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SINTEF Technology and Society
Industrial Management
2011-12-15
SINTEF A21364 .- Unrestricted
IO Center Report
Implementation and use of IO technologies in drilling
Using the TAM-IO model to influence training and promote organizational learning
Torbjørn Korsvold
Anders Dahlen Lauvsnes
PROJECT NO. 60T154.
REPORT NO. SINTEF A21364
VERSION FINAL 2 of 65
Document history
VERSION DATE VERSION DESCRIPTION
FINAL 2011-12-15
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Table of contents Executive summary ................................................................................................................................................................................................................ 5
1 Introduction .................................................................................................................................................................................................................... 6 1.1 Background ............................................................................................................................................................................................................. 6
2 Theoretical background ......................................................................................................................................................................................... 7 2.1 Integrated operations ...................................................................................................................................................................................... 7 2.2 Consequences of IO-technologies on work processes ............................................................................................................. 8 2.3 Man-Technology-Organization (MTO)- aspects of IO implementation .......................................................................... 8 2.4 Technology Acceptance model for Integrated operations (TAM-IO) ............................................................................... 8 2.5 Technology Acceptance and IO Mindset ......................................................................................................................................... 11 2.6 Organizational learning and implementation ............................................................................................................................... 12
3 Methods .......................................................................................................................................................................................................................... 13 3.1 Study 1: Technology Acceptance for IO questionnaire in ConocoPhillips Norway ............................................. 13 3.2 Study 2: Data quality survey ................................................................................................................................................................... 15 3.3 Study 3: Interviews ......................................................................................................................................................................................... 16 3.4 Analysis .................................................................................................................................................................................................................. 17
3.4.1 Statistics ............................................................................................................................................................................................ 17 3.4.2 Reliability and validity ............................................................................................................................................................... 17
4 Results ............................................................................................................................................................................................................................ 17 4.1 Study 1: TAM-IO questionnaire .............................................................................................................................................................. 17
4.1.1 Team work .......................................................................................................................................................................................... 17 4.1.2 Technology Acceptance Model and organizational issues related to implementation ............... 19 4.1.3 Technology specific findings ................................................................................................................................................ 19 4.1.3.1 General technologies ................................................................................................................................................................. 20 4.1.3.2 Portable video and camera devices ................................................................................................................................. 21 4.1.3.3 Usage of Desktop Sharing (DS) applications ............................................................................................................. 21 4.1.3.4 VAS – Modern Visualisation and Advisory Systems ............................................................................................ 22 4.1.3.5 VCT- Traditional Visualisation and Collaboration Tools .................................................................................... 22 4.1.3.6 RTiS - Real time information systems ............................................................................................................................ 23 4.1.4 Team specific findings .............................................................................................................................................................. 23
4.2 Study 2: Data quality survey ................................................................................................................................................................... 24 4.3 Study 3: Interviews ......................................................................................................................................................................................... 26
4.3.1 Trust in the quality of data ..................................................................................................................................................... 26 4.3.2 Challenges in team composition and turnover ......................................................................................................... 26 4.3.3 Focus of training ............................................................................................................................................................................ 26
5 Discussion .................................................................................................................................................................................................................... 27 5.1 TAM-IO as diagnostic instrument ......................................................................................................................................................... 27 5.2 Training for successful implementation and use of IO technologies in drilling .................................................... 29 5.3 Guideline for training ..................................................................................................................................................................................... 30
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6 Conclusions and further research ............................................................................................................................................................... 32
References ................................................................................................................................................................................................................................. 33
A Appendix 1: Team specific findings on technology usage.......................................................................................................... 35
B Appendix 2: Data Quality Survey questionnaire ............................................................................................................................... 42
C Appendix 3: Results from Data Quality Survey (Study 2) ........................................................................................................... 52
D Appendix 4: TAM-IO questionnaire .............................................................................................................................................................. 61
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Executive summary
This work builds on substantial earlier research that underlines the importance of psychosocial interventions to
increase commitment and learning when implementing new technologies in already working organizations. The
continuous correct usage of technologies, both in terms of amount and modes of use, is crucial for the integrity
of the operations. This is especially important in the light of recent accidents within the oil- and gas industry,
such as Deepwater Horizon, where deviance in the usage of technology was found to be one contributing factor.
Within the framework of the NTNU IO Center the authors conducted a pilot survey within TOTAL Norge in 2010,
using an expanded version of the so called ‘Technology Acceptance Model in order to look at factors that
predicted usage and attitudes towards of new technologies. Based on the results and analysis of that survey a
new TAM-IO model was conceived. In the present survey, from ConocoPhillips Norge, this new model was used to
study the use of several technologies relevant for IO. We also surveyed on data quality issues and interviewed
on learning and implementation issues.
In brief, the results suggest that aspects of perceived usability of the technology are important for the usage and
attitudes towards technologies. This also includes mental representations of the technology. This is moderated
by relevant HSE aspects such as trusting the technology and transparency related to how the technology
generates its output; also data quality to some extent was related to the attitudes towards the technology.
Finally we use the findings to form the basis of a framework for organizational learning and training. This
framework has the successful implementation and maintenance of learning and use as main priorities. At the
same time as organizations such as drilling teams are highly qualified technology professionals, the team
composition is often fluctuating and there is a huge room for misunderstandings, lack of use and wrong use of
data and technology if this is not addressed in a systematic fashion such as with the suggested model.
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1 Introduction
This study set out to describe, understand and tackle typical challenges in implementing and making use of
new technologies relevant to Integrated Operations (IO) in operational organizations. Building on previous
research, our previous pilot survey in Total Norge (Lauvsnes & Korsvold, 2010), and results from two
surveys, this report suggests interventions and tools for follow-up of technology implementation within the
oil and gas industry in general, and drilling and well planning in particular.
1.1 Background
The inquires in the aftermath of the Deepwater Horizon accident on the Macondo field in 2010 highlight
poor training, inadequate experience transfer and communication as well as unsatisfactory usage of
technology as important underlying causes (Tinmannsvik et.al 2011). These are also known as important
risk factors in other major industrial accidents in general (Reason, 1997). Linked to these causes is the
increasing degree of complexity taking place across the oil and gas industry. This complexity is especially
explained by collaborative work across large number of disciplines, locations and actors, and increased
instrumentation such as more advanced technology to retrieve the drilling data in more complex reservoirs.
Also the drilling process is characterized more as a continuous process of change with shifting and
unexpected situations continuously arising. The industry is also characterized by changes in team
composition due to turnover and use of subcontracted specialists.
Consequently, measures that will contribute to improve the capability to collaborate and make high quality
decisions in the drilling environment will be vital. In that respect, one important aspect will be improved
training concepts and methods for how to utilize modern ICT tools. A guideline describing the steps for
developing the training concept towards a full-scale training program is provided in the last chapter.
One will often encounter some level of resistance when trying to implement new technologies in already
existing organizations. As an example this may be visible as some kind of resistance against usage of the
technology, lack of confidence in it or overt protests. Thus implementation of new technologies can affect
work negatively with respect to quality and quantity of work in basically two ways. First, resistance to new
technologies will affect the organizations possibility to maintain their operations with the expected quality
and effectiveness, also new technologies requires a period of training to be used effectively. A lot of
resources have probably been spent in vain trying to implement new technologies because of a failure to
handle the human side of technology introduction.
Considerable efforts have been made to study the users’ acceptance and their behavior towards new
technologies as well as which factors that governs their decision to use new technologies. Understanding the
determinants of technology acceptance and subsequently its use is thought to be important and will provide
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means to create more favorable outcomes of technology implementation. Technology acceptance is thought
to be technology and context specific and this is addressed in the following study.
Vast amount of research underlines the crucial importance of psychosocial interventions and follow-up in
order to create sustainable change when implementing new technologies. The methodology and some of the
background for doing this is described below. The findings from the current survey will be discussed in
relation to relevant previous research. SINTEF has previously done a pilot with the TAM-IO model in Total
E&P Norge, (Lauvsnes and Korsvold, 2010). The findings from the TOTAL Norge survey was elaborated,
condensed statistically and refined for this purpose.
As we describe further down, the drilling- and well sector often has a fluctuating staff, with an array of sub-
contractors. It is thus reasonable to say that in this sector implementation is not static, but rather a
continuous condition as context and people change keeping track of the maturity of usage and which factors
that guide usage. The main aim of this TAM-IO questionnaire is that of a diagnostic tool and a possibility to
evaluate the effect of implementation and training efforts.
2 Theoretical background
2.1 Integrated operations
Integrated operations (IO) refers to the new work processes within the oil- and gas industry that draws on
information- and communication technologies to enable and ameliorate multidisciplinary collaboration
across geographical and organizational boundaries. More specifically, as pointed to by Grøtan, Skarholt and
Albrechtsen (2009), across companies and over time some distinctive agendas of the IO development have
been, and still are:
• To maximize the utilization of Information and Communication Technology (ICT).
• By utilizing real-time data, onshore support, shared information and expert knowledge it is claimed
that decisions and decision-making processes will improve and give “better, faster and safer
decisions”
• To link different kinds of expertise into more efficient work processes, independent of time and
space
• Increased value creation by reduced operational costs, longer life-span, accelerated and increased
production, and higher HSE level. The industry has declared that this is the main driver of IO
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2.2 Consequences of IO-technologies on work processes
Integrated operations has been thought to have the potential to influence teamwork, knowledge sharing and
team effectiveness for all personnel involved in the drilling process, as well as to integrate the drilling team
more closely. The effects on team work from IO are generally observable on two levels, process- and task
level. On the process level mechanisms such as e.g. team cohesiveness, trust, collaboration, effectiveness,
leadership, job satisfaction, and communication are important aspects. On the task level, exchange of
information, job performance, group efficacy and roles and responsibilities are vital factors1. The use of IO
specific technologies is thought to influence a range of these team work processes2.
2.3 Man-Technology-Organization (MTO)- aspects of IO implementation
According to the Norwegian Petroleum Safety Authority the goal of focusing on human and psychosocial
aspects of the drilling operations is to create a work environment that contributes to efficient and safe
operations. This is often separated into three different pillars (DNV, 2005). Firstly the personnel or human
side of operations often comprises the issues of competence and limitations of humans. Secondly the
technology side of operations includes the actual hard- and software, its functionality in supporting the work
processes and finally organizational issues comprehend aspects of leadership, organizational structure and
staffing (DNV, 2005). From our perspective these three pillars may seem as rather static concepts, and the
implementation phase of technology is often not given sufficient attention. One important aspect is that the
implementation phase often, as described further up, will create some kind of organizational friction that
may perturb organizational agility and performance for some time. Also this has implication for
organizational learning. This is described in further detail below.
2.4 Technology Acceptance model for Integrated operations (TAM-IO)
Despite that the introduction of new information and communication technologies (ICT) in already working
organizations may have the possibility to improve the quality and effectiveness of knowledge workers
efforts, it may be expensive and according to some research it has a fairly low success rate (Legris, Ingham
& Collerette, 2003). Based on the Theory of reasoned Action (Ajzen & Fishbein, 1980) Davis proposed the
Technology acceptance model in 1989 (Davis, 1989) as an overarching model describing to predict the
potential user's intention to use a technological invention proposed by someone else.
The technology acceptance model in its original form founded upon the hypothesis that user behavior may
be explained by a user’s beliefs, attitudes and intentions. The original TAM as it is described below consists
1 Lurey, J.S. & Raisinghani, M.S. (2001) An empirical study of best practices in teams. Information & Management,
38, pp. 523-544. 2 Korsvold, T. & Nystad, E. (2007). Evaluating organizational change, new work processes and collaborative practice with Next Generation Integrated Drilling Simulator. NTNU/IO-center: report STF50 F07046.
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of four concepts. The perceived ease of use (PEU) and the Perceived usefulness (PU) as independent
variables and behavioral intention (BI) both as a dependent variable for the PEU and PU and a predictor
variable for actual usage. PEU refers to the person's belief of to what degree the use of particular technology
will be effortless. In contrast to the perceived usefulness that is described below, a person may believe that a
technology will be very useful but that the difficulty of using it, or the perceived cognitive effort prevents
the person from using it. The perceived usability refers to the user’s perception that the use of a particular
technology will improve his work performance. Phillips (1994) described the perceived usability as the
user’s belief that the use of a technology from an external source will enhance that person’s or the
company’s well being. In this one may include the quality, accuracy, time consumption and efficiency of
doing the job. Attitude is included in the model below, but Venkatesh and Davis (2000) have proposed an
extension of the model excluding the attitude. King and He (2006) did a meta-analysis of the relationship
between PEU, PU and BI confirming this model.
The model is supposed to be generic in that it is predictive across a range of computing technologies
(Alghatani, & King, 1999) and contexts (Venkatesh & Davis, 2000). The model was originally developed in
the USA but the question of its cross-cultural validity has been important due to the globalization of
businesses and information systems. In one study comparing e-mail-users in Japan, Switzerland and the
USA, Straub, Keil and Brenner (1997) were able to demonstrate its validity in the USA and Switzerland, but
not Japan. This may suggest that it is not automatically culture insensitive. Turner et.al. (2010) stresses that
the model should not be used outside the contexts were it has been validated (Turner, Kitchenham, Brereton,
Charters & Budgen, 2010).
Davis in his 1989-article acknowledged the fact that these generic qualities may also be the achilles of the
TAM, since it does not provide enough or sufficiently specific information for developers and people
involved with implementation to actively create high technology acceptance, and subsequently in a 2008
article Vankatesh and Bala suggested a research agenda for the ‘TAM3’ based on the TAM in order to
inform how interventions can influence the known determinants of IT adoption and use.
To address this gap in the literature, we draw from the vast body of research on the technology acceptance
model (TAM), particularly the work on the determinants of perceived usefulness and perceived ease of use,
and: (i) develop a comprehensive nomological network (integrated model) of the determinants of individual
level IT adoption and use; (ii) empirically test the proposed integrated model; and (iii) present a research
agenda focused on potential pre- and postimplementation interventions that can enhance employees'
adoption and use of IT. Our findings and research agenda have important implications for managerial
decision making on IT implementation in organizations (Vankatesh & Bala, 2008, p.1).
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A growing body of literature is calling for an expansion of the TAM model to include more about the
external antecedents of the perceived ease- and usefulness constructs. Davis (e.g. 1993) did actually
originally describe a mediating role of perceived ease of use and usefulness between system specific
external variables and user intention and actual use. Later work suggested that other factors influenced these
two predictor variables such as shared beliefs in the benefits of the technology (Amoako-Gyampah &
Salam, 2004), compatibility (Karahanna, Agarwall and Angst, 2006) and user resources (Mathieshon,
Peacock & Chin, 2001). Yet other regards computer self-efficacy (Yi & Hwang, 2003), organizational
issues, socioeconomic factors (e.g. Porter & Bonthu, 2006) and social influence (e.g. Venkatesh et.al. 2003)
as areas of interest when looking at technology acceptance. Also the users' perception of enhanced output
quality, subjective norm or the individuals' perception of the organizations values and norms are predictive
if perceived usability or usefulness (Venkatesh et.al. 2003).
Some research and theoretical work, however, suggest that integrating measures of task-technology fit or
compatibility would enhance the explanatory power of the model. Task-technology fit is defined as the
match between the user’s task needs and the availability functionality of the Information technology
(Dishaw & Strong, 1999). Also aspects of the organization, the management’s and coworkers' behavior and
attitudes are believed to influence on behavioral intention.
Another central issue is also the question of whether the introduction and application of the new technology
is mandatory or voluntary (e.g. Schepers et.al., 2005)3. Many studies have been conducted in areas where
the use of the technology has been voluntary, and in domains where the use have not been voluntary one
have found some differences in the configuration of the technology acceptance model (Brown, Massey,
Montoya-Weiss & Burkman, 2002).
Figure 1: The original Technology Acceptance Model (Schepers & al., 2005)
3 Schepers, J., Wetzels, M. & de Ruyter, K. (2005) Leadership styles in technology acceptance: do followers practice what leaders preach?, Managing Service Quarterly, 15, pp. 496-508.
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In our rapport from the pilot study in Total Norge (Lauvsnes and Korsvold, 2010) we added a wide array of
questions related to computer self efficacy, task-technology fit, compatibility. Finally the following model
seemed to give a theoretical and practical account of our findings.
Figure 2: The adapted TAM-IO model (Lauvsnes & Korsvold, 2010)
2.5 Technology Acceptance and IO Mindset
According to Hermundsgård, Madsen and Hansson (2011) one key measure of implementation success is
achieving the intended level of technology usage. People’s actual use of a technology depends on their
perception of that technology. Implementation of Integrated operation is a change process. Based on the
mindset, it will be more or less easy to perform a required change. We would characterize a mindset as high
or low. These characteristics are pointing to differences in gaps from where a person is today and the level
or step which is required in an IO setting. A “high” mindset will make the step easier than a “low” mindset.
But having a low mindset does not imply that you can’t change. It is possible to learn and improve. But a
person with “low” mindset will require more support, training and time than a person with a “high” mindset.
In such process, a change in mindset must also be regarded as depending on the person’s willingness to
learn and to change. This is why mapping of the organization, team or personnel’s IO Mindset is important
as a starting point in an implementation process.
To sum up, an IO Mindset is defined as a mental model through which we view the aspects of integrated
operations. IO Mindset is formed on the basis of our knowledge, skills, beliefs, attitudes, and experiences.
The technology acceptance model for Integrated operations (TAM-IO) may function as a mapping tool for
the technology specific aspects of the IO-mindset. This is of crucial importance since it 1) helps understand
and thus enhance the actual usage by understanding bottle-necks and important, catalyzing aspects and 2)
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enables the agents implementing to identify and handle ‘friction’ in a constructive way, by learning aspects
described below.
2.6 Organizational learning and implementation
In some of the most influential work on organizational learning Argyris (Argyris, 1999) defines his concept
of action learning. This includes the concept of double-loop learning (DLL) in which an individual or
organization is able to achieve a goal on different occasions, to modify the goal in the light of experience or
possibly even reject the goal if they have already tried to achieve that goal without success. Single-loop
learning is the repeated attempt at the same problem, with no variation of method and without ever
questioning the goal.
There is a great deal of intuitive sense in the supposition that competence and learning is fostered through
experience, however the operations of organizations such as the one in this case study are so complex that
one might wonder whether shared experience is enough for such learning and later implementation to take
place. Day (2010) states that ‘Given the complexity of work experience, learning from such experience is
unlikely ‘in the wild’ (i.e. on the job in an organizational context)’ (Day, 2010. p. 41). Day proposes a
middle way between mere teaching and experiential learning, that is ‘deliberate learning’. This may add to
our understanding of double loop learning in that it also draws on expertise in translating and making the
learning understandable and relevant and articulate, enabling double loop learning. The distinction between
reflection IN action and reflection ON action may be of interest in this context. Reflection in action,
according to Smerk (2010) is a form of skilled intuition, the way that a skilled professional would act
‘online’ when confronted with a unique, unstable or value conflicted task. Reflection on action differs from
this in that it concerns the retrospective study of what have been done ‘off-line’ to learn from it. By using
reflection on action, double loop learning may be enabled as the theories in use of the skilled professionals
that tacit may be made explicit for less experienced co-workers. Using simulation/visualization technologies
in this work may possibly be an enabler for these processes.
Haavik (2010) studied the drilling process in a Norwegian operator company and concludes that the process
of articulating tacit knowledge and invisible work is of importance to learn from successful operations and
accumulate and consolidate learning in the organization. Haavik (op.cit) also underlines that this is of even
greater importance in drilling organizations, as they are often made up of personnel from different
contractors, and may vary over time. This is the rationale for the facilitated workshops as learning arenas as
described in the discussion section.
Several routes of influence are important to predict technology usage. The Technology Acceptance Model
was based on two former widely accepted psychological theories; the theory of reasoned action (TRA) and
the theory of planned behavior. TRA suggests that the ‘subjective norm’ of the individual organization is
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crucial for the intent to use a technology, and may in fact be a necessary. Subjective norm may be defined as
the individual’s perception of whether people that they have a positive opinion of, think that they should
engage in the behavior (Venkatesh et.al. 2003). Thus with all of the other points in the TAM fulfilled, the
peer influence actually becomes a 'setting event' for the usage, or in other words; all other factors being in
place; if there is not a perception that other people that the individual identify with on a personal and/or
professional basis, the technology is not going to be used.
Poor data quality may have substantial economic and safety consequences and influence technology
acceptance in the oil- and gas industry. Improvement efforts in data quality however tend to focus on
accuracy. There is a possibility that data quality perception is more of a sociotechnical nature, some central
aspects are that the data must be accessible (ease of use), the user must be able to interpret the data (ease of
use), the data must be relevant (usability) (Wang & Strong, 1996). Thus often the problem is not the data
itself, but the perception of it leading to lack of use, participation in information commons and decision
making with weak empirical support or use of corrective information.
3 Methods
This report builds on our own research and our own research to construct the working model of TAM-IO.
Based on this a survey was initially sent out concerning Technology Acceptance and Integrated operations
in ConocoPhillips (TAM-IO questionnaire) (Study 1). Also a survey concerning Data quality in relation to
technology usage was sent out to the partners of the NTNU IO-center (Study 2). To conclude this we
integrate the review of earlier research with the empirical findings in study 1 and 2 in a framework for
continuous technology implementation. Study 3 was an interview study with employees from
ConocoPhillips. This represents method triangulation (literature review, quantitative survey and qualitative
interviews). All three studies are described in this method-section.
3.1 Study 1: Technology Acceptance for IO questionnaire in ConocoPhillips Norway
Study 1 was carried out in ConocoPhillips Norway. A wide range of IO-technologies of various complexity
and sophistication is in use within the drilling department of ConocoPhillips in Norway. They offer
possibilities for direct communication across geographical and professional interfaces. In study 1 we also
looked at the use of basic technologies such as e-mail, videoconference, meetings and phone as well as other
more specific, advanced decision support tools specifically relevant for the drilling process.
The main focus for this survey was five of the most common ICT tools for decision and collaboration
support within drilling and well construction in ConocoPhillips:
1. Portable video or camera devices, e.g. Pixavi
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2. Desktop Sharing type applications (in this report abbreviated DS applications), e.g. Netmeeting,
GoToMeeting, Webex
3. Modern Visualisation and Advisory Systems (abbreviated VAS), e.g. eDrilling, Verdande,
Drillscene, Decision Space
4. Traditional Visualisation and Collaboration Tools (abbreviated VCT systems), e.g. Vispo 3D and
EDM
5. Real time information systems (abbreviated RTIS), e.g. Insite from Halliburton and Interact for
Schlumberger
The TAM-IO survey was constructed based on previous published research on technology acceptance, some
questions were modified in order to be relevant for the drilling context and some questions were added to
cover issues such as HSE and team work. In this version we are looking at several tools, something that will
be needed in most cases, so a shortened TAM questionnaire was used and repeated for each tool. The survey
was distributed through a web based survey tool (Confirmit) to 270 members of the ConocoPhillips drilling-
and well organization and a total of 6 reminders were sent out. The response rate, as well as other sample
characteristics is stated below.
Table 1: Years of experience from current or similar positions.
Freq % of resp.
CopNO 102 57,6
Contractor 60 33,9
Missing 15 8,5
Total N 177 100
Table 2: Affiliation, external or internal employee.
15
The respondents were also asked to rank their team affiliation. Since most employees have more than one
affiliation the respondents were allowed to rank team affiliation from 1 to 3. In the following analysis
section the respondents were ranked according to the teams they ranked as their primary affiliation, or 1.
Rank Team name 1 2 3
Drilling team onshore 15,3 15,8 9
Drilling team offshore 12,4 9,6 5,6
Well integration and abandonnement 4,5 5,6 8,5
Well intervention and completion onshore 11,3 9,6 3,4
Well intervention and completion offshore 13 5,6 2,8
Subsurface team 9,6 4 4,5
HSE&Q team 6,2 2,8 4
Drilling support team 8,5 3,4 4
Other 6,8 3,4 5,6
Total 87,6 59,8 47,4 Missing 12,4 40,2 52,6
Table 3: Team affiliation, 1 represents the primary team.
3.2 Study 2: Data quality survey
A survey was sent out to 164 individual participants from the partner organizations of the NTNU IO-center.
This survey was constructed by SINTEF and NTNU-IO center staff to understand aspects of data quality
(DQ). The survey was case based. The TAM-IO questions were included. Of these 62 answered and 30 of
these were from ConocoPhillips. The results from the cases are extensive, and reported in an appendix. They
are not reported to a greater extent in the main part of this report as they are not directly related to
technology acceptance and training which are the main foci of this report. The survey was distributed
through a web based survey tool (Confirmit).
In the following descriptions a high ’Mean’ indicates that the statement/phenomenon occurs often.
Please do also observe that there was a substantial amount of ’data missing’ and ’don’t know’ in the case
questions. This may interfere with the value and reliability of findings, but may also be an indication that
DQ-issues are not often explicit but an integral, tacit part of work.
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17
3.4 Analysis
3.4.1 Statistics
SPSS (Statistical Package for the Social Sciences v.16) was used for statistical analysis. As this survey
aimed both at giving input to the current process and develop tools for later similar processes we used
parametrical statistics analyzing the relationships between the variables as well as psychometric analysis on
the survey itself.
3.4.2 Reliability and validity
Reliability is the consistency of a set of measurements or of a measuring instrument. This means looking at
whether groups of items in a questionnaire are related to each other as they are presumed to do, or ensuring
that the measurement gives the same result if repeated. Cronbachs Alpha analysis, Pearson’s correlation and
factor analysis are often used as tools to look at this. A high Cronbachs alpha-score indicate that the items in
each factor (e.g. team work) are correlated to each other and vice versa, meaning that they have high internal
consistency. Using factor analysis one may determine whether there are other groupings of questions that
better explains the variance of the respondents’ answers. Validity refers to the extent to which a concept,
conclusion or measurement is well-founded and corresponds accurately to the real world.
4 Results
4.1 Study 1: TAM-IO questionnaire
4.1.1 Team work
The questions in the TAM-IO questionnaire concerning teamwork were analyzed to see if they had high
internal consistency as a single factor. All the items together yielded a Cronbach’s alpha score of .09, which
is considered poor, meaning that the items were very loosely related to each other.
A factor analysis (PCA/Varimax rotation) revealed that the questions 9 and 11 had the lowest correlation
with the rest, this included the question ‘It is room for posing the silly questions in our team’, as well as
‘Our team has a good collaboration between on- and offshore’. These questions were thus excluded in later
correlational analysis.
18
Table 5: Questions related to team work. A score of 1 indicates ‘Strongly agree’ and 5 ‘Disagree’.
Team work issues were thought to be influence and to influence the different aspects of the TAM-IO model.
As the later analysis show, this was not the case in this sample in general. We therefore proceeded to see if
the score on the teamwork-variables above was influenced by the primary team affiliation of the
respondents. We tried to see if team-affiliation had an effect on the answers on the teamwork questions. This
is shown below, there was little or no significant effects of what team the respondents adhered to and their
answers on the teamwork-questions in the TAM-IO survey.
Figure 3. Correlation between aspects of teamwork and team affiliation
4
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19
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20
type of general technologies (1) and the five more advanced collaboration and decision support technologies
(2-6):
2. Portable video or camera devices, e.g. Pixavi
3. Desktop Sharing type applications (in this report abbreviated DS applications), e.g. Netmeeting,
GoToMeeting, Webex
4. Modern Visualisation and Advisory Systems (abbreviated VAS), e.g. eDrilling, Verdande,
Drillscene, Decision Space
5. Traditional Visualisation and Collaboration Tools (abbreviated VCT systems), e.g. Vispo 3D and
EDM
6. Real time information systems (abbreviated RTIS), e.g. Insite from Halliburton and Interact for
Schlumberger
4.1.3.1 General technologies
E-mail was the most commonly used, and had a low standard deviation (SD). Phone was second most
commonly used, but with a slightly higher SD, indicating more variance in the use of it. There is a general
lack of benchmarking of use of these technologies thus these results also speak for themselves, and serve as
a point of reference for later measurement.
Table 6: descriptive statistics of the use of general technologies. A low score indicates a high degree of use: A score of 1 indicates ‘Several times a day’, 2 'Daily', 3 'Weekly', 4 'Less' and 5 ‘Never’.
Table 7: Frequencies of use of general technologies
21
4.1.3.2 Portable video and camera devices
The general usage of portable camera and video devices, e.g. Pixavi, is fairly low with about 75% of the
employees using it less than weekly or never. When looking at the relationships between the different part
of the TAM-IO model it is clear that
- HSE issues influenced the intent to use and the user behaviour both through an influence general
perceptions of usability, but also directly.
- The ease of use aspects had little, nonetheless significant influence on the user intent.
Table 8: The frequencies of use of portable video and camera devices.
Table 9: The TAM-IO issues of portable video and camera devices. A score of 1 indicates ‘Strongly agree’ and 5 ‘Disagree’.
4.1.3.3 Usage of Desktop Sharing (DS) applications
The usage of Desktop Sharing type applications (DS applications, e.g. Netmeeting, GoToMeeting, Webex)
is somewhat higher, with about 38 % using the technology weekly or more often. Again the survey says
nothing about the intended or ideal level of usage. As for the portable camera and video devices the HSE
issues seems to be important and influences both directly and through general usability aspects.
22
Table 10: The frequencies of use of DS applications.
Table 11: The TAM-IO issues of DS applications. A score of 1 indicates ‘strongly agree’ and 5 ‘Disagree’.
4.1.3.4 VAS – Modern Visualisation and Advisory Systems
The usage of VAS technologies (Modern Visualisation and Advisory Systems, e.g. eDrilling, Verdande,
Drillscene, Decision Space) are in line with the others with approximately one third using them weekly or
more. The patterns of the TAM-IO is replicated also.
Table 12: The frequencies of use of VAS.
Table 13: The TAM-IO issues of VAS. A score of 1 indicates ‘strongly agree’ and 5 ‘Disagree’.
4.1.3.5 VCT- Traditional Visualisation and Collaboration Tools
The usage of VCT (Traditional Visualisation and Collaboration Tools (VCT), e.g. Vispo 3D and EDM ) are
in line with the others with approximately ¼ using them weekly or more. The patterns of the TAM-IO is
replicated also.
23
Table 14: the frequencies of VCT.
Table 15: The TAM-IO issues of VCT. A score of 1 indicates ‘strongly agree’ and 5 ‘Disagree’.
4.1.3.6 RTiS - Real time information systems
The usage of Real time information systems (RTIS), e.g. Insite from Halliburton and Interact for
Schlumberger, are also in line with the others, approximately 1/3 using it weekly or more.
Table 16: The frequencies of use of RTIS.
Table 17: The TAM-IO issues of RTIS. A score of 1 indicates ‘strongly agree’ and 5 ‘Disagree’.
4.1.4 Team specific findings
In this section we give a brief summary of the technology profiles of the different main teams involved in
this survey. First we look at a comparison of the different teams. One should note that we found no
statistical significant effects of team affiliation for the usage of different technologies. Other factors in the
TAM-IO model were more predictive of the usage.
I
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24
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25
and qualitative data. These data are relevant for the direct improvement of data quality rather than
technology acceptance (although the may indirectly influence both technology acceptance and training more
specific case-oriented training needs). They are reported in full extent in appendix. In the table below some
general questions are presented. These questions describe the accuracy of data in the planning and drilling
phase respectively. We do not have any benchmark values from the industry on these aspects, so it is
difficult to say if this is above average, but data inaccuracy seems to be an area of concern where there is
great room for improvement. These numbers may serve as a point of reference for later comparison.
Table 18: General data quality experience. A score of 1 indicates 'Never', 2 'Rarely', 3 'Sometimes', 4 'Often' and 5 'Don't know'.
We were also interested in the relationships between readiness to change and data quality. These results may
indicate the sense of urgency and form a basis for training interventions. As seen in the table below there is
a sense that use of different technologies and training is seen as important for improvements. When looking
at the data from question 15 in the Data Quality survey (see appendix C), it seems to be a tendency towards
believing that improvements in areas such as competence, organization and work processes may be of
greater importance than mere focus on new technologies.
Figure 8: TAM-IO issues and data quality (table left and graph right). A score of 1 indicates 'Disagree completely', 2 'Disagree', 3 'Nether-nor', 4 'Agree' and 5 'Agree completely'.
2,822,92How often in your experience do we knowingly accept that the data being used in decision making is of low quality?
2,812,75How often have you seen or heard about other persons/ teams that have made a
wrong decision because of inaccurate data
2,662,57How often have you or your team made a wrong decision because of inaccurate
data?
3,033,03How often have you experienced situations where you are uncertain data is
accurate
3,002,92How often have you experienced situations where you are sure data is
inaccurate
During drillingIn the planning phase
2,822,92How often in your experience do we knowingly accept that the data being used in decision making is of low quality?
2,812,75How often have you seen or heard about other persons/ teams that have made a
wrong decision because of inaccurate data
2,662,57How often have you or your team made a wrong decision because of inaccurate
data?
3,033,03How often have you experienced situations where you are uncertain data is
accurate
3,002,92How often have you experienced situations where you are sure data is
inaccurate
During drillingIn the planning phase
,6643,9041I think that improvement in work processes can be an important driver for improved data quality
,8033,8341I think that better contracts and business relationships canbe a main driver for improved data quality
,5834,1041I think data quality can easily be improved by bettertechnology and better competence
,6253,9041I think that improvement in organization can be an importantdriver for improved data quality
,8733,7141I see that improved (and more expensive e.g. visualisationtools, automation) technology is necessary to carryout the daily job in the right way
SDMeanN
,6643,9041I think that improvement in work processes can be an important driver for improved data quality
,8033,8341I think that better contracts and business relationships canbe a main driver for improved data quality
,5834,1041I think data quality can easily be improved by bettertechnology and better competence
,6253,9041I think that improvement in organization can be an importantdriver for improved data quality
,8733,7141I see that improved (and more expensive e.g. visualisationtools, automation) technology is necessary to carryout the daily job in the right way
SDMeanN
,00,50
1,001,502,002,503,003,504,004,505,00
I see thatimproved (and
more expensivee.g. visualisation
tools,automation)
technology isnecessary tocarry out the
daily job in theright way
I think thatimprovement in
organization canbe an important
driver forimproved data
quality
I think dataquality can easilybe improved by
better technologyand better
competence
I think that bettercontracts and
businessrelationships canbe a main driver
for improved dataquality
I think thatimprovement inwork processes
can be animportant driver
for improved dataquality
26
4.3 Study 3: Interviews
4.3.1 Trust in the quality of data
Based on the interviews we were able to identify three different aspects of trust related to data quality and
technology usage. It is important to understand that we consider data quality to be a predictor of technology
usage. We will return to this in the discussion. Firstly, the respondents underlined the aspects of relational
trust meaning that in order to be comfortable with usage (perceived usefulness) it is important with personal
relationships, this breaks down distrust. According to the survey relational trust eases implementation and
actual usage. This means that physical and/or face-to-face meetings heighten the trust.
Secondly, also data trust comprehend the importance of knowing something about what data is being used
by whom in order to perceive participation as useful. This is in accordance with survey and previous
research on participation in organizational information (Yuan & al, 2005).
Thirdly, there is the issue of predictability. In order to use technology the participants need to know where
data is available on demand. This may load on the perceived-ease of use part of the TAM-IO model;
however this was not tested statistically. The participants agreed that all of these are realistic to achieve
trough our learning model.
4.3.2 Challenges in team composition and turnover
The interviewees confirmed comments made in the survey that one central challenge in order to achieve a
high level of usage and trust in data quality, one need to take into account that the organization and the
specific teams are made up of internal employees, external consultants and often have a high turnover. This
may result in differences in use, errors in use and misunderstandings and human error that may lead to
safety breaches.
This means that technology implementation need to be a ongoing and continuous endeavor, that, in
accordance with the learning model should create, structure and reinforce arenas that strengthens experience
transfer. Also these arenas create peer influence for use and clarify management incentives for use. This has
proved in the survey to be an important predictor for use.
4.3.3 Focus of training
We wanted to find a focus or foci of training interventions. It seems that the driller may form a focal point of
the operations that enables all participants to engage in training. The driller is placed in a crucial position in
the drilling organization and it seems that all stakeholders relate to the driller. Using this assumption in
training implies presenting cases from the driller's perspective.
27
5 Discussion
This section starts by looking at the TAM-IO model, and how it may be used to understand implementation
process of IO relevant ICT and how it subsequently influence training interventions.
5.1 TAM-IO as diagnostic instrument
It is reasonable to presume that this survey is and will be a useful tool both from a practical and scientific
point of view with respect to the implementation of new technologies in high-risk, high qualified work
environments. Our take-home message is that the TAM-IO questionnaire is a valuable diagnostic tool that
has now been empirically tested and that may guide the continuous learning and training process that entails
the usage and implementation of technology in the oil and- gas industry.
Our aim from the beginning was to make the TAM model as relevant as possible for the drilling/oil- and gas
industry. One part of this is to statistically reduce the model to the point where the most information is fitted
within as few items and/or factors as possible. Using multiple regression analysis, we found that the
following model is the best overview of how the decision process goes. In the discussion section we will
touch into several aspects of this, to make it practically relevant. But this model accounts for about 85 % of
the variance in usage and clearly shows that a focus on usability aspects.
Figure 9: TAM model with calculated correlations.
It was predicted that adding organizational issues in to the model would be beneficial and expand the
potential for predicting outcome in intent and attitude, however different aspects of team-work and
organizational climate did not have such an effect. Adding perceived ease of use and computer self-efficacy
added little new explanatory insight into the technology acceptance in ConocoPhillips. Aspects of the
implementation process however did and the degree of voluntariness and management versus peer
incentives did play a role.
Usability Intent to use
HSE
Usage
28
One interesting finding was also that there were no systematic and significant effects of team affiliation and
usage of the technologies, nor for the most central predictors in the TAM-IO model. This suggests that the
findings are rather organizational specific, rather than team specific. This justifies the reporting of our
findings on an organizational, rather than a team specific level in this report.
In previous research the technology acceptance model has been validated as an important tool to understand
and explain the user intention and behavior when it comes to technology usage. In our previous survey in
Total Norge in participation with the IO Center (Lauvsnes and Korsvold, 2010) we expanded on the model
in accordance with research and report both from the original researchers and other investigators and
included aspects of team work, self-efficacy and compatibility. The results from that research, which only
investigated one technology (eDrilling) was extracted and used to make the survey in which this report is
founded. As the result section of this report shows we replicated previous findings and were able to simplify
and divide the survey into one technology specific, and one general part. With this part we are satisfied and
the psychometric aspects of this questionnaire is to our full satisfaction, and we hope and believe that it will
continue to be an important diagnostic tool in the implementation and training for the use of IO-relevant
technologies within continuous operative organizations within drilling in the oil- and gas industry.
It seems that HSE issues are of relevance for the users' technology acceptance. In line with Venkatesh and
Bala (2008)'s call to inform interventions using the TAM we hope that knowledge about HSE, as an
important predictor of Perceived Usefulness will enhance the success rate of implementation. We found that
social influence and perceived peer intentions and use were of great importance for the use of the
technologies. In previous research (e.g. Yuan & al, 2005) the participation in organizational information
commons depends partly on social influence (the extent to which one believe that the colleagues uses the
information) and technology specific competence. As Haavik (2010) points out the articulation work is
important in order to let tacit knowledge (e.g. ‘how/when/why to technology usage) be articulated and we
hypothesize that it is also crucial to enhance social influence to use the technology. In the interviews that
was conducted as a part of this survey different issues of trust arised as we discussed the technology
acceptance model. Social influence and relational trust are interrelated and it makes both empirical and
intuitive sense that relational trust, trust in data and trusting data predictability may be achieved through
training sessions such as those described in this report.
There were some tendencies for a difference between external contractors and internal employees with
respect to attitudes and intentions, Haavik (2010) also underlines the fluctuating character of teams and
organizations within the oil- and gas industry, with frequent rotation of employees, contractors and leaders
and diverse technologies in use. This, in conjunction with our findings thus underlines the need for social
influence, technology competence and work process competence to be a main part of ‘training’ in what we
29
believe is a continuous implementation of technology. The learning model developed in the Total Norge
survey (Lauvsnes and Korsvold, 2010), thus gains additional empirical justification from our results.
The Data quality survey amongst other aspects indicates that there is a fairly high degree of data inaccuracy.
As commented in the results section these results may now be used as a point of reference after later
interventions and training, and also as a bench mark for later investigation within other companies. Our data
is too limited when it comes to the number of respondents from each company for us to do a between-
company-analysis.
5.2 Training for successful implementation and use of IO technologies in drilling
The results indicate that implementation process may benefit and run smoother if one focuses especially on
creating a common understanding amongst co-workers, rather than pushing management incentives. Peer
influence is a more significant predictor of technology usage. The training also needs to address the issues of
trust. In the training ‘conferences’ one should focus firstly on creating an arena for personal introduction,
and enhance cohesion by focusing on in-group similarities and shared goals. Secondly, making knowledge
about how and by whom information is produced explicit and thirdly, discuss the availability of data.
The learning model for IO-training in Figure 10 below (Lauvsnes & Korsvold, 2010)4, which incorporates
previous research, our survey findings and the interview findings, is likely to predict high usage of important
IO ICT tools. Also focusing on compatibility issues with the ongoing work processes and the fit between the
task at hand and the technology is of importance. These workshops enable experience transfer, control, and
a sense of participation in the continuous development of work processes and technology usage. The
workshops/meetings should be documented and distributed to ensure organizational memory.
Figure 10: The implementation model for IO-technologies in the oil and gas- industry (Lauvsnes and Korsvold, 2010).
4 The learning model in Figure 10 was developed as part of the survey in Total in 2010 and is fully described in the report Lauvsnes and Korsvold (2010).
30
The main structure of the learning model in Figure 10 consists of the repeated TAM-IO survey as a
diagnostic tool, and the moderated workshops addressing main subjects especially within the categories of
compatibility, HSE and general usability. This structure will ensure experience transfer on both technology
and work processes as well as social influence to use. Although not elaborated issue in this survey, it seems
that traditional Human factor issues are relevant in this case as well. An example of Human factor might be
how to detect an error in the system or the possibility to continue working despite software-errors. These
issues correlate with usability and may thus be seen in relation to the necessary focus on usability in the
continued implementation process.
As mentioned in the result section (Data quality survey, section 4.2) there seems that the respondents have a
tendency to believe more in improvements in work processes, competence and organization than just new
technologies. This is in accordance with findings and experiences from numerous information technology
implementation projects, suggesting that focusing on training on work processes and competence in using
the output of technology is of importance. This also goes to indicate that the interventions suggested in this
report with respect to training are both wanted and necessary.
5.3 Guideline for training
In research parallel to this (Korsvold et.al 2010; Bremdal and Korsvold, 2012) we have developed a method
for characterizing arenas for learning and improvement, called the "4xE Method" (Korsvold et.al. 2010;
Bremdal and Korsvold, 2012). The name refers to Ends (Why-learning), Effectiveness (What-learning),
Efficiency (How-learning) and Efficacy (Doing) and includes tools to evaluate such arenas according to
each of the three Why, What and How (WWH) learning dimensions. The 4xE is an experimental framework
and method that has been created with the aim to improve collective learning. It has been developed
through compilation of a number of case studies in different domains. It focuses on how to make Why,
What and How learning actionable. This method cater for a screening technique that can be used to
characterize arenas, teams and equipment to determine its strength and weaknesses in terms of decision
support an learning. This includes assessments and concerns related to technology, people and physical
settings. An analysis based on this will increase transparency into training and performance improvement
capabilities. This pertains to both day-to-day operations as well as long term improvement. The output of
the screening is the WWH-charts created to analyze and characterize various arenas, organizations/teams
and tools to determine their value with respect to collective learning. The analysis can be supported by a
computer tool that also has been developed. A chart (see example in Figure 10) for each learning dimension
including a set of criteria has been developed and conceptual properties identified in order to support the
analysis. In practice these properties must be customized to fit the domain and the type of analysis it is
intended for.
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31
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32
6 Conclusions and further research
The TAM-IO questionnaire represents aspects of Technology Acceptance that have theoretical coherency
and are highly validated in previous research. This survey replicates already published research concerning
the Technology Acceptance Model to a large extent. The interesting findings that differs in our sample of
highly qualified, experienced technology users in the oil and gas industry, is that the usability/usefulness
aspect is more related to motivation and intent to use the software. Also usability and traditional HSE-issues
are correlated.
Our findings are summed up in a generic model for training that incorporates the findings from the
development of the TAM-IO model and data quality. The next step is now to create and execute these
training-sessions, evaluate them in relation to performance, technology integration and acceptance and on
safety performance. Our data will serve as a point of reference (bench-marks) and guides the process owners
in the companies on what areas to have emphasis on in training sessions.
Later research should look at the results of these sessions, and the participant’s perception of them. Also of
interest is the relationship between actual HSE performance in terms of incidents and the technology
acceptance variables and of course try to replicate our findings in a longitudinal design. Also one should
evaluate the generic model for implementation and training. One interesting comparative design could look
at using our training model in day-to-day operations versus not using it and how it relates to actual amount
of usage. This could be investigated in an intra- or inter-organizational comparative design or as several case
studies with longitudinal follow up. Based on our work it is highly likely that one would see a differential
benefit of the usage of our generic training model with the focuses described above.
33
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Technology, MIS Quarterly, 13, p. 319-340. Dishaw, Strong & Bandy (1999) Extending task technology fit with computer self-efficacy, ACM Database,
37, pp. 96-107. Johnson, C.M., Redmon, W.K., Mawhinney, T.C. eds. (2000) Handbook of organizational performance –
behaviour analysis and management, New York: The Haworth press. Grøtan, T.O., Skarholt, K. and Albrechtsen, E. (2009): How shared situational awareness influence
organizational accident risk in the offshore oil industry, in Reliability, Risk and Safety: Theory and Applications, The Netherlands: CRC Press, ISBN 9780415555098, pp. 2207-2214
Karahanna, Agarawall & Angst (2006) Reconceptualizing Compatibility Beliefs, MIS Quarterly, 30. King W, & He, J. (2006). A meta-analysis of the technology acceptance model, Information and Management. Korsvold, T. & Nystad, E. (2007) Evaluating organizational change, new work processes and collaborative
practice with Next Generation Integrated Drilling Simulator. NTNU/IO-center: report STF50 F07046. Korsvold, T., Madsen, B.E., Bremdal, B., Herbert, M., Nystad, E., Danielsen, J.E. (2010): Creating an
Intelligent Energy Organization through Collective Learning. Paper presented at SPE Intelligent Energy Conference and Exhibition, Utrecht, The Netherlands
Lauvsnes, A.D. (2008) Offshore Customer Survey – 2008. SINTEF report F8732. Lauvsnes A. D., Korsvold T. (2010). Development and Validation of Technology Acceptance Model for
Integrated Operations (IO) - TAM-IO. (SINTEF report F17610) Legris, Ingham & Colorette (2003) Why do people use information technology? A critical review of the
technology acceptance model, Information & Management, 40, pp 191-204. Lurey, J.S. & Raisinghani, M.S. (2001) An empirical study of best practices in teams. Information &
Management, V. 38. Madsen, B-E. (2008) IO-mindset. SINTEF presentation.
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Mathisen, G.E. Einarsen, S., Jørstad, K. & Brønsvik, K. (2004) Climate for work group creativity and
innovation: Norwegian validation of the team climate inventory, Scandinavian Journal of Psychology, 45. Mathieson, Peacock & Chin (2001) Extending the technology acceptance model: the influence of perceived
user resources, Advances in Information Systems, 32, pp. 86-112. Porter & Donthu (2006) Using the technology acceptance model to explain how attitudes determine Internet
usage: The role of perceived access barriers and demographics, Journal of Business Research, 59 , pp. 999–1007.
Robey, D., Bodreau, M-C. & Rose, G.M. (2000) Information technology and organizational learning: a review and assessment of research, Accounting Management and Information technologies, V.10.
Rommetveit, R. Bjørkevoll, K. & Ødegaard, S-I. (2008) Real-time, 3D visualization drilling supervision system
targets ECD, downhole pressure control. Drilling contractor Schepers, J., Wetzels, M. & de Ruyter, K. (2005) Leadership styles in technology acceptance: do followers
practice what leaders preach?, Managing Service Quarterly, 15. Yuan, Y., Fulk, J., Shumate, M., Monge, P. R., Bryant, J. A. and Matsaganis, M. (2005), Individual
Participation in Organizational Information Commons. Human Communication Research, 31: 212–240. Tinmannsvik, R.K., Albrechtsen, E., Bråtveit, M., Carlsen, I.M., Fylling, I., Hauge, S., Haugen, S., Hynne, H.,
Lundteigen, M.A., Moen, B.E., Okstad, E., Onshus, T., Sandvik, P.C. og Øien, K. (2011). Deepwater Horizon-ulykken: Årsaker, lærepunkter og forbedringstiltak for norsk sokkel. SINTEF report A19148
Turner, M., Kitchenham, B., Brereton, P., Charters, S. Budgen, S (2010). Does the technology acceptance
model predict actual use? A systematic literature review, Information and Software Technology, 52, 463-479.
Venkatesh , V and Bala, H. (2008) Technology Acceptance Model 3 and a Research Agenda on Interventions,
Decision Science, 39:2, 273-315. Venkatesh, V. and Davis, F. D. (2000), "A theoretical extension of the technology acceptance model: Four
longitudinal field studies", Management Science, 46(2): 186–204 Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. (2003) User Acceptance of Information
Technology: Toward a Unified View, MIS Quarterly, 27, 3, pp. 425-478 Yi & Hwang (2003) Predicting the use of web-based information systems: self-efficacy, enjoyment, learning
goal orientation, and the technology acceptance model, International Journal of Human-Computer Studies, 59, pp. 431–449.
A
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B Appendix 2: Data Quality Survey questionnaire
Data Quality Survey in drilling and well construction Thank you for participating in our Data Quality Survey in drilling and well construction! An important objective of this survey is to contribute to better understanding of challenges and improvements needed to improve the data quality in drilling and well construction. One striking observation is that the industry of today is apparently tolerant of poor data, and many errors, and consequently wrong decisions are often a result of this data or information.
The survey consists of two parts. The first one is case based and consists of questions regarding the following two cases:
1. "Planning and execution of the operation (re-use/ retrieval ability): Ensure that uncertainty of well bore position is taken into full account, including geological/reservoir uncertainties”
2. “Execution of the operation: Ensure that ECD is below the formation fracture gradient – during drilling”.
The second part of the survey deals with more general questions and statements regarding data quality. This general part start with some questions concerning general experiences and use of modern Information and Communication Technology related to Integrated Operations (IO-ICT), also referred to as decision and collaboration support tools. Examples of IO-ICT might then be video conference, CCTV, UHF, Decision Space, etc.
The results depend on that you will answer as honest as possible. We would appreciate if you could provide your input to the survey as soon as possible. Final deadline to give your answers will be the 1st of July 2011.
This work is carried out as part of activities in drilling and well operations at the Centre of Integrated Operations (IO Centre, http://www.ntnu.edu/iocenter) at NTNU in Trondheim, and furthering education and competence building in the area of IO.
The survey will take around 10-12 minutes to complete, simply click on the ‘Next’ button below to take part. Your answers will be anonymous, but the general conclusions will be presented in a report that will be available to all participants. We hope that you can take the time to participate!
Should you experience any difficulties or need assistance please contact:Torbjørn Korsvold by phone 918 97 508 or email [email protected]. Kind regards Torbjørn Korsvold
i1 - Information about you: q1 Company Aker Drilling (1) Archer Well (2) ConocoPhillips (3) DNV (4) Halliburton (5) IBM Norway (6) Norske Shell (7) Petrobras (8) Seawell (9) Shell International E&P (10) SINTEF (11) Statoil (12) Total E&P Norway (13)
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q2 - What is your main position? Operational (1) Operational support (e.g. HSE, tech. support, drilling optimization, etc.) (2) Management (3) ICT support (4) Other (5) q3 - Years of experience in your current or similar position: 0 - 2 years (1) 3 - 5 years (2) 6 - 10 years (3) 11 - 20 years (4) More than 20 years (5) i4 - Experience and use of IO-ICT Questions concerning general experience and use of modern Information and Communication Technology tools relevant for Integrated Operations (IO-ICT), e.g. video conference, UHF, Decision Space (or other decision and collaboration support tools), that you both use today as well as plan to use in your work on regular basis.
q4_1 - Please indicate to what extent you agree with the following statements:
Disagree
completely (1) Disagree
(2) Neither-nor (3)
Agree (4)
Agree completely
(5) It is/will be easy for me to learn how to use Information and communication technology related to Integrated operations (IO-ICT) (1)
I intend to use IO-ICT (6) Using IO-ICT will increase the level of safety (5) I will enjoy using IO-ICT (4) Using IO-ICTwill increase my effectiveness (3) Using IO-ICT will increase the quality of my work (2)
q4_2 - Team work Please indicate to what extent you agree with the following statements:
Disagree completely (1)
Disagree (2)
Neihter-nor (3)
Agree (4)
Agree completely (5)
We have a high level of trust in our team (1)
Professional diversity and interdiciplinary collaboration strengthen our work (6)
We exploit individual strenghts in our team (5)
Our team has a shared understanding of roles and responsabilities (4)
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i5_a - CASE 1 Regarding inaccuracy* in data quality, please answer for each parameter concerning:
A. frequency of errors or misses in data B. extent, that is quantity or errors or misses in historical data C. consequences of errors or misses in data with regard to:
personal safety (C.1), technical safety (C.2) and economical risk (C.3) for the following case:
"Planning and execution of the operation (re-use/retrieval ability): Ensure that the uncertainty of well bore position is taken into full account, including geological/reservoir uncertainties."
i5_b - *Inaccuracy means error in, missing data. The following statements may explain the meaning of inaccurate data:
• Data exist, but are not available when you need them (you know where to find them) • Data exist, but you don’t know where- In fact - data don’t exist • You may get pieces of data, but not ”the whole package” • You receive data you don’t trust
q5_1 - A. Frequency: How often are these parameters inaccurate*?
Never
(0) Rarely
(1) Sometimes
(2) Often
(3)
Very often (4)
Don’t know (5)
A1: Depth (Measured depth) (1) A2: Direction (azimuth) (2) A3: Angle of the well (Inclination) (3) A4: Location of existing wells (offset wells) (4) A5: Survey tool error models (5) A6: Earths magnetic field, and other magnetic models which is used in positional calculations for magnetic survey tools (6)
A7: Geological uncertainty (7) q5_2 - B. Extent: When these parameters are inaccurate, how large is the inaccuracy?
Accuracy is OK (1)
Small (S) (2)
Medium (M) (3)
Large (L) (4)
Don’t know (5)
B1: Depth (measured depth) (1) B2: Direction (azimuth) (2) B3: Angle of the well (Inclination) (3) B4: Location of existing wells (offset wells) (4) B5: Survey tool error models (5) B6: Earths magnetic field, and other magnetic models - which is used in positional calculations for Magnetic survey tools (6)
B7: Geological uncertainty (7)
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q5_3_1 - C. Consequences - When these parameters are inaccurate, what are the consequences with respect to: C.1: Personal safety?
No consequences
(1) Small (S) (2)
Medium (M) (3)
Large (L) (4)
Don’t know (5)
C1: Depth (measured depth) (1) C2: Direction (azimuth) (2) C3: Angle of the well (Inclination) (3) C4: Location of existing wells (offset wells) (4) C5: Survey tool error models (5) C6: Earths magnetic field, and other magnetic models - which is used in positional calculations for Magnetic survey tools (6)
C7: Geological uncertainty (7) q5_3_2 - C.2: Technical safety?
No consequences
(1) Small (S) (2)
Medium (M) (3)
Large (L) (4)
Don’t know (5)
C8: Depth (measured depth) (1) C9: Direction (azimuth) (2) C10: Angle of the well (Inclination) (3) C11: Location of existing wells (offset wells) (4) C12: Survey tool error models (5) C13: Earths magnetic field, and other magnetic models - which is used in positional calculations for Magnetic survey tools (6)
C14: Geological uncertainty (7) q5_3_3 - C.3: Economical risk?
No consequences
(1) Small (S) (2)
Medium (M) (3)
Large (L) (4)
Don’t know (5)
C15: Depth (measured depth) (1) C16: Direction (azimuth) (2) C17: Angle of the well (Inclination) (3) C18: Location of existing wells (offset wells) (4) C19: Survey tool error models (5) C20: Earths magnetic field, and other magnetic models - which is used in positional calculations for Magnetic survey tools (6)
C21: Geological uncertainty (7)
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i6_a - CASE 2 Regarding inaccuracy* in data quality, please answer for each parameter concerning frequency of errors or misses in data (A), extent, that is quantity or errors or misses in historical data (B), and finally consequences of errors or misses in data (C), with regard to personal safety (C.1), technical safety (C.2) and economical risk (C.3)
for the following case:
"In execution of the operation: Ensure that ECD is below the formation fracture gradient – during drilling”
i6_b - *Inaccuracy means error in, missing data. The following statements may explain the meaning of inaccurate data:
• Data exist, but are not available when you need them (you know where to find them) • Data exist, but you don’t know where- In fact - data don’t exist • You may get pieces of data, but not ”the whole package” • You receive data you don’t trust
q6_1 - A. Frequency: How often are these parameters inaccurate?
Never (1)
Rarely (2)
Sometimes (3)
Often (4)
Very often (5)
Don’t know (6)
A1: ECD (Equivalent Circulating Density) measures (1)
A2: RPM (2) A3: Mud properties (Weight, viscosity, Rheologi, Drilling liquid density (3)
A4: ROP (4) A5: Measuring sensors of tank volume at the surface (5)
A6: Pump rate (6) A7: Flow rate (7) A8: Diameter of hole (8) A9: Diameter of drill pipe, drill collar or BHA (9)
A10: Annular velocity (10) A11: Length of drill pipe, drill collar or BHA (11)
A12: True vertical depth (12) A13: Annular pressure loss (13) q6_2 - B. Extent: When these parameters are inaccurate, how large is the inaccuracy?
Accuracy is OK (1)
Small (S) (2)
Medium (M) (3)
Large (L) (4)
Don’t know (5)
B1: ECD (Equivalent Circulating Density) measures (1)
B2: RPM (2) B3: Mud properties (Weight, viscosity, Rheologi, Drilling liquid density (3)
B4: ROP (4) B5: Measuring sensors of tank volume at the surface (5)
B6: Pump rate (6) B7: Flow rate (7) B8: Diameter of hole (8)
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Accuracy is OK (1)
Small (S) (2)
Medium (M) (3)
Large (L) (4)
Don’t know (5)
B9: Diameter of drill pipe, drill collar or BHA (9)
B10: Annular velocity (10) B11: Length of drill pipe, drill collar or BHA (11)
B12:True vertical depth (12) B13: Annular pressure loss (13) q6_3_1 - C. Consequences - When these parameters are inaccurate, what are the consequences with respect to: C.1: Personal safety?
No consequences (1)
Small (S) (2)
Medium (M) (3)
Large (L) (4)
Don’t know (5)
C1: ECD (Equivalent Circulating Density) measures (1)
C2: RPM (2) C3: Mud properties (Weight, viscosity, Rheologi, Drilling liquid density (3)
C4: ROP (4) C5: Measuring sensors of tank volume at the surface (5)
C6: Pump rate (6) C7: Flow rate (7) C8: Diameter of hole (8) C9: Diameter of drill pipe, drill collar or BHA (9)
C10: Annular velocity (10) C11: Length of drill pipe, drill collar or BHA (11)
C12: True vertical depth (12) C13: Annular pressure loss (13) q6_3_2 - C.2: Technical safety?
No consequences (1)
Small (S) (2)
Medium (M) (3)
Large (L) (4)
Don’t know (5)
C14: ECD (Equivalent Circulating Density) measures (1)
C15: RPM (2) C16: Mud properties (Weight, viscosity, Rheologi, Drilling liquid density (3)
C17: ROP (4) C18: Measuring sensors of tank volume at the surface (5)
C19: Pump rate (6) C20: Flow rate (7) C21: Diameter of hole (8) C22: Diameter of drill pipe, drill collar or BHA (9)
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No consequences (1)
Small (S) (2)
Medium (M) (3)
Large (L) (4)
Don’t know (5)
C23: Annular velocity (10) C24: Length of drill pipe, drill collar or BHA (11)
C25: True vertical depth (12) C26: Annular pressure loss (13) q6_3_3 - C.3: Economical risk?
No consequences (1)
Small (S) (2)
Medium (M) (3)
Large (L) (4)
Don’t know (5)
C27: ECD (Equivalent Circulating Density) measures (1)
C28: RPM (2) C29: Mud properties (Weight, viscosity, Rheologi, Drilling liquid density) (3)
C30: ROP (4) C31: Measuring sensors of tank volume at the surface (5)
C32: Pump rate (6) C33: Flow rate (7) C34: Diameter of hole (8) C35: Diameter of drill pipe, drill collar or BHA (9)
C36: Annular velocity (10) C37: Length of drill pipe, drill collar or BHA (11)
C38: True vertical depth (12) C39: Annular pressure loss (13) q7 - Other cases? According to your experience, describe 1 or 2 concrete situations where data quality problems occured and how it was handled. If possible, include field name, date and well number:
i8 - General questions Please answer the following questions concerning situations with more general data quality challenges (A), critical data quality situations (B), prevention of data quality problems (C), need for improvement in technology or competence (D) and finally some open questions concerning how you define data quality and possible measures to improve data quality (E):
q8 - A. Situations with data quality problems: Please answer how you experienced the data quality problem both in the planning phase of the drilling process (a) and in active operation during drilling (b):
Never (1)
Rarely (2)
Sometimes (3)
Often (4)
Don’t know (5)
1. How often have you experienced situations where you are
49
Never (1)
Rarely (2)
Sometimes (3)
Often (4)
Don’t know (5)
sure data is inaccurate (with mistakes or misses)? a. In planning (1) b. During drilling (2) 2. How often have you experienced situations where you are uncertain data is accurate (with mistakes or misses)? a. In planning (3)
b. During drilling (4) 3. How often have you or your team made a wrong decision because of inaccurate data? a. In planning (5)
b. During drilling (6) 4. How often have you seen or heard about other persons/ teams that have made a wrong decision because of inaccurate data? a. In planning (7)
b. During drilling (8) 5. How often in your experience do we knowingly accept that the data being used in decision making is of low quality? a. In planning (9)
b. During drilling (10) q9 - B. Dangerous or critical situations1. IN PLANNING: How often do dangerous situations (e.g. "outside of design"-situations) occur that could have been prevented with more accurate data?
Never (1)
Rarely (2)
Sometimes (3)
Often (4)
Very Often (5)
Don’t know (6)
Dangerous situations with large ...a. Technical risk (1)
b. Personal risk (2) c. Economical risk (3) q10 - 2.a. DURING ACTIVE DRILLING: How often have you experienced dangerous situations (e.g. "outside of design"-situations) that could have been prevented with more accurate data?
Never (1)
Rarely (2)
Sometimes (3)
Often (4)
Very often (5)
Don’t know (6)
Dangerous situations with large ...a. Technical risk (1)
b. Personal risk (2) c. Economic risk (3) q11 - 2.b: DURING ACITVE DRILLING: How often have inaccurate data made you feel uncertain about..
Never (1)
Rarely (2)
Sometimes (3)
Often (4)
Very often (5)
Don’t know (6)
a. ... the state inside the well? (1) b. ... the technical condition of the equipment inside the well? (2)
q12 - 3. How often have you experienced the following situations?
50
Never
(1) Rarely
(2) Sometimes
(3) Often
(4)
Very often (5)
Don’t know (6)
a. How often have you experienced situations or periods with delayed/ low productivity because of inaccurate data? (1)
b. How often do you think that better contracts and business relationship can be a main driver for improved data quality? (2)
q13 - 4. The value of data: Do you have a well defined process to access the value of a particular data set?Please describe the process and the main challenges in this process:
i14 - C. Prevention of data quality problems
q14 - 1. How often have you experienced "out of design" situations (with potential large risk) that could have been prevented with:
Never (1)
Rarely (2)
Sometimes (3)
Often (4)
Don’t know (5)
a. ... more relevant competence related to quality assurance and interpretation of data? (1)
b. .. better routines related to quality assurance and interpretation of data? (2)
c. ... improved presentation/ visualisation of data (interface, collocation, grouping etc)? (3)
q15 - D. Need for improvement in technology and competence 1. To what extent do you agree or disagree with the statements below?
Disagree completely
(1) Disagree
(2) Neither-nor (3)
Agree (4)
Agree completely
(5) I see that improved (and more expensive e.g. visualisation tools, automation) technology is necessary to carry out the daily job in the right way (correct and optimal performance) (1)
I think that improvement in organization can be an important driver for improved data quality (2)
I think data quality can easily be improved by better technology and better competence (3)
I think that better contracts and business relationships can be a main driver for improved data quality (4)
I think that improvement in work processes can be an important driver for improved data quality (5)
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q16 - 2. In what way can better (but not so expensive) technology related to data quality improve safety or reduce costs?
q17 - E. General open questions1. Please describe according to your opinion what specific measures may lead to significant improvement of data quality, e.g. measures regarding work processes, routines, organization, new technology, competence, experience transfer?
q18 - 2. Please describe how you understand and define the issue of data quality:You may use the following questions as a starting point: How do you define data quality? (describe also a company known definition of data quality) How should deviations from acceptable quality standards be handled?How do you report errors/misses in data?
i19 - You have now answered all the questions in this survey. NB! In order to submit your responses, please click the >> button below. Thank you for taking part in this survey, your response is greatly appreciated and will help us to understand more about data quality.Kind regardsTorbjørn Korsvold, IO Centre at NTNU/SINTEF
C
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questions 9-1
ts from Dat
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D Appendix 4: TAM-IO questionnaire
IO SURVEY
Survey on use of decision and collaboration support tools in drilling and well planning This survey is part of the efforts to increase understanding of what important factors are involved in the successful implementation, and use of decision and collaboration support tools in drilling and well planning. The Drilling and well planning process is characterized by high complexity and accordingly, demanding collaboration between several professions and companies, in a much more integrated way than before. This is part of the industries move towards more widespread implementation of Integrated Operations (IO).
The survey consists of 43 questions concerning the most common IO-relevant decision and collaboration support tools in ConocoPhillips. We expect that this should take around 10 minutes to complete. We would appreciate it if you could provide your input to the survey as soon as possible. Your answers will be anonymous, but the general conclusions will be presented in a NTNU IO-center report that will be available to all participants. We hope that you can take the time to participate!
This work is carried out in connection with ConocoPhillips support of the IO centre at NTNU in Trondheim, and furthering education and competence building in the area of IO. You may choose to pause and return to the survey at any time. Next time you log in, just use the same link that were sent to you. But you have to click yourself forward to the section where you last chose to abort your answering.
Should you have any additional questions, feel free to contact one of the contact persons listed below: Mike Herbert, Conoco Phillips [email protected] Phone: 950 45 019 Torbjørn Korsvold, Sr. researcher/Project Manager, NTNU IO-center [email protected] Phone: 918 07 508
I am a contractor or ConocoPhilips staff Contractor ConocoPhillips staff I have .... years experience from my current or similar positions.. 0-3 years 3-5 years 5-10 years 10-20 years More than 20 years My main team is: Please choose at least one and maximum three, from the list below, teams you spend most of your time during a regular work week and rank them by filling in numbers from 1 to 3 where 1 indicate the team you spend most time. If there are one or more teams you can’t find in this list, please pick “Other” and fill in the missing team(s)
____ Drilling team onshore ____ Drilling team offshore ____ Well integrity and abandonment ____ Well interventions and completions onshore ____ Well interventions and completions offshore ____ Subsurface team ____ HSE&Q team ____ Drilling support team ____ Other (Please specify below)
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If you chose other, please indicate which team:
Questions concerning team work (referring to the team ranked as ’1’ on the previous page)
Agree (1) (2) (3) (4)
Disagree (5)
Our team has a common goal that motivates us to reach our desired results Our team has a shared understanding of roles and responsibilities We exploit individual strengths in our work Professional diversity and interdisciplinary collaboration strengthens our work
Cultural disparity enriches our team We have a high level of trust within our team I see the need to work in a team Team members have mutual respect for each other There is room for posing ’the silly question’ in our team Team members share knowledge and skills for common good of the team Collaboration between on-and offshore
Agree (1) 2 3 4 Disagree (5) n/a Our team has good collaboration between on- and offshore Questions concerning general use of ICT (Information and communication technology)
Agree (1) (2) (3) (4) Disagree (5) I consider myself a skilled ICT user In my work I use: Please indicate for each of the following ICT tools in the list below how often you use it
Several times a day Daily Weekly Less Never Phone Mobile phone Social media (Facebook, Twitter etc.) Microsoft Office Tablet PCs (e.g. iPad) UHF Phone E-mail Physical meetings Video conferencing CCTV (Closed Circuit Television) Other (Please specify bow) If you chose ’other’ above please indicate which:
63
1 Questions concerning use of portable video or camera devices such as Pixavi (formally called Visivear)
Agree (1) (2) (3) (4)
Disagree (5) n/a
It is/I expect that it will be easy for me to learn how to use portable video or camera devices
Using portable video or camera devices will increase the quality of my work
Using portable video or camera devices will increase my effectiveness Using portable video or camera devices will increase the level of safety Using portable video or camera devices will help me handle unwanted incidents
I will enjoy using portable video or camera devices I intend to use portable video or camera devices How often do I use it:
Several times a day Daily Weekly Less Never I use portable video or camera devices What concerns/challenges do you see related to the use of portable video or camera devices? (E.g. related to HSE, training, support, learning and competence development...) Please feel free to formulate your personal opinions
2 Questions concerning use of Desktop Sharing type applications, e.g. Netmeeting, GoToMeeting, Webex (here abbreviated as DS applications)
Agree (1) (2) (3) (4) Disagree (5) n/a It is/It will be easy for me to learn how to use DS applications Using DS applications will increase the quality of my work Using DS applications will increase my effectiveness Using DS applications will increase the level of safety Using DS applications will help me handle unwanted incidents I will enjoy using DS applications I intend to use DS applications
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How often do I use:
Several times a day Daily Weekly Less Never I use DS applications What concerns/challenges do you see related to the use of DS applications? (E.g. related to HSE, training, support, learning and competence development...) Please feel free to formulate your personal opinions
3 Questions concerning use of newer and future Visualisation and advisory systems, e.g. eDrilling, Verdande, Drillscene, Decision Space (here abbreviated as VAS)
Agree (1) (2) (3) (4) Disagree (5) n/a It is/It will easy for me to learn how to use VAS Using VAS will increase the quality of my work Using VAS will increase my effectiveness Using VAS will increase the level of safety Using VAS will help me handle unwanted incidents I will enjoy using VAS I intend to use VAS How often do I use:
Several times a day Daily Weekly Less Never I use VAS What concerns/challenges do you see related to the use of future VAS? (E.g. related to HSE, training, support, learning and competence development...) Please feel free to formulate your personal opinions
4 Questions concerning use of more established Visualisation and Collaboration Tools, e.g. Vispo 3D and EDM (here abbreviated as VCT)
Agree (1) (2) (3) (4) Disagree (5) n/a It is/It will easy for me to learn how to use VCT
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Agree (1) (2) (3) (4) Disagree (5) n/a Using VCT will increase the quality of my work Using VCT will increase my effectiveness Using VCT will increase the level of safety Using VCT will help me handle unwanted incidents I will enjoy using VCT I intend to use VCT How often do I use:
Several times a day Daily Weekly Less Never I use Viewer Collaboration Tools What concerns/challenges do you see related to the use of VCT? E.g. related to HSE (Health-Saftey-Environment), training, learning, support and competence development. Please feel free to formulate your personal opinions.
5 Questions concerning use of Real time information systems (RTIS), e.g. Insite from Halliburton and Interact for Schlumberger
Agree (1) (2) (3) (4) Disagree (5) n/a It is/It will easy for me to learn how to use RTIS Using RTIS will increase the quality of my work Using RTIS will increase my effectiveness Using RTIS will increase the level of safety Using RTIS will help me handle unwanted incidents I will enjoy using RTIS I intend to use RTIS How often do I use:
Several times a day Daily Weekly Less Never I use RTIS What concerns /challenges do you see related to the use of RTIS? (E.g. related to HSE, training, support, learning and competence development...) Please feel free to formulate your personal opinions.
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A few questions concerning general use of the ICT tools mentioned in this survey (ICT includes decision and collaboration support tools)
Agree (1) (2) (3) (4) Disagree (5) Management expect me to use the mentioned ICT tools Co-workers expect me to use the mentioned ICT tools The use of the mentioned ICT tools is voluntary Additional comments Feel free to give any additional comments. We encourage you share with us any thoughts you might have, positive or negative, regarding e.g. the implementation process towards Integrated Operations in your department or team, or the survey itself like missing questions or improvement of questions, how the survey has been carried out, expectations for follow-up, etc
We thank you for your participation. We will give you feedback on the general findings as soon as they become available.
