Raúl Carrasco

Manuel Vargas

Ismael Soto

Leonardo Banguera

Guillermo Fuertes
email: guillermo.fuertes@usach.cl

Chaotic Time Series for Copper's Price Forecast Neural networks and the discovery of knowledge for big data

Keywords: Big Data, Copper Price, Chaos Theory, Neural Network, Nonlinear Systems, Time Series Forecasting

We investigated the potential of Artificial Neural Networks (ANN), ANN to forecasts in chaotic series of the price of copper; based on different combinations of structure and possibilities of knowledge in big discovery data. Two neural network models were built to predict the price of copper of the London Metal Exchange (LME) with lots of 100 to 1000 data. We used the Feed Forward Neural Network (FFNN) algorithm and Cascade Forward Neural Network (CFNN) combining training, transfer and performance implemented functions in MatLab. The main findings support the use of the ANN in financial forecasts in series of copper prices. The copper price's forecast using different batches size of data can be improved by changing the number of neurons, functions of transfer, and functions of performance s. In addition, a negative correlation of -0.79 was found in performance indicators using RMS and IA.

Introduction

Copper is one of the basic metal products listed on major exchanges in the world: the LME, Commodity Exchange of New York (COMEX) and Shanghai Futures Exchange (SHFE). Prices in these exchanges reflect the balance between the supply and demand of copper worldwide, although they may be strongly influenced by the rates of currency exchange and investment flows, factors that may cause fluctuations of volatile prices partially linked to changes in the economic cycle activity [START_REF] Roberts | Duration and characteristics of metal price cycles[END_REF].

The price of copper is a sensitive issue for major producers such as Codelco, Freeport-McMoRan Copper & Gold, Glencore Xstrata, BHP Billiton, Southern Copper Corporation, American Smelting and Refining Company. Economies such as those of Chile and Zambia rely heavily on copper production and, subsequently, in the evolution of the prices of the same [START_REF] Sánchez Lasheras | Forecasting the COMEX copper spot price by means of neural networks and ARIMA models[END_REF], being Chile the largest producer and exporter of the world.

Several studies include copper as one of the products of interest in the evaluations of the forecast to improve the prediction of prices. They employ a variety of different methods and mathematical models: time series [START_REF] Dooley | An assessment of time series methods in metal price forecasting[END_REF][START_REF] Carrasco | Forecast of copper price series using vector support machines[END_REF][START_REF] Seguel | A Meta-Heuristic approach for copper price forecasting[END_REF],combined with wavelet [START_REF] Kriechbaumer | An improved wavelet-ARIMA approach for forecasting metal prices[END_REF][START_REF] Goss | Simultaneity, forecasting and profits in London copper futures[END_REF], transformed of Fourier [START_REF] Khalifa | Return distributions and volatility forecasting in metal futures markets: Evidence from gold, silver, and copper[END_REF], swarm optimisation algorithm [START_REF] Ma | Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm[END_REF], and models of multiproducts [START_REF] Cortazar | Can oil prices help estimate commodity futures prices? The cases of copper and silver[END_REF].

A fairly accurate time series model could predict several years forward, whose skill is an advantage for the planning of future requirements. Research on nonlinear dy-namical systems has allowed in recent years significantly improve the impact of the predictive capacity of the times series. Chaos is a universal complex dynamic phe-nomenon that exists in different natural and social systems such as communication, economics and biology. The auto-correlation of chaotic behaviour in the economy began in the decade of 1980, applied to macroeconomic variables such as the gross domestic product (GDP) and monetary aggregates [START_REF] Lebaron | Chaos and nonlinear forecastability in economics and finance[END_REF]. Since then, several studies have been conducted to search chaotic behaviour in economic and financial series [START_REF] Los | Visualization of chaos for finance majors[END_REF][START_REF] Los | Persistence characteristics of the Chinese stock markets[END_REF].

In this context, amongst the most used techniques and tools are graphics analysis of recurrence, Temporal Space Entropy (STE) Hurst coefficient and exponent of the Lyapunov dimension of correlation for the matching of chaotic behaviour in these series [START_REF] Espinosa | Caos en el mercado de commodities[END_REF]. Additionally, the existence of chaotic behaviour in the commodities of copper was corroborated in [START_REF] Carrasco | Copper Metal Price Using Chaotic Time Series Forecating[END_REF].

The motivations and the need to carry out this research is to evaluate the ANN by different dimensions, type of networks, functions and structures.

Methodology

The continuous increase of the power of calculation and availability of data has grown the attention in the use of ANN in many types of problems of prediction. The ANN can model and predict linear and nonlinear time-series with a high degree of accuracy, to capture any relationship between the data without prior knowledge of the problem which is modelled [START_REF] Kourentzes | Neural network ensemble operators for time series forecasting[END_REF].

The methodology used for visual and statistical analysis is summarised according to the Fig. 1 adapted the Loshin [START_REF] Loshin | Chapter 5 -The Business Intelligence Environment[END_REF].

The closing prices of copper traded on the LME were used with the different functions of learning, optimisation and transfer as input. Then, the quality of the forecast of the ANN performed in MatLab R2014a simulation tool was evaluated with Root Mean Square (RMS) and the Adequacy Index (IA). The saved data goes through the process of Extract, Transform and Load (ETL) to be stored in the Data Warehouse (DW) in a multi-dimensional form, using SQL Server 2008. The visual analysis of the results is done by the software BI Tableau Desktop 8.3 as Frontend, and the R version 3.1.2 software to perform the corresponding statistical analysis. Segmentation according to the evaluated batch records is according to the Table 1.

Artificial Neural Networks

Represented FFNN has been taken to this work in the Fig. 2(a) and the represented CFNN in the Fig. 2(b). We have selected some of the features available in the toolbox of Matlab software. The inputs used in the system correspond to 𝑡 -1 , 𝑡 -2 , 𝑡 -3 , 𝑡 -4 , 𝑡 -1 , 𝑡 -6 and target 𝑡 0 the copper price time series.

Alternatives of Evaluation

The different alternatives of a network are given according to different combinations between their functions as shown in equation [START_REF] Roberts | Duration and characteristics of metal price cycles[END_REF]. n, It is the number of neurons in the hidden layer.

Replacing values in the equation ( 1) gets the number of alternatives for a network.

M=9*2*3*20=1080

To calculate the total number of alternatives evaluated in this work is obtained from the equation [START_REF] Sánchez Lasheras | Forecasting the COMEX copper spot price by means of neural networks and ARIMA models[END_REF].

𝑇 = 𝑀 * 𝑁 * 𝐿 (2)
where, 𝑇, It is the number of evaluated alternatives. 𝑁, It is the number of networks (structure of similar functions) 𝐿, It is the number of batches of data.

Replacing values in the equation ( 2) obtained 4320 alternative evaluated in this work.

𝑇 = 1080 * 2 * 2 = 4320

Were 27 simulations for each of the 4320 alternatives evaluated in this work with a total of 116640 simulations according to the equation [START_REF] Dooley | An assessment of time series methods in metal price forecasting[END_REF].

𝑇 𝑠 = 𝑇 * 𝑆 (3)
Where, 𝑇 𝑠 , It is the total number of simulations carried out. 𝑆, It is the number of simulations for each alternative.

Multidimensional Model

This research focuses on the evaluation of two networks simultaneously from different dimensions. The FFNN and the CFNN in dimRed, training features (trainbfg, traincgb, traincgf, traincgp, traingd, traingdm, traingda, traingdx, trainlm) in dimFuncTrain, in dimFuncTransfer (logsig, tansig, purelin) transfer functions, functions (mse, sse) performance in dimFuncPerform. Number them 1 to 20 neurons, with batches of 100 and 1,000 data. The function of learning was the learngdm and the metrics of evaluation [RMS, IA, bestEpoch, duration] in fact table.

Fig. 3 shows the "star model" [START_REF] Howson | Successful Business Intelligence: Secrets to Making BI a Killer App[END_REF] used to perform multidimensional analysis according to the methodology proposed by Ralf Kimball [START_REF] Sherman | Chapter 6 -Data architecture[END_REF]. The construction of the DW was based on multidimensional modelling, in which the structures of the information on table are facts and dimensions [START_REF] Soler | Building a secure star schema in data warehouses by an extension of the relational package from CWM[END_REF].

2.5

Performance Measures

𝑅𝑀𝑆 = √ ∑ (𝑜 𝑖 -𝑝 𝑖 ) 2 𝑛 𝑖=1 ∑ 𝑜 𝑖 2 𝑛 𝑖=1 (4) 𝐼𝐴 = 1 - ∑ (𝑜 𝑖 ′ -𝑝 𝑖 ′ ) 2 𝑛 𝑖=1 ∑ (|𝑜 𝑖 ′ |-|𝑝 𝑖 ′ |) 2 𝑛 𝑖=1 (5)
The results were validated using performance measures that allow indicating the degree of generalisation of the model used. The indexes are the average quadratic Error (RMS), and the index of adequacy (IA). Both are shown in the equations ( 4) and ( 5), respectively, where 𝑜 𝑖 and 𝑝 𝑖 are the values observed and predicted respectively at the time 𝑖, and 𝑁 is the total number of data. In addition, 𝑝 𝑖 ′ = 𝑝 𝑖 -𝑜 𝑚 and 𝑜 𝑖 ′ = 𝑜 𝑖 -𝑜 𝑚 , being 𝑜 𝑚 value average of the observations [START_REF] Zambrano Matamala | Análisis de rendimiento académico estudiantil usando data warehouse y redes neuronales[END_REF].

IA indicates the degree of adjustment that has the values estimated with the actual values of a variable. A value close to 1 indicates a good estimate. On the other hand, near-zero RMS indicate a good quality setting.

Results

Knowledge Discovery

The knowledge is in the data, however, must be extracted and built for the consumption of the users. New technological solutions are required for data management. The new software and methodologies are the solution to make sense of the data, allowing to extract useful information for the construction of knowledge, supporting decision making. Data from markets together with the new data generated from different data mining techniques, can help to the analysis and the management of Big Data [START_REF] Xhafa | Big data knowledge discovery[END_REF][START_REF] Lagos | Big Data on Decision Making in Energetic Management of Copper Mining[END_REF]. In this case, the business intelligence tool enables the discovery of knowledge. Fig. 4 shows the best results obtained in the average assessment (RMS) ̅ in lots of 100 data FFNN. The indexes are shown in a scale of colors, where the blue colour indicates better value performances corresponding to the minimum of the RMS and the red colour lower performance. After several iterations were obtained the best results to correspond to functions purelin, traincgb, traincgf, traincgp, mse and sse between 1 and 5 neurons. (𝐴𝐼) in the FFNN average assessment indicates a better fit in the curves, for this case are kept the same functions and expands the range of neurons from 1 to 20.

In the case of the FFNN network for batches of 1000 data, the best results of 𝑅𝑀𝑆 ̅̅̅̅̅̅ and 𝐴𝐼 ̅̅̅ they correspond to the functions purelin, trainlm, mse and sse with ranges of neurons from 6 to 10 in Table 2. In the case of CFNN for batches of 100 data, the best results for the averages of 𝑅𝑀𝑆 ̅̅̅̅̅̅ and 𝐴𝐼 ̅̅̅ they correspond to the functions purelin, traincgb, traincgf, traincgp, mse and sse between 1 and 2 neurons in Table 3. CFNN batches of 1000 data, for best results unemployment averages 𝑅𝑀𝑆 ̅̅̅̅̅̅ and 𝐼𝐴 ̅̅̅ they correspond to the functions purelin, trainlm, mse and sse with ranges from 3 to 4 neurons in Table 4.

We found that FFNN and CFNN for batches of 100 data obtained the best results with the training functions named traincgf, traincgb, and traincgp. On the other hand, for batches of 1000 data, the best function of training was the trainlm in FFNN and CFNN.

Forecast

The charts show the forecast made by the FFNN and chaotic CFNN in series of the copper's price (Fig. 5a, Fig. 5b, Fig. 5c and Fig. 5d). FFNN and CFNN show similar values in his performance's ratings.

Statistic Analysis

The CFNN (trainFcn = trainlm, performFcn = sse, transferFcn = purelin, learnFcn = learngdm, lot = 1000, numNeuronas = 3) simulo up to 10000 simulations, where obtained good results in the performance of the network, with average measures of RMS of 0.00767957, IA of 0.9725127 and best_epoch of 3.1376; minimum values of 0.007535554; 0.9698627 and 1 respectively, maximum values of 0.007712344; 0.9731063 and 11 respectively, in the Table 5 the statistical summary of the simulation.

To review the distribution of RMS performance measures is observed which is skewed to the left Fig. 6 a, Therefore, the bias is negative (less than the median average); Instead the IA is skewed to the right index Fig. 6 b, therefore, the bias is positive (greater than the median average) and the best_epoch this biased right in Fig. 6 c. On the other hand the correlations (Pearson) graph shows a high negative correlation of -0.79 between RMS and the IA which shows the good performance of the ANN indicated by RMS will also be it according to the IA. On the other hand presents a negative correlation of -0.49 between IA and best_epoch; and a positive correlation of 0.52 between RMS and best_epoch.

Conclusions

Forecasts based on neural network, nonlinear models achieved better results compared with linear forecasting models. Combinations of two neural network models, with nine training functions, two functions of performance, lots of 100 to 1000 data and ranges from 1 to 20 neurons and three transfer functions have been studied. In future work, it is necessary to review the contribution of other macroeconomic variables in the performance of the model, in particular, market of capitals of other commodities.

Finally, the characteristics of these time series, should be analysed the same set of combinations of functions in other economic series of price, to assess the possible relationships of the systems involved.

Fig. 1 .

 1 Fig. 1. Methodological graph based on methodology of Business Intelligence (BI).

  Data were segmented into two sizes of batches. The former with 100 and the later with thousand data. Periods from 22/01/2015 to 16/06/2015 and the 01/07/2011 to 16/06/2015 respectively.

Fig. 2 .

 2 Fig. 2. (a) FFNN of 1-20 neurons in the hidden layer. (b) CFNN of 1-20 neurons in the hidden layer [18].

Fig. 3 .

 3 Fig. 3. Star model for multidimensional analysis of project.

Fig. 4 .

 4 Fig. 4. Final result and visual analysis of the FFNN, batch =100.

Fig. 5 .

 5 Fig. 5. a)Forecast of the FFNN for data verification with lot of 100, purelin, traincgp and sse of 5 neurons functions; b) Forecast of the FFNN for data verification with lot of 1000, purelin, trainlm and sse of 7 neurons functions; c) Forecast of CFNN for data verification with lot of 100, purelin, traincgb and sse of 1 neurons functions; d) Forecast of CFNN for data verification with lot of 1000, purelin, trainlm and mse of 3 neurons functions.

Fig. 6 .

 6 Fig. 6. a) distribution of RMS, b) distribution of IA, c) distribution of best_epoch and d) Plot of correlations between IA and RMS best_epoch.

Table 1 .

 1 Segmentation's Records.

	Batch	Training	Testing	Verification
	Theoretician	First 70% of the data	15% next data	15% of the final data
	100	[1;70]	[71;85]	[86;100]
	1000	[1;700]	[701:850]	[851;1000]

Table 2 .

 2 FFNN for batches of 1000, functions purelin, trainlm, mse and sse with ranges of neurons from 6 to 10.

	Perform	Neurons	𝑅𝑀𝑆 ̅̅̅̅̅̅	𝐼𝐴 ̅̅̅
	mse	6	0.007682365	0.97249718
	mse	7	0.007676144	0.972600372
	mse	8	0.007686618	0.972512072
	mse	9	0.007678558	0.972517947
	mse	10	0.007682348	0.972498224
	sse	6	0.007680212	0.972510564
	sse	7	0.00768904	0.972449236
	sse	8	0.007678767	0.972536585
	sse	9	0.007675839	0.972631146
	sse	10	0.00768069	0.972486787

Table 3 .

 3 CFNN for batches of 100, purelin, traincgb, traincgf, traincgp, mse and sse between 1 and 2 neurons functions.

		Mse		mse	sse	sse
	trainFcn	n	𝑅𝑀𝑆 ̅̅̅̅̅̅	𝐼𝐴 ̅̅̅	𝑅𝑀𝑆 ̅̅̅̅̅̅	𝐼𝐴 ̅̅̅
	traincgb	1 0.008416036	0.999978052	0.008722153	0.999976384
	traincgb	2 0.008596207	0.999977127	0.008489567	0.999977647
	traincgf	1 0.008737803	0.999976242	0.008464134	0.99997776
	traincgf	2 0.008556195	0.999977299	0.008739409	0.999976321
	traincgp	1 0.008581721	0.999977098	0.008644758	0.999976784
	traincgp	2 0.008634526	0.999976813	0.008536383	0.999977431

Table 4 .

 4 CFNN for 1000 batches, functions purelin, trainlm, mse and sse with ranges from 3 to 5 neurons.

	Perform	Neurons	𝑅𝑀𝑆 ̅̅̅̅̅̅	𝐴𝐼 ̅̅̅
	mse	3	0.007678561	0.972558102
	mse	4	0.007684607	0.972423949
	mse	5	0.007675506	0.972534376
	sse	3	0.007670738	0.972539376
	sse	4	0.007681384	0.972468666
	sse	5	0.007683084	0.97254968

Table 5 .

 5 Statistical simulation.

	Perform	RMS	IA	best_epoch
	mean	0.00767957	0.9725127	3.1376
	sd	0.00002461	0.000225216	1.417486
	min	0.007535554	0.9698627	1
	25%	0.007671562	0.9723753	2
	50%	0.007693985	0.9723813	3
	75%	0.007694727	0.9726121	4
	max	0.007712344	0.9731063	11
	n	1000

Acknowledgment. The authors are acknowledgment the financing of the project "Multiuser VLC for underground mining", code: IT17M10012 and this research has been supported by DICYT (Scientific and Technological Research Bureau) of The University of Santiago of Chile (USACH) and Department of Industrial Engineering.


Stuart Moran
email: stuart.moran@nottingham.ac.uk

Sophie Berckhan
email: sophie.berckhan@nottingham.ac.uk

Alison Clarke
email: alison.clarke@nottingham.ac.uk

A Framework to Support the Design of Digital Initiatives in Social Science Based Research

Keywords: Academic Support, Digital Research, Digital Transformation

The rapid development of new digital technologies has increased the expectations of academic research outputs. To meet these pressures, researchers have greater expectations of their digital work environments. In this paper we outline the drivers for digital transformation at our institution and how this manifested itself in a series of exploratory projects called digital initiatives. Based on our findings we propose a Digital Research Initiative framework to support research institutions in assessing and implementing digital transformation.

Introduction

For the past decades, academic institutions have undergone rapid change driven by globalisation, greater social mobility, new funding patterns and the introduction of new technologies [START_REF] Altbach | Trends in Global Higher Education : Tracking an Academic Revolution[END_REF]. This has led to a change in the way institutions operate, moving away from a self-centred to a self-determined model and environment [START_REF] Gewirtz | Representing 30 years of higher education change: UK universities and the Times Higher[END_REF]. More recently, several new trends have emerged relating to the scale at which research is undertaken, the need to evidence the public benefit of research and a changing set of government and funder regulatory requirements. This has placed new pressures on researchers to deliver high-quality results, at a fast pace, with real-world relevance and in compliance with regulations. Consequently, researchers have ever increasing expectations of their institution to offer the appropriate tools and environment that enable them to respond to these pressures. This is perhaps most prominent in terms of the Information Technology (IT) provision, with researchers expecting timely access to the very latest and ever changing digital tools and services. This has created new opportunities for institutional IT services to make a shift from being a purely operational function, toward a strategic enabler and leader. This role change will result in digital services that not only continue to support research but also actively inspire new research possibilities.

In this paper we seek to outline the formation of our institution's digital research vision and explore how this manifested itself through a series of small-scale digital transformation projects specific to Social Sciences. Based on our findings we then propose a framework that can support institutions in assessing their readiness for digital transformation and broadly inform decision making on the types of solution to implement.

The Need for Digital Transformation

Prior to exploring our institution's digital transformation vision, we must first understand more clearly the broader research context and drivers of change:

Transparency and Compliance. Research councils and funders are placing greater emphasis on the need for research transparency and open access to research findings. This will also include in the requirement that research data be suitably archived and preserved for the long term [START_REF]Concordat on Open Research Data[END_REF]. In terms of the management of data, the General Data Protection Regulation (GDPR) also places new constraints on the way personally identifiable research data is acquired and managed [START_REF] Chassang | The impact of the EU general data protection regulation on scientific research[END_REF]. Cybersecurity is also a major consideration particularly in light of sensitive research data topics, commercial partners and patentable research outputs.

Collaboration and Skills.

A stronger emphasis is now placed on large scale critical mass research driven, amongst others, by the Global Challenges Research Fund [5]. These are often addressed by multi-disciplinary and multi-institutional consortia driving the need to work collaboratively on larger projects. To be competitive research groups must continually refine their areas of expertise and promote their ability to add value in interdisciplinary projects more clearly. Interestingly, this behaviour means that while researchers are actively exposed to new techniques and knowledge from other disciplines, they do not necessarily need to apply and acquire those skills for themselves. This puts groups with specialist skills in high-demand [START_REF] Davé | Landscape review of interdisciplinary research in the UK[END_REF], creating bottlenecks in interdisciplinary research Digital Equipment and Data. Recent developments in precision manufacturing and technology development have made numerous highly specialised instruments available to researchers [START_REF] Brynjolfsson | The second machine age: Work, progress, and prosperity in a time of brilliant technologies[END_REF]. The high cost of these equipment and services means effective resource sharing is required. This has led to the consolidation of research equipment into large centres and shared regional and national facilities. The Wakeham Review [START_REF] Review | Wakeham Review of STEM Degree Provision and Graduate Employability[END_REF] concluded that shared resources and services will provide opportunities for better science. However, as instruments can generate significant volumes of data, their centralisation has created new challenges about the speed and safety of moving this data between institutions. Furthermore, the amalgamation of equipment has also further consolidated the availability of expertise [START_REF] Van Der Wende | International academic mobility: towards a concentration of the minds in Europe[END_REF].

Impact and Metrics.

With public funds used to support research, there is a growing need to demonstrate the public benefit of research outputs. These developments have led to a greater emphasis on research metrics with a focus on the production of outputs with the highest possible influence. The presentation of research outcomes to the public has created opportunities for recognition outside of academic disciplines through a range of media channels [START_REF] Stilgoe | Why should we promote public engagement with science?[END_REF].

Underpinning Digital Capabilities. Institutions are in an intriguing situation where digital technologies and services both serve as the cause of the changing environment but also as the route to a viable solution. Research funders have an expectation that institutions will provide researchers with a "well-founded laboratory" that includes IT infrastructure and services. Many institutions are in a position where the digital demand and expectations of researchers currently outweighs their ability to offer the capabilities. Without a step-change in the digital provision, institutions risk not being able to serve what is becoming a basic research need and will fail to support researchers in fulfilling their research ambitions. This would in turn reduce an institutions research income, power and reputation.

Informing the Digital Research Strategy

In response to the changing work environment of researchers, our institution took steps toward creating a research environment that not only supports researchers' current needs, but also actively encourages them to increase the scope of their ambitions. A digital research vision was formed that recognized that the existing digital service provision was no longer viable, and that there was an opportunity for the IT function to not only enable, but to actively create innovative research. The vision outlined a research environment that would not only facilitate high quality research, but ultimately change the culture and working practices of researchers. While at a high level, the areas that needed investment were broadly known, they were ill-defined and poorly understood. This is in part due to communication and knowledge barriers between IT services and the highly specific and complex research areas across the institution. Without a means of bridging this gap, the institution was not in a position to deliver an appropriate research environment. In response, a small innovative and strategic Digital Research Team was established within IS, made up of specialist post-doctoral researchers each with a portfolio of ambitious digital research projects. The collective research experience of the team covered all five faculties of the institution. It was anticipated that this unique combination of research knowledge and experience would allow researchers' needs to be more effectively captured and articulated to the IT delivery teams. Furthermore, these roles would provide the expertise to challenge and inspire researchers with new digital research opportunities. A survey of Russel Group universities revealed a focus on digital humanities centres, academic-led digital innovation centres, or specific roles such as research software engineers, data managers and data scientists. There were no indications that a similar institution-wide strategic digital research team is currently in place at these institutions. In order to establish the specific research needs of the institution, researchers across each faculty were approached by the Digital Research Team. The remainder of this papers focuses on the findings of the Faculty of Social Sciences as due to the broad range of disciplines it suitably represents the challenges faced across the institution.

Digital Research Environment in Social Science

At our institution Social Science is an exceptionally broad field, with a range of research methods applied across different disciplines in different contexts. In order to better understand the 'digital-readiness' of the faculty, approximately 100 researchers were met with over a 6-month period. This was on a one-to-one basis and intended to establish (1) the types of research methods adopted (2) the digital barriers faced by researchers and (3) an outline of the long-term research ambitions. Following these discussions, three key themes emerged around digital skills, digital systems and digital support.

Digital Skills in Research. As research technologies have advanced over the last 15-20 years, a digital divide has emerged, with those who make use of technologies to their fullest, and those who miss opportunities. Particularly in the qualitative space, there appears to be either a misunderstanding as to the broad capabilities of current technologies, or a perception that technology cannot support their work. For those who engage in quantitative work, there are many challenges around selecting the most computationally efficient technique or hardware platforms to use for a given dataset. Where programming is required, researchers are generally self-taught and often viewed their code and statistical modules as inefficient. This was often cited as the reason for not sharing these research outputs more widely. Without a complete understanding of the research possibilities with the latest digital tools, techniques and technologies, researchers are missing opportunities to do more with their research with fewer resources. However, it is the barriers to entry, not the ambition, that frequently outweigh the digital opportunities available.

Digital Systems in Research.

There is a large variety of data being created and captured by researchers, both in terms of their file formats and the degree of sensitivity of their content. Researchers are offered many different storage solutions by our institution considering their data requirements. However, a consequence of being able to satisfy this range of needs is that identifying the appropriate solution is difficult, creating barriers for researchers. Furthermore, in order to practically and safely share research data with the community, researchers are required to adhere to a cumbersome controlled access process, which acts as a barrier to sharing data. In some extreme instances, researchers have chosen not to pursue lines of enquiry due to the inefficient and uncertain storage mechanisms. Computationally intensive research is often completed using 'always-on' local desktop machines, running for days at a time. This is because the high performance computer at our institution not only requires a set level of expertise to access and utilize, but is also limited exclusively to the use of the Linux operating system. There are also a range of digital devices that can support the capture of research data that are underutilized.

Research findings and opportunities are missed as a consequence of data not being stored or data being unusable due to poor management. Similarly, without the appropriate compute facilities, key insights into large data sources continue to be difficult to elicit or simply unachievable.

Digital Support in Research. Researchers are broadly unaware of the types of professional support that is already available to them, in areas such as social media, digital research, data management and intellectual property. Some support services are run by researchers for researchers but gaining timely access to these can be difficult due to limited resources. There are also no current services available to help develop small scale code pilots or advise on specific statistical technologies. While the institution does provide state-of-the-art computational facilities and guidance on its use, there is a perception that this is tailored toward expert users. Without support and awareness of the use of new research technologies and techniques, researchers are unlikely to adopt them, resulting in missed opportunities and less competitive grant applications.

Digital Research Initiatives

Following the elicitation of researcher needs across the institution, three key strategic areas for investment were identified: Research Data Management, Compute and Analytics and Communication and Collaboration. The aim was to introduce new digital technologies and services that support researchers in each of these key areas which support and challenge the working practices and culture of research. In order to explore what digital technologies and services are likely to be most successful for researchers, a small scale rapid-delivery approach was adopted. These projects, or digital research initiatives (DRIs), seek to introduce and explore innovative research methods, off-theshelf and custom technologies, and specialist knowledge. The DRI's are essentially meta-research projects with the goal of exposing researchers to new ways of working and technologies, and directly demonstrating their value. In order to secure buy-in from researchers, the DRI's were designed to align with existing practices where possible, and produce research outputs such as publications and grant applications. The outcomes of the projects were then used as case studies to inspire the wider research community to engage in new ways of working. In the next section we discuss some of the most successful DRI's implemented in Social Science in terms of Skills, Systems and Support based initiatives. As these projects are currently unpublished research they have been consciously obfuscated and described at a high level.

Skills-Based Initiatives

The following interventions were focused on demonstrating the value of how advanced digital skills can lead to new research opportunities. In both instances, new skills were made available to researchers through targeted collaborations with digital experts.

Agent Based Modelling.

A group of Geographers had built a large longitudinal dataset over a 30-year period and carried out typical statistical analysis on the data. Given the size and content of the dataset the researchers were not realizing the full potential of their data. An opportunity was identified to apply a new research method and analytical technique, which was outside the skillset of the researchers. A specialist research software engineer was injected into the team and developed a prototype agent-based model that was grounded in the data. This model was then run using our institution's highperformance computer, creating new data and insights for the researchers to work with. This exposure to new digital possibilities led to an increase in their research ambition.

Virtual Reality. Digital technologies can be extremely effective at engaging people in ideas, particularly in immersive experiences. A specialist virtual reality (VR) expert was sought to collaborate with Politics and Law researchers on developing an engaging experience. A portable VR game was developed from the ground-up with the intent of exposing players to the sensitive topics in an interactive and playful way. The system was designed to present players with different introductory instructions (independent variable) and capture in-game behaviours and decisions (dependent variable). Based on the players actions, a set of tailored outcomes are automatically generated and used to stimulate post-experience discussions. By using an immersive game, players will be able to better reflect on their experiences and perceptions, creating higher quality interview data.

System-Based Initiatives

The following interventions were designed to introduce new technologies for capturing, analysing and showcasing research data. Primarily these off-the-shelf and custom systems enhance the management of text, audio and image-based research data.

Optical Character Recognition. Many researchers work with paper-based research data, including historical records, newspapers and legislation. The challenge for researchers is the speed at which this data can be searched through, and the ability to hold an accurate mental model of the entire contents. Furthermore, the data is not easily shareable, and is at risk of accidental loss. Optical character recognition (OCR) essentially makes any text machine readable and the researchers were not aware of this possibility. By using a scanner and a text analytics tool, researchers were able to not only accurately search through the data, but also create statistical information a linguistic analysis. This transformed the nature of the research, with the ability to query the data in new and unprecedented ways.

Handwriting Technologies. The most widely used form of paper-based research data is in the form of hand written notes. These are made by field-based researchers making observations or notes around interviews. Sharing, searching and securing these notes can be a challenge, particularly given the amount that can be generated through ethnography. In recognition of this, a range of off the shelf 'smart pen' devices were trialled with researchers. These devices create digital copies of hand written notes using real paper and ink. Unique codes are printed onto each page, which allows the user to retroactively change the content of pages physically and digitally. Other than charging the device, there is no change to the practice of taking notes, meaning all the digital benefits are achieved with no barriers. The integrity of the data is protected and becomes easily searchable and shareable. The technology can even record audio, pairing digital notes with the audio files.

Automated Transcription.

Researchers typically spend time transcribing their audio interviews themselves or pay for a transcription service to transcribe the data. The consequence of these resource (time/cost) heavy tasks is that the transcription of audio files is done on a priority basis. This means significant amounts of research data are not being fully utilized or shared; essentially the information is lost. Using Azure cognitive services and working with a software developer, a customized solution was developed that allows researchers to save audio files to their computer and automatically return a transcript. This can be done in bulk, meaning all existing audio files can be transcribed and even translated automatically. This then creates the opportunity for researchers to effectively search through their audio data and even conduct quantitative analysis.

Support-Based Initiatives

The following interventions were intended to explore the role of new specialized support services. Their main aim is to offer consultancy to researchers on the effective use of digital tools and technologies for their research.

Research Software Engineering. Researchers in Geography who conduct computationally intensive research were not aware of nor able to realise the full potential of their work due to the perceived technical barriers. An expert software engineer was introduced into the research team, and worked with the researchers on restructuring their code and data, which in turn allowed them to fully utilise the high performance computing facility at our institution. During the project, the researchers were also exposed to the potential of cloud computing. The initiative meant that the researchers could not only complete what they originally planned faster, but could run more simulations with the same resources. Optimisation of their code meant that they are now able to run larger models, increasing the accuracy and scale of their outputs i.e. they can model entire countries rather than a few islands.

Research Data Management. In May 2018 the GDPR [START_REF]General Data Protection Regulation 2016/679[END_REF] becomes enforceable, and will strengthen the rights of users over their data. Social Science is one of the research areas to be strongly affected by this change, as much of the work involves the digital capture and management of personal data. However, researchers were generally unaware of the new regulations. In response to this, a technology-legal expert was consulted to review the current governance processes around personal data how data is shared. Working with researchers, the legal expert amended and reinforced the institutions existing data access and user agreements in full recognition of the legislation and digital technologies used by researchers. This intervention will allow researchers to continue to digitally capture personally identifiable data in an ethical and legal way. Following the implementation of the DRI's in the faculty, we began to reflect on the areas of success. While the initial focus of the DRI's was on skills, systems and support, it became clear through the implementation that there were attributes that were more impactful than others. This led to the formation of the DRI Framework (Fig. 1) which is based on the relationship between three specific types of intervention in an academic setting. The first are Digital Technologies which are broadly defined as systems (hardware and/or software) that create or manipulate digital data. The second are Research Methods which are defined as systematic processes for creating data or concepts with the intent of answering a scientific research question. The third is Expert Knowledge, which refers to specific expertise in an area of specialism. We now consider the areas of intersection between these characteristics and relate back to the initiatives above.

Digital Research Initiatives Framework

Digital Research Data. Digital technologies are often know to create or capture digital data. However, is only when they are used as a part of a specific research method that digital research data is generated. Consider both OCR and handwriting technologies, these produced digital research data only when used as a part of the research process.

Specialised Digital Solutions. Digital technologies are ubiquitous in academic institutions and industry. However, simply having access to a technology does not make it effective or useful. Experts with specialist knowledge can help to customise, build and advise on the best use of the tools for a given purpose. Even without designing a specific technology from the ground up in a research context, these specialized solutions can be of use to researchers. The automated transcription is a clear example of this, where the digital tools were available and expert knowledge helped to construct a service around the technologyall of which was driven without a specific research method in mind.

Tailored Support. While researchers are experts in their specific area of interests, there are techniques and principles from other fields, particularly in Computer Science, that can advance their research. This was demonstrated by the introduction of the research software engineer in use of parallelization and the lawyer in managing research data.

Digital Innovation. Digital Innovation is where digital technologies, research methods and expert advice come together. This is the goal of our institution's digital research strategy, to maximize the interplay of these three areas. The immersive VR environments and agent based modelling projects were the most revolutionary for the researchers involved, as it opened new avenues and increased the ambitions of their research.

Using the Digital Research Initiative Framework

The framework can be used to assess the readiness of an institution for digital research transformation, but also to help guide the types of areas that might be implemented. The aim is to position a project or opportunity in the framework based on the three attributes, and to then determine the type of intervention required to 'pull' the project toward the centre. The first step is to assess an institution's capabilities in terms of three areas, research methods, digital technologies and expert knowledge. The second is to consider how these areas currently, or could, overlap to create new opportunities, these can often be the low hanging fruit. The final step is to consider how areas of overlap might be further enhanced, and 'pulled' toward the centre of the framework by introducing the appropriate third attribute. For example, our institution has used the framework to design three new exploratory and transformational projects ─ Internet of Things devices (digital technologies) are built by non-researchers (expert knowledge). By considering the role of research methods, a new initiative was identified to explore the ethical considerations for use of this technology in research. ─ A Sociology researcher is using a linguistic analytical tool (digital technologies) to apply a specialist technique (research method) to digital text. By considering the role of expert knowledge, a new initiative was identified to introduce a machine learning expert to help further develop the technique. ─ Specialist economics researchers are developing code (expert knowledge) and exploring game theory (research method). By considering digital technologies, a new initiative was identified to introduce an eye-tracking device to capture new data.

To the best of our knowledge there are no other examples in the literature of conceptual frameworks for assessing the digital readiness of an academic institution. Although notably Arribas et al., [START_REF] Arribas | Digital transformation of a small fashion house: a PLM implementation[END_REF] have proposed such a readiness framework in the context of the fashion industry. However their framework is exclusively grounded in digital technologies and drivers, and unlike the DRIF, fails to consider the role of expert knowledge. In a broader context, the DRIF directly informs Matt et al., [START_REF] Matt | Digital transformation strategies[END_REF] Digital Transformation Framework by proposing a series of new elements to be included in each of their "four cornerstones" of digital transformation; thereby grounding it in the context of an academic research institution.

Conclusion

Universities are complex organisations that operate in a challenging climate. It can therefore be arduous for institutions to define areas for transformation and successfully implement meaningful change. The strategic framework presented here enables institutions to introduce digital transformation in research via a three-pronged approach. By assessing the state of research methods, digital technologies and expert support available to researchers, this framework was used to successfully deploy practical methods for achieving our institution's digital research strategy.

Fig. 1 .

 1 Fig. 1. The Digital Research Initiatives Framework (DRIF) allows institutions to assess current digital research capabilities and assist in the design of digital initiatives.


Adrian Benfell
email: adrian.benfell@port.ac.uk

Zoe Hoy
email: zoe.hoy@port.ac.uk

Reformulating Requirements Modelling for Digitalisation: a Structuration and Semiotic Informed Approach

Keywords: Structuration, Semiotics, Requirements Modelling, Digitalisation

Articulated within this paper is a qualitative investigation into using structuration and semiotic theories that suggests improvements to communication when developing new software. Forming the analysis, an Information System (IS) designed using the Unified Modelling Language (UML) allied with the Agile software development process exposed a key factor: the need for effective communication. This factor caused a negative outcome when developing a new IS necessary to support the digitalisation of a business. Analysing the conditions and adjusting requirements modelling with structuration and semiotic theories, team members expressed with clarity requirements for their new IS as real-world effects and causes. Such cause and effect statements align to Peircean interpretant signs that facilitated effective communication.

Introduction

Digitalisation, the digital transformation of business, requires businesses to be more agile, people-orientated, pioneering, and customer-centric. Some articles point to digital technologies and their transforming properties, for example [START_REF] Bano | A systematic review on the relationship between user involvement[END_REF][START_REF] Kane | Strategy, Not Technology, Drives Digital Transformation[END_REF][START_REF] Giddens | Modernity and Self-Identity, Self and Society in the Late Modern Age[END_REF]41]. Extracting key themes from [START_REF] Bano | A systematic review on the relationship between user involvement[END_REF][START_REF] Kane | Strategy, Not Technology, Drives Digital Transformation[END_REF][START_REF] Giddens | Modernity and Self-Identity, Self and Society in the Late Modern Age[END_REF]41] suggests that effective communication is a requisite in any digitalisation process that needs new software. To realise digitalisation however, some persistent challenges must be answered. For instance, [START_REF] Fernández | Naming the pain in requirements engineering: A design for a global family of surveys and first results from Germany[END_REF][START_REF] Rosenkranz | Language quality in requirements development: tracing communication in the process of information systems development[END_REF][START_REF] Schwarz | Toward a process-based view of information technology acceptance[END_REF] draw attention to the limitations of requirements modelling for software development. They highlight that communication inconsistencies between people when embarking upon requirements modelling can cause negative project outcomes. Hence, in this paper we focus upon improving communication in relation to software development.

Based upon our experience with an Information Systems Development (ISD) project, we argue that when using the Unified Modelling Language (UML) [START_REF]Unified Modelling Language Resource Page[END_REF] combined with Agile [START_REF] Agile | Agile Alliance[END_REF], as contemporary requirements modelling tools, do not promote effective communication during software development. Our view follows [START_REF] Charaf | The emergence of shared understanding: applying functional pragmatics to study the requirements development process[END_REF][START_REF] Bano | A systematic review on the relationship between user involvement[END_REF] who emphasise that communication underpins requirements modelling. Many researchers place an emphasis upon effective communication when requirements modelling, however few pro-vide empirical evidence to resolve this challenge. In this paper we take a similar position to [START_REF] Charaf | The emergence of shared understanding: applying functional pragmatics to study the requirements development process[END_REF][START_REF] Bano | A systematic review on the relationship between user involvement[END_REF] and judge communication to be a permanent and relevant issue when requirements modelling.

Using UML as an up-to-date example, challenges to effective communication during requirements modelling start with UML Use cases. Modelling them further with UML Activity diagrams and narrative to describe functional properties changes the dynamics of communication between project stakeholders. Hence, communication focuses upon Use cases to depict state changes between the functional elements of IS and end-users. Extracting functionality from Use cases with Activity diagrams, the three stereotype classes, entity, boundary and controller, as they comply with the model-view-controller architectural design pattern [START_REF] Krasner | A description of the model-view-controller user interface paradigm in the smalltalk-80 system[END_REF], stabilise requirements when UML Communication modelling early in the requirements modelling process. Also, at the start of UML Communication modelling to show how stereotype classes communicate with each other, complexity increases when detailing requirements with full Object-Orientated (OO) class notation.

We assert, that during requirements modelling, intricacies that materialise when using UML modelling techniques to specify IS functionality impedes effective communication. In this paper we use [START_REF] Pohl | Requirements engineering: fundamentals, principles, and techniques[END_REF][START_REF] Loucopoulos | System Requirements Engineering[END_REF][START_REF] Bharadwaj | Digital business strategy: toward a next generation of insights[END_REF][START_REF] Nuseibeh | Requirements engineering: a roadmap[END_REF] who define modelling techniques as abstract representations of the problem domain using narrative combined with diagrams. Modelling techniques format communication during ISD, hence recognising how communication works in organisations should become a primary mechanism to facilitate improvements in requirements modelling. This view takes us to structuration and semiotic theories and frames the research questions (RQ) in this paper as: RQ1: What can structuration and semiotics inform about communication when requirements modelling? RQ2: How can structuration and semiotics reformulate requirements modelling to improve communication?

To answer the RQs, the structure of this paper is as follows. The next section, theoretical background, presents structuration and semiotic theories as they relate to requirements modelling and acts as preparation to answer the RQs. Following the theoretical background, the research method section includes a case study as supporting empirical evidence derived from an ISD project. The theoretical background provides the scope to assess the case study. Also, to fit digitalisation, the research method section includes recommendations to resolve communication issues when requirements modelling. This paper finishes with a conclusion that discusses outcomes and further research.

Theoretical Background

Giddens [START_REF] Giddens | Central Problems in Social Theory: Action, Structure, and Contradiction in Social Analysis[END_REF][START_REF] Giddens | The Constitution of Society: Outline of the Theory of Structuration[END_REF][START_REF] Giddens | Modernity and Self-Identity, Self and Society in the Late Modern Age[END_REF] defines the relationship between structure (the rules and resources that reproduce social systems) and agency (the conduct of an individual and of others) as structuration theory. A broad range of viewpoints exists regarding structure and agency. First, Durkheim [START_REF] Durkheim | Socialism[END_REF] who emphasised stability and durability of structure, and second, Marx [START_REF] Marx | Preface', to A Contribution to the Critique of Political Economy[END_REF] who defined structures as protecting the few and doing little to meet the needs of many. Giddens [START_REF] Giddens | The Constitution of Society: Outline of the Theory of Structuration[END_REF] however places structure at different levels within society. At the top-level, society comprises socioeconomic stratifications, for example social class. On a mid-level scale, institutions and social networks, and at a lower-level, a community or a grouping of social norms that outlines agency. By developing structuration theory, Giddens defines the 'duality of structure', whereby social structure is both the medium and the outcome of social action and people (as agents) share an 'equal ontological status'. Giddens's framework offers three parts to structuration theory. First, signification, where signs used in communication convey meaning specific to organisations. Communication reference points [START_REF] Charlton | The modernization imperative[END_REF], for example police uniforms, acronyms in professional languages and so on, are organisation specific signs. Charlton and Andras [START_REF] Charlton | The modernization imperative[END_REF] also suggest that specialised communication reference points demarcate organisations from others. Second, legitimation includes normative perceptions rooted as social norms that govern people in their actions and understanding in organisations. Third, domination, links to how individuals in influential roles apply power to control resources also in organisations. Hence, the mix of signification, legitimation, and domination links to semiotic theory which are discussed next.

Signification

Giddens [START_REF] Giddens | The Constitution of Society: Outline of the Theory of Structuration[END_REF], whilst taking a semiotic view of signs and their interpretation, provides little guidance about semiotics. However, semiotics includes two strands, one by Saussure [START_REF] Saussure | Course in General Linguistics (trans. Roy Harris)[END_REF] and the other by Peirce [START_REF] Peirce | The Essential Writings: Selected Philosophical Writings (1893-1913[END_REF]. Both strands have a subjective element to understanding signs present in communication, as signs can have different meanings in different organisations. However, Saussurean semiotics focuses upon communicative acts and text only [START_REF] Eco | A theory of semiotics[END_REF] but Peirce's view of semiotics helps to comprehend signs used in communication and requirements models. For example, to understand a sign Peirce and Saussure defined the term semiosis, the process of interpreting signs based upon different elements. In Peircean semiotics, the interaction between three combined parts: a sign, an object and an interpretant forms semiosis. A sign may align to three types: symbolic, iconic, and indexical. Regarding Figure 1, symbolic signs identify their objects through cultural conventions such as words; iconic signs represent their objects by similarities, such as images; and indexical signs have causal links to their objects, for example smoke identifying fire.

Regarding the process of semiosis in Figure 1, a sign refers to an object, and the object verifies its sign. Therefore, the object is the semantic meaning of a sign due to the ontological association. Interpretant signs add to the signobject association, as an interpretant is a more developed sign in someone's mind. Hence, the interpretant gives the contextual meaning given to a sign and may comprise social norms. Giddens view of legitimation and domination identifies how social norms provide the rules by which meanings emerge as interpretant signs. Also, according to Liu [START_REF] Liu | Semiotics in Information Systems Engineering[END_REF], semiosis is a signmediated process in which people give meanings to signs.

Legitimation

The interaction of agent and structure, as a system of normative behaviour supports structuration. Legitimation is applicable when attempting to understand communication issues when requirements modelling. For example, Giddens [START_REF] Giddens | The Constitution of Society: Outline of the Theory of Structuration[END_REF][START_REF] Giddens | Modernity and Self-Identity, Self and Society in the Late Modern Age[END_REF] uses the term reflexivity to describe normative behaviour as cause and effect cycles that occur when people revise their social world and alter their place in structures. In this setting, the interaction of people with structures induces normative behaviour. The interaction depicts how people understand their environment through an array of influences, marking out behavioural patterns that when followed define social normative behaviour [START_REF] Thøgersen | Social norms and cooperation in real-life social dilemmas[END_REF]. Hence, social norms evolve through the interaction of people when following expected conventions within organisations [START_REF] Boella | Introduction to Normative Multiagent Systems[END_REF]. Social norms emerge as legalised constraints in a contract, or surface resulting from reflexive social interaction [START_REF] Giddens | The Constitution of Society: Outline of the Theory of Structuration[END_REF].

Domination

Charlton and Andras [START_REF] Charlton | The modernization imperative[END_REF] and Luhmann [START_REF] Luhmann | Introduction to Systems Theory[END_REF] suggest that previous dialogue forms communication reference points that sustain contact whilst shaping ongoing discourse. People when they belong to an organisation have conversations using specific communication reference points. Thus, organisation specific communication reference points help to distinguish social norms owned by one organisation from another. An influence on social norms includes domination. Identified within structuration theory, domination identifies how people exert control over the rules and resources that influence social norms. However, through reflexivity, people may accept and change social norms shaped in power relationships. Domination thus influences understanding of social normative behaviour as interpretant signs.

Case Study

To answer the RQs, we present empirical evidence in a case study format split into four sections:

Project Context

Based upon a digitalisation project that started in 2013, its remit was to develop and deploy an IS to manage accommodation. The team comprised seven support managers, a senior support manager and a head of residential services. The team was self-managed and where possible involved team members in many stages of the Agile approach. Related to Information Technology (IT), all team members showed competence when using known computer-based office products. Team members provided feedback comments only and did not take part in software coding. Provision for the project came from a service department offering computing support, of which two further staff members helped to scope initial project requirements. One author of this paper involved with the ISD project took the title requirements engineer, whilst the other provided expertise associated with Agile. Working with the accommodation management team, the requirements engineer carried out requirements modelling and software development, organised and attended review meetings, and catalogued feedback. Access granted to historical hard copies of documentation associated with core business processes identified the ISD project context.

The project included one year for ISD, one year for a post implementation review, and a third year (and a fourth if required) for any modifications. The project took threeand-a-half years to complete. Additional supporting data collection methods included documentation associated with the ISD project as versioned requirements models, meeting agendas, and recorded minutes. Interviews were unnecessary as ample information, assembled by the requirements engineer, describes the situation.

An Outline of the ISD Challenge

The approach taken to develop the new IS, at the request of the computing department, asked that the project to undergo a full systems analysis and design strategy to suit the needs imposed by ITIL and its Service Design guidelines [START_REF]Best Practice Solutions[END_REF]. UML selected as a standardised set of modelling techniques suited ITIL, and UML Use case modelling shaped requirements modelling prior to systems design in an iterative ISD process cycle following Agile principles. In addition, to drive requirements modelling, UML Activity and Communication modelling techniques aligned to Use cases and Agile User stories, baselined requirements. The Agile approach followed the most popular Scrum VersionOne [START_REF]10th Annual state of agile survey[END_REF] that helped create an iterative and incremental approach to ISD. Meetings, iterations, and iteration planning according to VersionOne [START_REF]10th Annual state of agile survey[END_REF] formed the generalised ISD process. Iteration planning had a longer-term outlook and determined the high-level requirements as features needed for the new IS. User stories identified the Use cases which helped capture initial requirements, following the concept introduced by [START_REF] Beck | Extreme Programming Explained[END_REF].

For the plan-driven approach taken, a Computer Aided Software Engineering (CASE) tool facilitated requirements modelling. User stories included a small amount of narrative, one paragraph only, to help create a shared understanding between all team members [START_REF] Patton | User story mapping: Discover the whole story, build the right product[END_REF]. At the end of each iteration, team members provided feedback informing any changes needed [START_REF] Inayat | A systematic literature review on agile requirements engineering practices and challenges[END_REF]. With all team members in different locations presented challenges. This included working different shift patterns, and distances travelled. As a result, this reduced the number of opportunities to have consistent communication with all team members.

The versioned requirements models produced from UML modelling techniques informed initial and detailed class design as development iterations. For implementation, a completed class model coupled with relational database design advised software coding specifications. Also, it was compulsory that the class and relational database models complied with the model-view-controller design pattern to suit a client-server architectural setting for multi-user access. The database installed on the server (as the model), controller classes programmed to handle data transactions in Structured Query Language (SQL) commands across a network, and the views constructed to conform to the underlying data structure, followed the needs of all team members.

The planned Agile software development process linked to the UML Use case driven approach had a supporting project lifecycle and agreed milestones. Sanctioned by all team members, a project initiation document detailed the overall scope of the ISD project and included a short and precise narrative explaining the outcomes. Accomplished in team meetings and by visiting each team member, Agile User stories and linked UML Use cases formed the initial baselined requirements. UML Use cases underwent further development using UML Activity and/or Communication diagrams. UML Communication models aligned to Use cases helped to generate a final Communication model in the CASE tool. Applying UML stereotype classes in the final UML Communication model assisted in designing classes to fulfil the model-view-controller architecture. This approach intended to formalise requirements before moving to software coding.

Observation and Analysis

After achieving an initial agreement for Agile User stories and associated UML Use case model, complications surfaced. Information encapsulating reflexivity as social norms against communication reference points in an interpretive scheme (signification), proved difficult to capture using UML modelling techniques. To ensure that the new IS met with success, we deemed this information essential to realise the needs of each team member and match it over to the information required to create class diagrams to remove complications. The attempt to capture and represent this information resulted in numerous modifications to the UML Use case model, Agile User stories, and UML Activity and Communication diagrams but without success. Hence, the project ran into severe time difficulties.

To capture requirements for the new IS implied that previous dialogue formed communication reference points, which we did not expect, for the basis of contact between team members whilst shaping on-going discourse based upon social norms, aligning to [START_REF] Charlton | The modernization imperative[END_REF]. In addition, the requirements engineer intervened attempting to keep the project on target as many of the Agile User stories became invalidated as the project advanced through requirements modelling towards software coding. However, team members identified and agreed upon real-word effects. For example, with the existing IS one team member stated, "we produced more accurate case reports when updating case details with correct time allocations". Team members detailed effects that occurred in the real-world and agreed to them, and in doing so, identified what we described as social norms linked to communication reference points.

When discussing functionality with Agile User stories and UML Use cases, team members lost the link between the functional description and what they wished to achieve. However, a prototype relational database designed from an existing, but incomplete, requirements model helped meet the initial one-year deadline. This plan eased time pressure, but the prototype required further development. A review into how requirements modelling could meet the fast-changing demands placed by team members allowed us to consider how communication reference points, linking to social norms, could reformulate requirements. The idea was to generate requirements for the project as short and precise statements. First signifying communication reference points in a process of semiosis. Second, aligning reflexive cause and effect cycles to Peirce's view of sign and object. Third, using legitimisation and domination to identify social norms as Peirce's interpretant signs.

Reformulating Requirements Modelling

To represent communication reference points, we identified a suitable scheme that provided a means to categorise information harvested during requirements modelling. Peircean semiotics, further explained by [START_REF] Atkin | Peirce's Theory of Signs[END_REF][START_REF] Chandler | Semiotics -The Basics[END_REF][START_REF] Short | Peirce's Theory of Signs[END_REF][START_REF] Sowa | Knowledge Representation: Logical, Philosophical, and Computational Foundations[END_REF] provided a way to adjust requirements modelling using symbolic, iconic and indexical signs. First, symbolic signs as particular words structured what we called Normative Statements based upon Figure 2. Symbolic signs included the rules that governed how words formed phrases and sentences within each information element (such as Project, Project Scope and so forth in Figure 2). Second, icons as oval shapes with names identifying UML Use Cases and linked Normative Statements. Third, indexical signs depicted the association between communication reference points and social norms, and between causes and effects structured as narrative declarations. Hence, Normative Statements modelled reflexivity as cause and effect cycles understood and communicated by team members. To ensure consistency when deciding cause and effect statements, we followed necessary and sufficient conditions [START_REF] Brennan | The Stanford Encyclopaedia of Philosophy (Winter 2012 Edition)[END_REF]. Also, each cause statement that supported an effect became a candidate function within a candidate class. Normative Statements as we designed them, codified interpretant signs.

Normative Statements for each UML Use case emerged by collective agreement across connected team members as Roles (shown in Figure 2). For revision to requirements modelling, each Normative Statement promoted effective communication. For example, Normative Statements allowed an autonomy of expression with short natural language blocks of text [START_REF] Rosenkranz | Language quality in requirements development: tracing communication in the process of information systems development[END_REF] that allowed team members to detail supporting necessary conditions to real-world effects. They also captured a balance between agency and structure through the principle of legitimation and domination. Based upon Figure 2, Normative Statements forming a complete requirements model emerged within two months, four months shorter than when using UML, and reflected the demands placed by team members.

Using Normative Statements helped us to meet the original ISD project timeframe. This approach also removed the need for further modelling with UML Activity and Communication diagrams. We claim Normative Statements simplified requirements modelling, improved communication, and validated the use of structuration and semiotic theories to understand communication issues.

Discussion and Conclusion

Structuration and semiotic theories provided a method to understand and modify communication when requirements modelling. For example, signification identified the use of communication reference points in communication that link to social norms based upon legitimation and domination. Combined, these items demarcate organisations and semiotic theory helped to represent this phenomenon. Thus, communication reference points in the form of Normative Statements acted as a prime mechanism by which team members understood and communicated requirements for the ISD project.

Underpinned by necessary and sufficient conditions [START_REF] Brennan | The Stanford Encyclopaedia of Philosophy (Winter 2012 Edition)[END_REF], supporting real-world effects structured by Figure 2 are our view of Peircean interpretant signs. This approach considered how words and phrases, structured inside Normative Statements, combined to represent meaning attached to them by team members for requirements [START_REF] Rosenkranz | Language quality in requirements development: tracing communication in the process of information systems development[END_REF]. The necessary and sufficient conditions allowed team members to understand and communicate reference points and social norms.

Using structuration and semiotic theories as the focal point for research, generalisations are possible with similar situations in organisations. For further research, testing the propositions in this paper needs to advance by considering design artefacts and attaching their composition to Figure 2. Although UML and Agile are useful to develop software artefacts that support IS, our recommendations suggest that an alternative way of requirements modelling incorporating Giddens structuration theory and Peircean semiotics is plausible to meet communication challenges, and therefore digitalisation. However, semiotics needs more research in a digitalisation context.

Stamper [START_REF] Stamper | Signs, information, norms and systems[END_REF]; Liu [START_REF] Liu | Semiotics in Information Systems Engineering[END_REF] and Liu and Li [START_REF] Liu | Organisational Semiotics for Business Informatics[END_REF] place an emphasis on studying the effect of signs in organisation contexts. Their view of semiotics forms an inquiry system approach to analysing organisations from the viewpoint of sign creation, utilisation and meaning that govern the behavioural aspects of interaction. Stamper devised a 'Semiotic Ladder' as an Organisational Semiotic (OS) approach to investigate organisations based upon Morris [START_REF] Morris | Foundations of the Theory of Signs[END_REF], shaping most of the work with the realm of OS. Stamper's Semiotic Ladder distinguishes between organisational and IT domains. It says the IT domain supports organisations and gives technology a particular flavour according to 'social worlds'. Social worlds comprise beliefs, law and culture feeding into Giddens legitimation and domination as intentional communication. OS offers a wealth of research to develop further Normative Statements.

Fig. 1 .

 1 Fig. 1. Semiotic triangle, the signobject association and interpretant signs

( 3 . 1 )

 31 overviews the context of the study; (3.2) outlines the ISD challenge based upon UML and Agile; (3.3) details an observation and analysis from the viewpoint of the theoretical background and answers RQ1; (3.4) includes proposals to reformulate requirements modelling to improve communication answering RQ2.

Fig. 2 .

 2 Fig. 2. The Structure of Normative Statements and Codification of Interpretant Signs


Chekfoung Tan
email: chekfoung.tan@uwl.ac.uk

Sara Abdaless
email: abdaless@lsbu.ac.uk

Kecheng Liu
email: kecheng.liu@henley.ac.uk

Norm-based Abduction Process (NAP) in Developing Information Architecture

Keywords: Abduction, Logical Reasoning, Information Architecture, Organisational Semiotics

Abduction is a logical reasoning process that allows the discovery and creation of new knowledge. However, the function of knowledge is not explicitly developed in the existing research on abduction. Developing information architecture is a scientific inquiry in a practical context as it engages multiple stakeholders. However, the current research in information architecture does not appear to be underpinned by sound theoretical foundations. This paper proposes a norm-based abduction process (NAP) where norms are seen as knowledge in developing information architecture. A case study of a UK hospital is used for illustration purposes. The key contribution of this paper is to incorporate norms in the existing abduction process, to assert abduction as the foundation of a logical reasoning process and to derive a theoretical proposition for information architecture.

Introduction

The term of information architecture (IA) was coined by Richard Saul Wurman back in the mid-seventies. IA is seen as a tool to gather, organise and present information that serves a purpose [START_REF] Dillon | Information Architecture[END_REF]. Research has shown that IA did not seem to have sufficient theoretical underpinnings as it is defined based on the application context [START_REF] Tan | Information Architecture for Healthcare Organizations: the case of a NHS Hospital in UK Paper presented at the Thirty Fourth International Conference on Information Systems[END_REF]. For instance, IA is referred to as the external presentation of information (e.g. the websites) or the internal organisation of information (e.g. information management) [3]. Therefore, IA can be seen as a design process or a solution for a design problem [modified from 4]. Hence, there is a need to establish a theoretical proposition for the development of IA which is in relation to its nature as a design process. Abduction is a type of logical reasoning and it plays a vital role in design process [START_REF] March | The Logic of Design[END_REF]. Peirce [START_REF] Peirce | Collected Papers of Charles Sanders Peirce: Pragmaticisms and Pragnoaticism[END_REF] defined abduction as the process of forming explanatory proposition which starts a new idea. Peirce viewed the reasoning approach as a semiosis process.

Semiosis is a sense-making process which involves three universal categories (firstness, secondness and thirdness) which correspond to the three main reasoning approaches (abduction, deduction and induction). Being based on prior knowledge and preliminary data collection about the phenomenon, abduction is used to generate a theoretical proposition which is then explicated through deduction and empirically verified through induction [START_REF] Liu | Organisational semiotics and business informatics[END_REF]. However, the existing research on the abduction process does not develop the function of knowledge explicitly. For this reason, a new abduction process highlighting the importance of knowledge in the development of information architecture is introduced in this paper.

The aim of this paper is to propose a norm-based abduction process (NAP) in the development of IA. Norms are regarded as the interpretation of signs, or knowledge, which guides human behaviour and actions [START_REF] Stamper | Information Systems as a Social Science: An Alternative to the FRISCO Formalism[END_REF][START_REF] Stamper | Understanding the roles of signs and norms in organizations-a semiotic approach to information systems design[END_REF][START_REF] Braf | Knowledge or Information: : What makes the difference?[END_REF]. The notion of norms is employed to unambiguously elicit the function of knowledge in each stage of NAP. Norms emphasise the cumulative nature of knowledge in each stage of the scientific inquiry. NAP helps researchers to resolve the puzzlement based upon the prior knowledge and preliminary data collection. IA is seen as a design process for an observed problem; it is also an inquiry process which involves reasoning in deriving the artefacts. The reasoning process is complicated as it engages multiple human agents. NAP is therefore an abduction process that explicitly focusses on the use of knowledge in developing IA.

This paper is structured as follows: Section 2 describes the theoretical foundation of this research, Section 3 illustrates NAP, Section 4 demonstrates a practical application of NAP in developing the IA for a UK hospital. Healthcare organisations are complex and known as the world's largest and most inefficient information enterprises [START_REF] Hillestad | Can electronic medical record systems transform health care? Potential health benefits, savings, and costs[END_REF][START_REF] Martin | A resurgence of interest in Information Architecture[END_REF], and Section 5 concludes the discussion of the contributions, limitations and future work on NAP as a reasoning process in developing IA.

2

Literature Review

Information Architecture

Information architecture (IA) describes the transformation of data into meaningful information for people to use [START_REF] Dillon | Information Architecture[END_REF]. IA possesses a high level map of information requirements in an organisation; it is also a design process in architecting information in order to achieve organisational benefits [START_REF] Brancheau | Information architectures: methods and practice[END_REF][START_REF] Evernden | Information first: integrating knowledge and information architecture for business advantage[END_REF]. In this section, the theories covered for developing IA are organisational semiotics (OS) and service-oriented enterprise architecture (SOEA). OS underpins the fundamental concept of signs and norms in learning the use of information in an organisation. IA plays various roles depending on the context of application [START_REF] Tan | An organisational semiotics inspired information architecture: pervasive healthcare as a case study[END_REF]. SOEA offers an architectural method for designing IA as an artefact.

OS characterises an organisation as a structure of social norms and it is seen as an information system [START_REF] Stamper | Understanding the roles of signs and norms in organizations-a semiotic approach to information systems design[END_REF]. Hence, information requirements can be deduced once the norms within an organisation are identified. Norms are developed through the practical experiences of the human agents in an organisation, thus the norms have directive and prescriptive functions on the human agent's action [START_REF] Liu | Semiotics in information systems engineering[END_REF]. Organisational onion (OO) categorises norms of an organisation into three layers [START_REF] Stamper | Linguistic Instruments in Knowledge Engineering[END_REF]: informal, formal and technical. The informal layer refers to organisational culture, customs and values which are reflected as beliefs, habits and patterns of members in the organisation. The formal layer denotes the rules and bureaucracy to perform the organisational activities. The technical layer contains technical systems which enable actions performed in the formal and informal layers. Organisational morphology (OM) further analyses the norms embedded in each layer in substantive, communication and control perspective which guide the activities performed in an organisation [START_REF] Stamper | Organisational morphology in re-engineering[END_REF]. Substantive norms are productivity related actions. Communication norms administer activities that involve message passing from one agent to another in order to coordinate their substantive activities. Control norms reinforce the substantive and communication activities through rules and regulation.

SOEA provides a holistic information management framework for an organisation [adapted from [START_REF] Steen | Service-Oriented Enterprise Architecture[END_REF][START_REF] Van Den Hoven | Data architecture: blueprints for data[END_REF]. SOEA is one of the approaches in designing IA. SOEA aims to leverage the strengths of service-oriented architecture in aligning various architectures derived from the enterprise architecture. The service-oriented concept emphasises on framing the business and IT capability in an organisation as a series of services. SOEA designs a language which is mutually understandable by the business and IT personnel. SOEA contains three layers: business layer, application layer and technical layer. The business layer offers services or products to customers through business components such as business processes and business actors. The application layer contains the application components which realise the business services. The technical layer offers technical components which realise the application services.

Abduction in Design Research

Abduction is a reasoning approach which aims to explore the data, find a pattern and suggest a plausible hypothesis [START_REF] Peirce | Collected Papers of Charles Sanders Peirce: Pragmaticisms and Pragnoaticism[END_REF]. The logical form of abduction is as follows: The surprising fact, C is observed; but if A were true, C would be a matter of course; hence, there is reason to suspect that A is true. Kovács and Spens [START_REF] Kovács | Abductive reasoning in logistics research[END_REF] and Thagard [START_REF] Thagard | Abductive inference: From philosophical analysis to neural mechanisms[END_REF] propose the abduction process based on Peirce's definition on abduction. The proposed abduction process starts with the observation of a phenomenon and simultaneously triggers a process of matching prior knowledge with the observed phenomenon. Abduction is therefore a logical reasoning process. Peirce stamped the logic of reasoning as a semiosis process. Based on the semiosis process (see Figure 1), one can postulate that abduction takes the firstness as the starting point to generate new ideas. These ideas are in the form of theoretical propositions which suggest a possible explanation of a phenomenon. Abduction process eventually generates new knowledge. Deduction commences at the secondness which takes the existing theoretical propositions and applies them in a small number of individual cases. Induction departs from the thirdness and it uses the data gathered in a large scale to conduct empirical studies. The results obtained from the empirical experiments serve for generalisation of the proposition. The logic of reasoning is employed to answer the research questions which require an answer in a form of the respective conclusion [START_REF] Minnameier | The logicality of abduction, deduction, and induction[END_REF].

Abduction is closely related with design science research. March [START_REF] March | The Logic of Design[END_REF] was the first to introduce Peirce's abduction to design. He posits "abduction is the only logical operation which introduces any new ideas; for induction does nothing but determine a value; and deduction merely evolves the necessary consequences of a pure hypothesis". Abduction is seen as a key reasoning approach in designing a solution which deals with complex problems [START_REF] Dorst | The core of 'design thinking'and its application[END_REF]. Abduction generates better ideas over time, hence boosting creativity in problem solving [START_REF] Gregory | Theorizing in design science research: inductive versus deductive approaches[END_REF][START_REF] Dubois | Systematic combining: an abductive approach to case research[END_REF][START_REF] Pauwels | Design thinking support: information systems versus reasoning[END_REF]. Fischer et al. [START_REF] Fischer | Forms of discovery for design knowledge[END_REF] propound four aspects of abduction in the design science research: 1) validity, ensures the design solution proposed by the theory works correctly, 2) utility, ascertains the design solution fulfils stakeholders' needs, 3) generality, reflects the design solution as a generic solution, and 4) innovativeness, demonstrates the new element in the design solution. The norm-based abduction process (NAP) is a logical reasoning process that involves the use of norms (knowledge) (see Figure 2). NAP consists of interactions (inferring, resulting or matching) between three main elements which are: 1) the human agent who is always going to have some sort of prior knowledge (A+K), 2) the sign which is referred to as S when it is representing the observed phenomenon and S+ when it refers to the data collected about the phenomenon and, 3) the proposition that is referred to as P when it is the first set of propositions and P+ when it is the second set of propositions after reforming the former. This relationship between A+K, S and P/P+ will vary from a stage to another in NAP. Section 4 provides the description and application of each stage of NAP.

Case Study: NAP in Developing IA for a UK Hospital

A case study has been conducted in a UK hospital in order to demonstrate NAP in the development of IA. IA is vital for healthcare organisations in providing the right information to the right hospital staff at the right time [START_REF] Tan | An organisational semiotics inspired information architecture: pervasive healthcare as a case study[END_REF]. Below is the illustration of the NAP (in reference to Figure 1) in the development of IA for a UK hospital.

Stage 1: Identifying the motivation of research NAP starts with a puzzlement following the observation of a problem (resulted by signs (S)) by the agent. The agent here refers to the researcher. The agent is then using the prior knowledge (A+K) to infer the observed problem (the S). The agent noticed that there were redundancy processes in creating clinical and operational reports in the hospital (the puzzlement) and the hospital did not opt for any architectural frameworks to address the issue of information mismanagement (the S). This stage refers to the motivation of having the IA in place to better the information management practice of the hospital.

Fig. 2. Norm-based Abduction Process (NAP)

Stage 2: Forming theoretical propositions

The researcher (agent) with the prior knowledge on IA attempts to form the theoretical proposition, which is the result of the information mismanagement phenomenon (sign).

In this case study, IA is meant to resolve the information mismanagement issue. Therefore, the theoretical proposition is that "IA is norm centric", whereby designing and using IA to enable effective information management. Norms, in the study of OS illustrate how the information is used by the human agents in performing certain actions in an organisation [START_REF] Stamper | Understanding the roles of signs and norms in organizations-a semiotic approach to information systems design[END_REF]. Norms therefore are adopted in the development of IA. Following OO and OM [START_REF] Tan | Information Architecture for Healthcare Organizations: the case of a NHS Hospital in UK Paper presented at the Thirty Fourth International Conference on Information Systems[END_REF], Figure 3

Fig. 3. Norms and Activities

Based on this activity categorisation, information requirements are elicited in six dimensions per each activity. The dimensions are: Who are using the information, What is the content of the information, How information is represented, Where information is stored, When the information is needed and Why the information is needed in that activity. Figure 4 depicts the example of information requirements from one of the organisational.formal.substantive (o.f.s) activities. SOEA was employed to model the actual IA for practical use in the final stage.

Fig. 4. Information Requirements of the 'check-in patient' activity

Stage 3: Conducting real life observation

In this case study, the researcher (agent) with prior knowledge (A+K) on IA collects data in order to understand the phenomenon of information mismanagement. Data (S + ) were collected through observations in two sites: 1) an outpatient clinic for gathering the information requirements by using the template as in Figure 2 and 3), and 2) information services department in order to understand how information is processed at the backend for reporting purpose. The secondary data were collected through various documents produced by the hospital. The standard operating procedures (SOPs) were the key documentation for understanding the business activities (formal norms) and how these business activities were executed through IT systems (technical norms). In addition, the outpatient commissioning data sets (CDS) from Hospital Episode Statistics (HES) [START_REF]HSCIS: Hospital Episode Statistics[END_REF] were adapted to detail the information elements which construct the IA.

Stage 4: Evaluating theoretical propositions

The researcher (agent) with prior knowledge (A+K) on information architecture and experience working in the hospital is inferred by the data collected (S + ) which will direct the method for data analysis. The result of the data analysis is then used to verify or refute the theoretical proposition (P) formed in stage 2, which "information architecture is norm centric". In this case study, thematic analysis was used to analyse the collected data in the outpatient clinic in stage 3. SOEA was adopted to encapsulate the collected information requirements into services. These services informed the information elements that supported the activities. As a result, there were two types of services: clinical and administrative services. The clinical services were: consultation (non-theatre procedure, elective surgical procedure), triage, advice and guidance, diagnosis (pathology, radiology), discharge and prescription. The administrative services were patient administration, referral, appointment and scheduling, payment, and information management. Descriptive analysis was adopted to analyse the collected data in the information service department. It narrated the information management processes (information collection, processing and dissemination) in the hospital. Document analysis was conducted for analysing the SOPs and CDS. This was used to detail the information elements that supported the services. Figure 5 illustrated one of the information elements for the 'radiologic diagnosis' activity. The information architecture was a collection of information elements which can be modelled with SOEA principles. IA shows how information was collected in the business layer and processed through the application and infrastructure layer. The theoretical proposition was preliminarily evaluated through gathering feedback from the subject matter experts in the hospital. According to the subject matter experts, the proposed IA covered the information requirements for a whole department instead of a single information system view and demonstrates how information was facilitated from the business environment to the technical systems. Moreover, they found that the proposed IA had a positive contribution to addresses the issue of information mismanagement in the hospital.

Discussions and Conclusion

This paper illustrates the norm-based abduction process (NAP) in the development of information architecture. The five stages in NAP involve prior knowledge of the human agents to perform the relative actions within the stages. Abduction is a logical reasoning that plays a role in design research and IA is seen as a design process. NAP is therefore applied in developing IA. A case study is conducted in a UK hospital to demonstrate the application of NAP in developing IA. IA is vital for healthcare organisations in ensuring the right information is provisioned to the right hospital staff at the right time.

The contributions of this paper are twofold: 1) extending the existing abduction process by incorporating norms, and 2) deriving a theoretical proposition for information architecture.

The first contribution of this paper is that NAP incorporates norms in the existing abduction process such as [START_REF] Thagard | Abductive inference: From philosophical analysis to neural mechanisms[END_REF] and [START_REF] Kovács | Abductive reasoning in logistics research[END_REF]. Norms are regarded to be knowledge as they are derived from the interpretation of signs by the human agents [START_REF] Stamper | Information Systems as a Social Science: An Alternative to the FRISCO Formalism[END_REF][START_REF] Braf | Knowledge or Information: : What makes the difference?[END_REF]. NAP scrutinises the use of norms (interpretation of signs) in each stage of the abduction process in order to highlight the function of knowledge (see Section 3 and 4). NAP fulfils the scientific inquiry process suggested by Kuhn [35] in leading to new knowledge in the form of a new proposition, which is the result of the observation of a phenomenon. In addition, NAP adopts Popper's [START_REF] Popper | Conjectures and refutations[END_REF] refutation approach, where the formed proposition can be refuted by the observed facts. This encourages the agent to continue the inquiry process in order to find the best proposition resulted by the phenomenon. NAP guides the design process in producing the IA and provides a specification of the IA as an artefact. The abduction appraoch in NAP where knowledge is imparted in each stage of the process helps in forming the theoretical proposition for the development of IA. In a practical perspective, the abduction approach in NAP helps information professionals to apply a new idea in boosting the informatics creativity for finding ways to progress from being "data rich and discovery poor to a state of information wealth" [START_REF] Ross | Informatics creativity: a role for abductive reasoning?[END_REF]. NAP is iterative, and the number of NAP cycles is depending on the subjective view of the agent.

There are two limitations identified in this paper. The first limitation is that it is hard to determine when the iteration of NAP stops. It is assumed that the agent will stop when he or she believes that a best proposition has been achieved. In addition, the second limitation is that there is only one cycle of NAP presented in the case study of developing the IA. There is a need for the agent to conduct multiple cycles of NAP in order to reform the theoretical proposition until it explains or resolves, in this instance, the information mismanagement phenomenon.

As for the future work, the impact of abduction on a scientific inquiry will be explored in depth especially with the purpose of discovering and creating knowledge. In this paper, NAP consists of five stages and regards norms as a mean to understand the function of knowledge. The norms specification from OS will be incorporated in each stage of NAP for knowledge profiling at the individual level by incorporating perceptual, cognitive, evaluative, denotative and behavioural norms. In addition, NAP will be developed as a research methodology which will enable the researcher to make more informed decision on how IA should be designed. From the practical perspective, more cycles of the NAP are going to be conducted in the industry with practitioners such as information managers. This approach aims to finalise the best proposition in the process of developing IA. In addition, this will increase the validity, utility, generality, and innovativeness of the specification of the IA as an artefact.

Fig. 1 .

 1 Fig. 1. Semiosis as a Logical Reasoning Process [adapted from 29,30,31,6,32,33]

Fig. 5 .

 5 Fig. 5. Information Element