Designing a Digital Marketing Model Innovation to increase the Competitiveness . First insights in Mexico Diseñando un Modelo de Innovación de Mercadotecnia Digital para incrementar la Competitividad . Primeros hallazgos en México

Introduction: This paper is aimed to determine the relationship between the competitiveness model of Mexico (NCM) and a proposal for a digital marketing innovation model (DMIM). This is of special value for SME experts and academics, both related to the practice of digital marketing, in order to obtain a tool capable to identify different variables involved in the design and driving of a digital marketing campaign, in order to increase their levels of competitiveness. Method: The theoretical framework for the DMIM proposal, was based on a documentary, exploratory, descriptive, correlational and transversal research (academic vision). Thus, it was applied focus group using Panel Delphi and Analytical Hierarchy Process, among the specialists of digital marketing sector (expert vision) to identify different variables and indicators registering their different importance rates as well as the order of occurrence of each one into the DMIM proposal construct, for both visions. The final DMIM proposal construct, was correlated with the NCM, previously known. Therefore a pilot questionnaire, with a test of validity and reliability, was applied during the period of January-April 2017, to 400 specialists related with digital marketing and competitiveness (200 SME experts, 100 consultants and 100 academics). As a quantitative stage (empirical evidence), multiple linear regression and correlation techniques were applied to determine the relationships in this model. Results: It was obtained an original and generalized model, able to explain the relationship of variables and indicators involved among 8 NCM variables and 10 variables of the DMIM proposal as a measuring tool, and final suggestions for the improvement of such relationship. Conclusion: An empirical model of NCM-DMIM that showed positive effects of correlation in 2/10 variables of the DMIM: among the mission, vision and values (MVS) as well as the Designing a Digital Marketing Model Innovation to increase the Competitiveness. First insights in Mexico No 20, Vol. 10 (1), 2018. ISSN 2007 – 0705, pp.: 569 591 570 knowledge and application of the tools of digital marketing, (DMT) both variables belonging to the DMIM over to the NCM. The rest of the (8/10) variables of the DMIM, show low correlations which means that they are not yet sufficiently widespread and known in the sector for what they represent the sector's opportunities. Finally, academic and expert visions about the variables of the construct NCM-DMIM, with several suggestions for improvement. Resumen Introducción: El presente trabajo tiene como objetivo el determinar la relación que existe entre el modelo de competitividad de México (NCM), con nuestra propuesta de modelo de innovación por mercadotecnia digital (DMIM). Lo anterior es de especial valor para expertos Pyme y académicos relacionados con la práctica de la mercadotecnia digital, a fin de lograr obtener una herramienta que les permita identificar qué variables tomar en cuenta en el diseño de una campaña de mercadotecnia digital y prever su impacto competitivo. Método: Marco teórico con investigación documental, exploratoria, descriptiva, correlacional y transversal aplicando, como etapa cualitativa, a especialistas del sector: focus group, Panel Delphi y Proceso de Jerarquía Analítica, en la que se identificaron dos visiones: académica y de expertos describiendo diferentes tasas de importancia así como el orden de ocurrencia de cada variable del constructo. Lo anterior, produjo la propuesta de variables e indicadores que conforman al DMIM a fin de ser correlacionado con el NCM, mediante el diseño y aplicación de un cuestionario piloto. Una vez comprobada su validez y fiabilidad, el cuestionario final se aplicó durante el período de Enero-Abril 2017, a 400 especialistas relacionados con la mercadotecnia digital y la competitividad (200 expertos Pyme, 100 consultores 100 académicos). Resultados: Se obtuvo una primera aproximación del modelo generalizado, capaz de explicar las variables involucradas en la relación de este original constructo de 8 variables de NCM relacionados con 10 variables de la propuesta DMIM, confirmando una herramienta de medición, y varias sugerencias para la mejora del sector de mercadotecnia digital en México. Conclusión: Un modelo empírico de que relaciona NCM-DMIM evidenció efectos positivos de correlación en 2/10 variables de la DMIM: entre la misión, visión y valores (MVS) así como el conocimiento y aplicación de las herramientas de mercadotecnia digital, (DMT) ambas variables pertenecientes al DMIM con el NCM. El resto de las (8/10) variables del DMIM, mostraron bajas correlaciones lo que significa que aún no están lo suficientemente difundidas y conocidas en el


Introduction
According to OCDE (2005), innovation is: "the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations".
Innovation aims at improving a firm's performance by gaining a competitive advantage.
Regarding the marketing innovation: "is the implementation of a new marketing method involving significant changes in product design or packaging, product placement, product promotion or pricing".
Today, Mexico's competitiveness is ranked as a country in the place 51/138 (WEF, 2017), so that digital marketing is representing a real marketing innovation driver to improve such place.
In this sense, the web portal Millones de Voces (2017), reports a sector with more than 200 small and media enterprises (SMEs) firms located at Guadalajara, Mexico, and several recognized institutions that are teaching and training about digital marketing issues.Nowadays the digital marketing has several and innovative tools for: planning and building a website, planning the content, blogging, paying the advertisement, the social networking, emailing, making video, making the web analytics, etc.So, what about the criteria of a digital campaign design to be aligned with the sector competitiveness?
The digital marketing sector is very interested to obtain a proposal of the DMIM capable to improve its competitiveness level based on the NCM.To achieve the proposal model, this work is divided into the explanation of: 1) Problem, hypotheses and rationale of the study; 2) Literature review 3) Methodology based on two visions: the academic and expert about digital marketing innovation and competitiveness to obtain a final DMIM to be related with NCM, and the design of the final questionnaire; 4) Results; 5) Conclusions; 6) Limitations and Future Studies.

Problem, Hypotheses and Rationale of the Study
For digital marketing experts, is a real need to obtain a model able to identify and drive their main variables and indicators of digital marketing in order to increase their level of competitiveness.Due at this moment, there is no evidence of contrast tests between the proposal of Digital Marketing Innovation Model (DMIM) vs. the National Competitiveness Model (NCM) the proposal of research question (RQ), is: Which are the empirical effects of DMIM on NCM?
To solve this, is necessary to propose a construct based on these two factors.Hence, regarding the DMIM variables it is proposed the following specific questions: SQ1: Which are the variables and the scheme involved?
SQ2: Regarding of these variables, are there differences between the academic vision vs. the expert vision?
SQ3: Which is the final questionnaire?
SQ4: How are the main relationships between NCM and the variables of DMIM?
Finally, the Hypothesis: The most significant DMIM variables are present in at least, on 50% of the variability of NCM.

Literature Review
The National Competitiveness Model (NCM).Competitiveness is the ability and performance of a company, sub-sector or country to sell and supply goods and services in a given market, in relation to the ability and performance of other firms, sub-sectors or countries in the same market (IMCO, 2017;Kotler & Lane, 2006).As a part of the Quality National Prize (Premio Nacional de Calidad) Mexico has designed its own National Competitiveness Model (PNC, 2017) adopted here in this study, with the following variables showed in Leaders reflect on their behaviours and commitment to achieve the mission of organization, change and innovation; they communicate with their staff, motivate them in their development and overall well-being; they are aware of and they respond to their own growth opportunities.

2
Customer Value Generation (CVG) The organization knows its clients intimately, knows what creates its value and responds with innovative proposals that ensure a memorable experience, follow the evolution of their needs and establish the basis for a constant alignment with them

3
Strategic Planning (STP) The organization ensures the fulfilment of its vision and mission; analysing their environment, understanding their challenges, setting priorities, defining strategic objectives, aligning their resources and capabilities to ensure their execution, The values of the organization lay the foundation for developing a culture focused on change, innovation and continuous improvement that is reflected in the way staff organize and engage to generate new ideas to respond to the challenges they face.

5
Social Commitment (SCO) The organization assumes responsibility for the social and environmental environment in which it operates.This commitment is reflected in its initiatives to reduce its environmental footprint, promote the integral well-being of its personnel and respond to the social needs of its community.

Wellness and Inclusion (W&I)
The organization ensures the integration of all its staff through the creation of a shared identity and responding to their physical and emotional needs in the workplace as well as in the communities where they live.

7
Knowledge (KNW) The organization collects, organizes, shares and analyses knowledge through the use of its resources and the skills of its staff, thereby generating the intellectual capital of the organization it capitalizes for the improvement and innovation of its products, services and processes.

Agility (AGY)
The organization responds quickly, adaptively and flexibly to the changes that occur in its internal and external environment.The Table 2 shows in different columns, the number, year of publication (since 2012 until 2017), author, a main definition of Digital Marketing and several variables that support each work, in order to identify them and make an analysis about how many times they are mentioned (see Table 3).Therefore, it was obtained an approach of the academic vision, to be submitted to the experts.
After this, it was applied the qualitative part of this research through focus group with Delphi Panel and Analytic Hierarchy Process (AHP, Saaty, 1997) to 10 specialists (5 professors and 5 CEO) as digital marketing campaign designers, focusing the attention and experience of each one of them, in the association of variables and the order suggested to be implemented to the DMIM.
The results are showed in Table 4.The Table 4 is divided in 3 parts.The first one, shows the a list of 15 ordered variables in importance as a result of the Delphi panel focus group with 5 professors in digital marketing.The column frequency is based on how many of these variables were mentioned by different analyzed authors (see Table 3).Thereby, it was reported the importance of each one of the variables suggested for the same group of professors as a result of the Analytic Hierarchy Process (AHP) as an academic vision.The second one, is based on the perception of 5 CEOs of digital marketing firms, whom ordered the same list of variables as a suggestion for implementation; it was reported the importance of each one of the variables suggested for the same group of CEOs as a result of the Analytic Hierarchy Process (AHP) as an expert vision.Finally, it was reported the % weighing difference between both visions (where less than 2% were excluded for practical reasons) where the sign (-/+) indicates what side is describing and explaining more about the variable.For instance, the variable strategy (STG) for academic vision has a rate of 19.6% vs. 9.7% as an expert vision; the % difference is +9.9; hence the academic vision is the side that is explaining more of this variable (or, the expert vision, needs to consider more about that).
Therefore, there were explained each of these variables to determine the general conceptual model of DMIM in the order suggested to be implemented, as follows.See Table 5.This stage represents the how to do, to achieve the GST, just like: --Awareness.Acquisition strategy to build awareness off-site and in offline media to drive to web presences -Engagement & Loyalty.Capture and retention as a growth strategy to build customer and fan relationships to encourage repeat visits and sales.
-Desire & Experience.Strategy based on the sample and testing of a service or a product, with a novelty presentation to increase the sensations and emotions, in order to be acquired.

Results
Therefore, the final DMIM-NCM ex ante, is showed in Figure 1.The final questionnaire proposed (shown in the Appendix) containing the NCM variables and DMIM variables, was applied as a pilot with 10 specialist in digital marketing campaign designers, located at Guadalajara, Mexico, to probe the confidence test.It was obtained the Table 6.The final questionnaire (8 variables corresponding to NCM as dependent factor and 10 variables corresponding to DMIM as an independent factor) as a survey, was applied to 200 specialists (100 professor and 100 CEOs) as digital marketing campaign designers, located at Guadalajara, Mexico, during January to April 2017.It was also obtained the Pearson's Correlations amongst the variables, showed at Table 7.

It was practiced Multiple Regression Analysis by
Stepwise Method to obtain the set of variables entered/removed (a) (see Table 8).The Model Summary is showed at Table 9.Using the Stepwise method SPSS produces an Analysis of Variance (ANOVA) for each model showed at Table 10.The Table 11 shows the results of Coefficients.

Analyses and discussion
About Table 6 and according by Hinton (et al. 2004), Cronbach's alpha corresponds: 0.70 to 0.90 shows high reliability.
Regarding the Table 7, as a general rule, predictor variables can be correlated with each other as much as 0.8 before there is cause for concern about multicollinearity (Hinton, et al. 2004;Hair et al., 2010).
Regarding the Table 8, the Variables Entered/Removed table shows that the Stepwise method of regression has been used.Notice that SPSS has entered into the regression equation three variables: DMT, and MVS that are significantly correlated with NCM.
About Table 9 shows the Models: 1 and 2 where the independent variables DMT and MVS accounts for 51 %, and 65% respectively of the variance in the scores of NCM dependent variable.The R value (0.223) in Model 1 is the multiple correlation coefficients between the predictor variables and the dependent variable.As DMT is the only independent variable in this model the R value is the same value as the Pearson's correlation coefficient in the pairwise correlation matrix (See Table 7).In Model 2 the independent variables DMT and MVS are entered, generating a multiple correlation coefficient, R =.267.The Adjusted R Square adjusts for a bias in R square and is usually used.The Std. Error of the Estimate is a measure of the variability of the multiple correlation.
Regarding the Table 10, 9) dividing the Regression Sum of Squares by the Total Sum of Squares.The values for Model 1 have been used as an example: 714.120/1400.00=0.510 (see Table 10).
In Table 11 the  independent variables are held constant).As these are standardized it is possible to compare them.The tests are performed to test the two-tailed hypothesis that the beta value is significantly higher or lower than zero.This also enables us to see which predictors are significant.By observing the Sig.values in the research for Model 1 the NCM scores are significant (p < 0.05), and so on with Model 2. Hence, it is suggested to use Model 2 because it accounts for more of the variance (see Table 11).The Unstandardized Coefficients Std.Error column provides an estimate of the variability of the coefficient.
Regarding the Table 12, The Beta In value gives an estimate of the beta weight if it was included in the model at this time.The results of t tests for each independent variable are detailed with their probability values.From Model 1 the t value for DMT is significant (p < 0.05).
However with the use of the Stepwise method this variable has been excluded from the model.

Conclusions
The conclusions are: 1. -Regarding the SQ1: Which are the variables and the scheme involved?
About the variables are discussed and described using the Table 1 for NCM and Tables 2, 3 2. -About SQ2: Regarding of these variables, are there differences between the academic vision vs. the expert vision?
This issue is solved since Table 4.As a conceptual evidence, there are important issues to consider as a result of the comparison of academic vision vs. expert vision.For instance, the Strategy (STG) is cited as 19.6 % importance of academic vision vs, 9.7% of expert vision (9.9 as % difference amongst them).Revising the case of Profitability (PRO) with 16.1% importance of academic version vs. 4.9% importance of expert vision (11.2 as % difference amongst them).
Other similar case is the variable Value Proposition (VAL) with 10.7% importance of academic version vs. 4.8% importance of expert vision (5.9 as % difference amongst them).These are the first insights about the three main variables with higher academic rates vs expert rates which mean a lack, to be developed as an opportunity to the expert vision in the final DMIM.
By other side, it was obtained the higher expert differences (more than -2) with the academic vision as: Planning (PLN) with -6.3 and Goal Settings (GST) with -4.5.These are the first insights about the two main variables with higher expert rates vs. academic rates which mean a lack, to be developed as an opportunity to the academic vision in the final DMIM.
Also, it is important to consider the order of how is treated each one of the DMIM variables, suggested by the expert vision showed in Table 4.This issue is solved since Table 7, where the higher correlations (more than .3),as: NCM-MVS (.492) and NCM-DMT (.714).These variables are an empirical evidence, that only the mission and vision (MVS) and the digital marketing tools (DMT), are considered and used by the specialists in digital marketing campaign design, with main incidence on NCM; 5. -The hypothesis: The most significant DMIM variables are present in at least, on 50% of the variability of NCM.
This issue is solved using Tables 8 and 9 where the partial variability was at least, of 51% Your firm assumes responsibility for the social and the environmental issues in which it operates.This commitment is reflected in its initiatives to reduce its environmental footprint, promote the integral well-being of its personnel and respond to the social needs of its community for competitiveness.

Wellness and Inclusion (W&I)
Your firm ensures the integration of all its staff through the creation of a shared identity and responding to their physical and emotional needs in the workplace as well as in the communities where they live for competitiveness.

Knowledge (KNW)
Your firm collects, organizes, shares and analyses knowledge through the use of its resources and the skills of its staff, thereby generating the intellectual capital of the organization, capitalizing the improvement and innovation of its products, services and processes for competitiveness.Agility (AGY) Your firm responds quickly, adaptively and flexibly to the changes that occur in its internal and external environment for competitiveness.

Mission-Vision (MVS)
Your firm considers the mission and vision involved in the digital campaign for competitiveness.Your firm considers the trademark, as an strategic asset to be used in the digital campaign design for competitiveness Your firm identifies and applies the value proposition in the digital campaign design for competitiveness Mejía-Trejo, 2017;Kanang & Hongshuang,2017;SmartInsights, 2017;Kharchuk et al.,2014;Stokes, 2014;Boelsen-Robinson et al., 2014 Mejía Trejo Hongshuang, 2017;Egol et al., 2014;Kharchuk et al.,2014;Stokes, 2014;Boelsen-Robinson et al., 2014;Martin, 2017;Hase Solutions, 2015;WSI,2013 All digital marketing campaign requires objectives to be reached, for instance: -The branding positioning;-The number (real & potential) of customers database; -The sales; -The product & services (current and new ones)

Figure 1 .
Figure 1.General Conceptual of NCM and DMIM underlying factors.Source: Own.

3.-
Regarding the SQ3: Which is the final questionnaire?It was designed and concluded in detail.Please see the Appendix 4. -Regarding the SQ4: How are the main relationships between NCM and the variables of DMIM?

Mejía
Your firm determines in the digital campaign design for competitiveness, as a goal to reach, to increase: -The branding positioning -The number (real & potential) of customers database -The sales -The product & services (current and new ones
(Mejía-Trejo, 2017, Mejía-Trejo et al. 2016;Kannan. & Hongshuang, ,2017;ompetitiveness recognizes the potential of the innovation which is defined as a: "implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organisational method in business practices, workplace organisation or external relations and it involves the innovation of: product, service, marketing, process and organization"(OCDE, 2015).In this sense, digital marketing is in itself, an innovation; due, the importance to get the DMIM, is concluded a definition in this research: "as a process to design the strategy and tactics in a planned implementation, selecting a set of digital marketing tools.These should be based on missionvision, the market segmentation, goal settings and value proposition of the firm, with the performance monitoring and the profitability of the digital campaign design, in a permanent way"(Mejía-Trejo, 2017, Mejía-Trejo et al. 2016;Kannan.& Hongshuang, ,2017;

Table 2 . No. Year Authors (Year) Definition of Digital Marketing Main Digital Marketing Variables approaching
"...is a huge step forward for the marketing industry, many business owners are in such a rush to get online that they fail to develop a proper strategy for the move into the digital space."DigitalMarketingTools (DMT); Tactics (TAC); Strategy (STG); Performance (PER) 15 2012 Smith "…is a tool can help small firms to compete on more equal terms with larger rivals on marketing."Strategies (STG);

Table 2 ,
and representing the academic vision approach.See Table3.

Table 4 .
Focus Group using Delphi Panel and AHP to determine the main variables of DMIM underlying factor.

Table 5 .
It is the reason why customers turn to one company over another solving their problems or satisfying their needs.It consists of a selected bundle of products and/or services that caters to the requirements of a specific Customer Segment.In this sense, is an aggregation, or bundle, of benefits that a company others customers.It is all about of the market segmentation as target.It comprises the heart of any business model.Without (profitable) market, no company can survive for long.In order to better satisfy the market, a company may group them into distinct segments with common needs, common behaviors, or other attributes.

Table 5 .
-Effectiveness on Call to Action.Conversion strategy to achieve marketing goals of leads & sales on web presences and offline.This represents all the activities to be implemented to follow the strategies, involving mainly, the use of the digital marketing tools (DMT), for instance: It involves all the digital marketing tools, like: Search Engine Optimisation (SEO); Search Engine Marketing (SEM); Affiliate and Partner Marketing; Online advertising; Online Public Relations; Social Media Marketing; Home & Site-Wide Page Effectiveness; Landing Page Design Effectiveness; Search and Browse Page Efficiencies; Category and Product Page Efficiencies; Basket and Checkout Efficiency; Social Commerce; Content Marketing; Newsletters; eMail marketing; e-Contact Strategy; Customer and Service Support; Mobile Marketing; Augmented Reality; Virtual Reality; Wearable Marketing; Social CRM, etc.This is the step where all the tools and techniques of the tactics is programmed logistically, to be implemented in the practice.This is your overall strategy for digital marketing.Defining a strategy to integrate communications across different customer touchpoints is often forgotten.Planning involves setting goals, creating a coherent strategy to achieve them and putting in place evaluation tools in place to make sure you're on track It implies to know how well the digital campaign is working on.Practically, it involves the measurement and assessment of all the previous stages, Its support is the web analytics to obtain a full control of the digital campaign DMIM variables description.

Table 6 .
Cronbach's Alpha Test.Source: SPSS 20 as a result of the research and adapted by the author.

Table 7 .
Pearson's correlations.**.The correlation has significance of 0.01 (bilateral).Source: SPSS 20 as a result of the research and adapted by the author.

Table 8 .
Variables entered/removed.(a) Dependent Variable: NCM.Source: SPSS 20 as a result of the research and adapted by authors.

Table 11 .
Coefficients by stepwise method (a).(a) Dependent Variable: NCM.Source: SPSS 20 as a result of the research.

Table 12
shows the Excluded Variables.
MVS has been included in Model 2 it has been removed from this table.As the variable NCM scores is present in both models it is not mentioned in the Excluded Variables table.The Partial Correlation value indicates the contribution that the excluded predictor would make if it is decided to include it in the model.Collinearity Statistics Tolerance values check for any collinearity in the data.As a general rule, a tolerance value below 0.1 indicates a serious problem corresponding to DMT (Digital Marketing Tools) variable.So, it was accepted the Hypothesis. of the total manufacturing SMEs.For future studies it could be interesting to involve suppliers and customers into the DMIM-NCM construct.The third limitation is about the model of competitiveness.This could be designed with other variables involving other sources just like the Competitiveness Mexican Institute (IMCO, Instituto Mexicano de la Competitividad), or the SME Competitiveness Index (SMECO, 2017), or more sophisticated model based on the use of internet technologies.About future studies, it would be interesting to do studies considering the DMIM as dependent variable to determine and analyze the variables more significant from the NCM.It is very important to the sector of digital marketing practitioners to do strategic planning to develop the rest of the DMI model.Finally, it would be interesting to do an exploratory factor analysis, to reduce the original variables into NCM and DMIM construct, and a confirmatory factor analysis to search underlying relationships among the NCM and DMIM variables through structural equations analysis, using techniques based on license software, such as: EQS, LISREL and AMOS.
quantity Nº 20, Vol. 10 (1), 2018.ISSN 2007 -0705, pp.: 569 -591 -590 - Your firm considers the use of Digital Marketing Tools for each strategy in the digital campaign for competitiveness, such as: -Awareness (SEO/SEM; Affiliate & Partner Marketing; On line Advertising; On line PR; Social Media) -Engagement & Loyalty (Content Marketing; Newsletters & eMail Marketing; e-Contact Strategy; Customer service & support; Mobile Marketing; Social CRM; Blogging) -Desire & Experience (Augmented Reality; Virtual Reality, Wearable Marketing) -Effectiveness on Call to Action (Home & Site-Wide Page; Landing page design; Search and Browse Page; Basket and Checkout; Social Commerce)Your firm design a strong program, with schedule and times to implement the digital marketing tools, in order to obtain obtain results in the digital campaign design for competitiveness Your firm determines the KPIs for performance monitoring to determine on real time, the current performance of the digital campaign for competitiveness.Use of the Web Analytics.Your firm makes profitability analysis, on permanent way to determine on real time, the current profitability of the digital campaign for competitiveness.