinery significantly falls short of its functional requirement. 7 out of the 13 polypeptides participating in the electron transport chain are encoded by the mitochondrial DNA. Extensive oxidative stress in the mitochondria damages mtDNA, along with mitochondrial proteins & lipids (lipid peroxidation forms more ROS!!). The mitochondrial SOD (Mn-SOD) often fails to quench the ROS thus formed. The damaged mtDNA often encodes defective ETC proteins, which in turn forms more ROS, and this continues in a cycle. ROS, thus formed, reduce mitochondrial membrane potential (ΔΨm), ie., depolarizes the mitochondrial membranes, thereby forming leak channenls in the mitochondrial membranes. Apoptotic proteins like cytochrome c and AIF come out into the cytosol through this channel and cause caspase-dependent as well as caspase-independent apoptosis. Similarly, ROS is also associated with senescence, necrosis and autophagy. The mode of cell death varies according to levels of ROS and the degree/target of ROS-induced oxidative damage. At the same time, oxidative stress can both stimulate and inhibit cellular proliferation, depending on the mode and site of ROS action. Interestingly, inhibition of cellular proliferation leads to senescence as a consequence.
As you can see, there is no single answer to your question. Effects of ROS on cell viability do not follow a thumb-rule; rather they are governed by a number of associated parameters to decide the fate of a cell.
I hope that helps. Cheers!
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Some researchers stay at their labs until midnight or maybe later. Some of these people enforce their students to do so, but I see that none of them produce more than other people who leave their labs on time.
What makes me post this question is the picture that I've attached with the question. It mainly talks about working in industry, but in my opinion it could be applied to researchers in their labs as well, couldn't it?
I totally agree with this, what do you think? Please tel me if I am wrong and these instructions are not associated with researchers.
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Hi all,
Good question, and a recurring one.
There are at least four other sides to it, though, now that communication tools tend to have people working everywhere, not just in the lab or in the office,
* first, because they're always with you , these tools will make you keep working even at home, on the plane or train (that's where I am as I write this), or sometime even in your car. Your smartphone is a great tool to keep you working anytime of day, anytime of year, and so is your Internet connection. Many of us check e-mails daily even during holidays or off-days. A colleague of mine ( head of a large department) once made a simple calculation: he simply counted all the e-mails he received during one calendar year, multiplied this by 5 mn (as an average time it takes to read, answer, archive or dump any message), and divided that figure by 365 ( the number of days in a year). The result was appaling: he would have needed 7.5 hours on every single day in that year to deal with his incoming mail box, doing nothing else. Of course, this explains why most of us don't answer (and even read) every e-mail that comes in, but it shows how technology can keep you working even when you don't want to, jut to keep up the pace ( another red Queen case, I guess).
* second, because these tools allow for fast communication, they increase the feeling of urgency. Many people keep on working because they are under the impression that something is expected RIGHT NOW from them, and that this cannot be postponed. Back to my young days in research, we sent paper manuscripts through the normal posting system ( now called 'snail mail'), as three print copies to the office of the journals. This office then sent them back to referees - as 'hard copies' again- etc. It then took three or four months before you even throught of getting the reviews back. Life was slower then, but was it less efficient?
* third, this immediateness in communication makes it more difficult to sort what really matters and what doesn't. A great exercise I love doing is checking my emails when I'm back from holidays. The number of messages that were sent as 'urgent', were not answered and for which this lack of answer had striclty no consequence is quite astounding!
* fourth, when you like what you're doing, working can become addictive.The 'workaholic syndrome' lurks, and then how much time you spend working is just not an issue anymore for you, because you enjoy doing it. True, others in your neighbouring circles might not feel that way, and many divorces stem from one of the partners enjoying his/her work more that his family life.
At the end of the day, the proper answer to the question is probably as much about the ability to 'switch off' and do something else when you're not in your usual working place, than just counting hoursthere. Working nowadays is far more than being present in the office or in the lab at given hours.
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A formate ion of some concentration is present in initially known concentration of naOH
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Well I would say why not go for the basic stuff. Formate ion is a week base right? and you are in a basic environment why not start to titrate your solution? first you will titrate the NaOH which is the strong base but then you might see also the peak(in the derivative) of your week base which will start to react after all the NaOH is consumed. You can use the potentiostat to measure the Potential as a function of time.
Another option is since formate can be oxidize to CO2 you can try to see if you get an oxidation wave using CV it will be something irreversible, and I am not sure how easy it will be to see it in a basc solution but you can give it a try.
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To improve productivity with costs under control is possible by MONITORING & CONTROLLING various other parameters such as Yield / Process Techniques and Improvised Methods / Research & Development / Market Expansions / Growth and Investments / Livelihood affiliation / Linking to Prosperity / Best practices in place...
Increasing productivity is one of the prime Good Manufacturing Practices...
Please refer
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I would like to know what parameters to use in establishing the efficiency of exploration and production activities in oil and gas organization
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The performance indicators are organized under two categories – core and additional – to differentiate between indicators that are commonly adopted across companies in the oil and gas industry versus indicators that may not have general applicability to all oil and gas industry companies and/or may not be sufficiently well-defined for common adoption.
Core indicators are typically:
• Considered relevant to almost all oil and gas industry companies;
• Inherent to activities in the oil and gas industry (e.g., upstream and downstream);
• Of common interest to a wide range of local and global stakeholders;
• Generally related to aspects or issues of national or global significance;
• Sufficiently mature in terms of consistent usage and reproducibility by those in the oil and gas industry.
On this basis, the core indicators have been defined to enable generally consistent reporting or aggregation on a global basis. There can be value and benefit in using core indicators to promote consistent performance reporting among companies, encourage best practice sharing and enable industry associations and organizations to generate reasonable overviews of sector performance.
Additional indicators can be of equal or even greater importance to individual companies than core indicators in specific contexts of location, activity or stakeholder group.
Additional indicators are typically:
• Assessed as relevant by the reporting company and its stakeholders;
• Associated with only a subset of the industry;
• Reflective of local regulations or legislation;
• Generally related to issues of local or regional significance;
• Evolving and under development.
Additional indicators may often represent a leading practice in sustainability or non-financial reporting.
Furthermore, some qualitative additional indicators may pertain to issues for which there are currently no generally accepted definitions or performance measurement practices. Additional indicators are typically locally defined and/or relate to local or regional issues. Since indicator definitions may not provide comparable or meaningful descriptors of overall company performance, reporters should exercise caution when interpreting additional indicators on a global basis by either consolidating information or aggregating data. Therefore, reporting at the local (operating unit or country) level is becoming more prevalent for oil and gas industry companies, especially to describe performance in locations where a particular issue has high significance or sensitivity.
Presentation of only consolidated qualitative information or aggregated quantitative data with these indicators may not be as meaningful or useful unless local context, disaggregated data or other explanations are provided by the reporting company. A company may not report all indicators addressed here if it has assessed that the indicator or issue is not relevant across all of its activities or because of insufficient information systems, quality, availability or resources.
Core and additional indicators can be defined either quantitatively or qualitatively. Quantitative indicators are reported as a number with a dimensional unit or some form of a numerical index. Certain indicators, however, do not readily lend themselves to quantification. Many social issues, in particular, are primarily reported in qualitative terms as there is not yet common understanding of appropriate quantitative measures.
When qualitative indicators are appropriate, reporting companies are encouraged to consolidate information and report their performance in terms of underlying policies, commitments, programme initiatives, stakeholder partnerships, industry alliances and case study examples that describe results, benefits and lessons learned from various initiatives.
Over time, qualitative indicators may evolve into more quantitative measures. Companies may start out by describing performance related to operational practices and by using anecdotal examples and local case studies. In time, these anecdotal descriptions may converge into a more objective approach for reporting performance within an organizational segment or operating region of a company. Finally, these practices may emerge as a quantitative index for measuring performance or assessing impacts.
Companies report performance data at varying levels of aggregation ranging from individual facilities to national/regional locations and to global coverage for the entire corporation. Aggregate reporting at the corporate level is most commonly observed for reporting occupational injuries, environmental emissions and incident data as part of both regulated and voluntary public reporting. Reporting companies are encouraged to determine the level of aggregation that is appropriate and provides a meaningful representation of the data being presented.
There are two principal aspects of performance indicators that are of interest to internal and external users of sustainability or non-financial indicator performance data: the absolute quantity of the indicator and the normalized quantity relative to some other measured input or output.
Reporting companies often present raw performance data in terms of absolute quantities that can be expressed in a physical unit of measurement related to weight, volume, energy or financial value. In general, absolute data can be expressed in units of measurement that are readily convertible. Absolute quantities may provide information about the magnitude or size of an output, input, value, or result.
Normalized quantities are relative figures representing ratios between two absolute quantities of the same or different kind. Ratios allow comparisons among operations of different size and facilitate comparisons of similar products or processes. They also help relate the performance and achievements of one company, business unit, or organization to those of another. Ratio indicators can provide information on the efficiency of an activity, on the relative intensity of an output (e.g., energy intensity) or on the relative quality of a value or achievement.
Often, companies measure and report performance based on both absolute and normalized quantities to provide a more complete and balanced representation of sustainability or non financial performance.
For many oil and gas companies, describing the boundaries of reported information or indicators is an important consideration because they often cut across an array of complex operational and organizational relationships, as well as direct and indirect impacts. In the oil and gas industry, two or more parties are often involved in an asset, such as in a joint venture, and work together under a variety of legal forms. In some but not all situations, performance indicators can be aggregated and normalized across a range of dimensions that consider ownership, management accountability, geographic and national locations, industrial sectors, company divisions, business units, facilities and source types.
The different types of performances indicators are the following:
1. Environment performance indicators:
•Hydrocarbon Spills to the Environment;
•Controlled Discharges to Water;
•Other Spills and Accidental Releases;
•Other Effluent Discharges;
•Hazardous Waste;
•Non-Hazardous Waste;
•Recycled, Reused or Reclaimed Materials;
•Greenhouse Gas Emissions;
•Flared and Vented Gas;
•Other Operational Air Emissions;
•Energy Use;
•Freshwater Use;
•New & Renewable Energy Resources;
• Environmental Management Systems;
•Biodiversity;
•Other Environmental Indicators.
2.Health & Safety Performance Indicators
•Employee Participation;
•Workforce Health;
•Occupational Injury and Illness Rates;
•Product-related Health Risks.
3.Social Responsibility Performance Indicators
•Human Rights;
•Bribery and Corruption;
•Political Contributions;
•Political Lobbying and Advocacy;
•Non-Discrimination and Equal Opportunity;
•Employee Satisfaction;
•Training and Development;
•Non-retaliation and Grievance System;
•Local Employment Opportunities;
•Labor Practices;
•Community Relationships;
•Social Investments;
•External Capacity Building;
•Indigenous Communities;
•Resettlement and Land Rights;
•Security.
4.Economic Performance Indicators
•Transparency of Payments;
• Dividends Paid Plus Share Repurchases;
•Payroll and Benefits Suppliers ;
•Capital Expenditures;
•Interest Paid
5.Normalization Factors
•Production of crude oil, condensates, natural gas liquids and dry gas in barrels of oil equivalent;
•Refining throughput;
•Product delivered or terminal throughput;
• Pipeline throughput;
• Motor fuel sales;
• Cargo transported.
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The difference between efficiency and effectiveness is that efficiency refers to doing things right, while effectiveness refers to doing the right thing. Efficiency focuses on the means, while effectiveness focuses on the end result. Moreover, efficiency is short term i.e. current state, while effectiveness is long term.
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Dear C. Lewis Kausel
Thank you for your answer
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In the question below, Carlos Alberto asked about the meaning of productivity. Can we use the ideas of productivity for tangible goods also for intangible results as in science and academia? Could it help heal the many problems our community has with impact, impact engineering and abuse of different related indices?
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Dear Michael
Scientific productivity is a socially constructed phenomenon, differentially interpreted by people operating in dissimilar work contexts, who establish expectations, codes and cultures of productivity. Scientists also differ from one another in their perception of productivity as a result of their specific training, socialization process and divergent career paths.
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Productivity analysis is a new approach to evaluate technologies in production processes at the micro level, we use the stochastic frontier approach to the analysis and macro economic development of countries use Malmquist indices. Generally, policy makers use a piecemeal approach and confuse productivity with yields.
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I found your question after a long time. The API was developed by myself. Did you find the article. If not please write me.
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I am more interested in what is considered "real effort" (not abstract effort), whether from cognitively taxing tasks (e.g. on working memory) or from phsyiologically taxing tasks. Both forms of effort are often experience as "fatiguing" or "exhausting".
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As a starter, check out "Aging, adult development, and work motivation." R Kanfer, PL Ackerman - Academy of Management Review, 2004. And then on the fatigue issues, follow up with the P.L. Ackerman edited book "Cognitive Fatigue: Multidisciplinary Perspectives on Current Research and Future Applications." by APA, 2010. Those might provide you with a basis for starting to address you questions.
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As productivity is the ratio of output/input, can we use both output and input as predictor variable and productivity as response variable to form regression equation?
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Dear Aloke,
productivity == ouput/input.
The above is essentially an identity. There should be NO random variation in the above equation (no errors). Productivity is CALCULATED from outputs and inputs. it is not RELATED to them in a STOCHASTIC manner. If you were able to calculate an individual productivity series (over time) for each group you had (e.g. each firm, industry or country) you could then consider a regression of productivity on EITHER outputs OR inputs (not both). However I don't believe you will gain much information from this as it is already encapsulated in the calculated input, output and productivity series.
One approach which might add some value to current knowledge is to consider how terms of trade (ratio of output prices to input prices) leads or lags productivity (ratio of output to input). The reason is that the product of productivity and terms of trade is equal to profitability. hence if productivity is primarily driven by previous downturns in terms of trade it would suggest that enterprises/industries are innovators primarily when cost-push factors cause them to be so (enterprise viability) rather than being innovators purely in order to maximise profits. I think this is possibly the case for some agricultural enterprises. This approach requires information on outputs, inputs, output prices and input prices. Note that these two innovation causes should be the same when entry costs to the industry are zero or low. It is barriers to entry which may allow an enterprise to 'satisfice' rather than profit maximise. Thus this divergence in innovation causation is more likely to occur in agricultural industries with high costs of entry such as (possibly) broad acre beef production in developed countries. This divergence may also occur for agricultural producers who are semi-subsistence where profit-maximisation is secondary in management concern to providing enough to sustain the family. In such a case innovation (productivity) may be a complex function of terms of trade, availability of new practices and the (perceived) riskiness of new practices. As you can see i personally think that considering the interactions of (lagged) observations of terms of trade on productivity to be of more interest than productivity alone.
Note: You should use a well-accepted aggregation method to create your input index.
Hope that helps.
Cheers,
Daniel