Classification of phosphate glasses as a function of O/P ratio and Qn tetrahedral sites.
\r\n\tThe applications are those related to intelligent monitoring activities such as the quality assessment of the environmental matrices through the use of innovative approaches, case studies, best practices with bottom-up approaches, machine learning techniques, systems development (for example algorithms, sensors, etc.) to predict alterations of environmental matrices. The goal is also to be able to protect natural resources by making their use increasingly sustainable.
\r\n\r\n\tContributions related to the development of prototypes and software with an open-source component are very welcome.
\r\n\r\n\tThis book is intended to provide the reader with a comprehensive overview of the current state of the art in the field of Ambient Intelligence. A format rich in figures, tables, diagrams, and graphical abstracts is strongly encouraged.
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"68658",title:"Structural and Calorimetric Studies of Zinc, Magnesium and Manganese Based Phosphate and Phosphate-Silicate Glasses",doi:"10.5772/intechopen.88539",slug:"structural-and-calorimetric-studies-of-zinc-magnesium-and-manganese-based-phosphate-and-phosphate-si",body:'Over the past several decades, great interests have been considered for phosphate glasses due to their superior physical properties which impart them a several advantages over conventional silicate and borate glasses.
Phosphate-based glasses are an interesting class in the world of glass and glass ceramics owing to their higher thermal expansion, lower melting and softening temperature, higher ultra-violet transmission and optical characteristic. Phosphate glasses have potential of applications in medicine, biology, batteries, laser technology, electronic, telecommunication [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44].
In recent times, phosphate glasses have received considerable interest as a result of the synthesis of new glass composition with high chemical stability. The improvement of this quality induces the application of phosphate glasses in numerous fields of materials science, such as fast ionic conductors, semiconductors, photonic materials, hermetic seals, rare-earth in host solid state lasers and biomedical materials [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44].
Thus, phosphate and silicate glasses are the most important materials which can extensively be used for laser sources and fiber amplifiers [3].
The basic structure unit of the phosphate network is based on corner-sharing PO4 units which form chains and rings or isolated groups PO4 [6].
The covalence of the bridging oxygen atoms is responsible for the formation of phosphate groups [18, 27].
The structure of the three dimensional network is obtained by linking three oxygen atoms with others PO4 tetrahedrons. The metaphosphate groups contain two covalent bridging oxygen atoms. Whereas, the pyrophosphate groups are formed by bending only single oxygen atom with other tetrahedral site.
Recently, the structure of phosphate glass is describes using the O/P ratio [13, 18, 19, 27]. Furthermore, many investigators used the O/P ratio in order to classify the distribution of phosphate groups in the vitreous network. Table 1 reports the classification of phosphate glasses as a function of O/P ratio [45].
O/P | Classification | Qn |
---|---|---|
2.5-3 | Ultraphosphates | Q3 |
3 | Metaphosphates | Q2 |
>3 | Polyphosphates | Q2 + Q1 |
3.5 | Pyrophosphates | Q2 |
>3.5 | Orthophosphates | Q0 |
O: oxygen atom | P: Phosphorus atom | Q: Phosphorus tetrahedral sites |
Classification of phosphate glasses as a function of O/P ratio and Qn tetrahedral sites.
For 2.5 < O/P < 3, the glass network is described by the distribution of ultraphosphate groups [19].
The metaphosphate groups are obtained for O/P ratio equal to 3. The glass network is described by the connection of PO4 tetrahedral anions with neighbors in order to form chains and rings.
For polyphosphate glasses, the O/P ratio is between 3 and 3.5. The structure is described by chains formed by PO4 tetrahedral anions joined with others.
For O/P = 3.5, the structure is obtained by forming the phosphate dimers such as pyrophosphate groups in which two PO4 tetrahedral shared one bridging oxygen [19]. For 3.5 < O/P < 4, the isolated PO43− units are formed such as orthophosphate.
The increase of O/P ratio induces the depolymerization of phosphate groups which suggests the shortening of the average chain length [19].
The network connectivity of phosphate compound is conventionally expressed as Qn tetrahedral sites (n = 0…3), when n is the number of bridging oxygen (BO) per Q unit to neighbor phosphate tetrahedral [2, 23, 28]. Q0 represents orthophosphates (PO43−), Q3 is pure P2O5 and Q2 (metaphosphates) and Q1 (pyrophosphates) are intermediate structures [11, 13, 16, 19, 20]. Figure 1a shows the nomenclature of phosphate groups as Qn tetrahedral sites also with the variation of an O/P ratio [45].
a. Infrared spectra of (50-x)Na2O-xZnO-50P2O5 glasses: (a) 0 mol% ZnO, (b) 5 mol% ZnO, (c) 10 mol% ZnO, (d) 15 mol% ZnO, (e) 20 mol% ZnO, (f) 25 mol% ZnO, (g) 30 mol% ZnO, (h) 33 mol% ZnO. b. Infrared spectra of (0.9-x)NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 mol) glasses: (a) 0 mol SiO2, (b) 0.02 mol SiO2, (c) 0.04 mol SiO2, (d) 0.06 mol SiO2, (e) 0.08 mol SiO2, (f) 0.10 mol SiO2.
For silicon-oxygen networks, n varies between 0 and 4, where Q0 represents orthosilicates (SiO44−), Q4 is pure SiO2 and Q3, Q2 and Q1 represents the intermediate silicate structures [2, 20].
Glasses results from many possible combinations of network-forming oxides together with one or several modifier or intermediate oxides which lead to a special physical properties [22].
Introducing alkali metal oxide or divalent metal oxide to the glass network induces the fundamental optical absorption edge falls in the UV region bellow 400 nm which meets with the requirement for desirable applications in optical systems [22]. These additions not only enhance the chemical durability of the phosphate glasses but also can impart special functions to the glasses and expand the glass application fields.
Furthermore, alkali phosphate glasses have attracted more attention due to their mixed electronic ionic conductivity, low melting point and strong glass-forming character [4]. Among the phosphate-based glasses those containing calcium, magnesium, sodium and zinc have received great attention due to their excellent optical properties, high refractive index, low dispersion and good transparency in the UV and IR region [1]. With the decrease of P2O5 content, the glass become more resistant to moisture attack but restricts the glass formation areas. Thus, MgO oxide was incorporated in order to overcome these problems [23].
Nevertheless, phosphate-based glasses containing transition metal ions are scientifically interesting materials due to their attractive properties which can be used in many technological applications including electronic and electro-optical devices [21].
In fact, transition metal oxides can be dissolved easily in phosphate glasses which exhibit one than more oxidation sates [21, 25, 30].
For glasses doped with manganese ions. These latter are presented in the +2 or +3 oxidation states. The content of Mn3+ ions in the glass leads to the staining glass in the color range from light to dark purple depending on the concentration [30]. This characteristic coloration could be explained by the d-d electronic transitions. This color can be associated with the broad absorption band in the visible region at 520 nm, which pertains to the Mn3+ ions [25, 30]. This behavior allows obtaining additional luminescence bands in the red spectral region that shift LED emission from cool white to warm light [30].
Contrary to Zn2+ and Mg2+ ions presented in one oxidation state, spectral-luminescent properties of manganese ions in phosphate glasses allow them to be good candidates for interesting optical applications [21, 25, 30].
In borate glasses, manganese exists mainly as Mn3+ ions with octahedral coordination in glass networks whereas in silicate and germinate glasses, it identified as Mn2+ ions with both tetrahedral and octahedral coordination [21].
Referring to literature, Montagne et al. have been studied the zinc phosphate glasses with a general formula (100-x)NaPO3-xZnO with 0 < x < 33.3 mol% using 31P MAS-NMR, 31P NMR of liquid sample, visible spectroscopy, refractive index measurements, density evolutions, Tg variations, activation energy, chemical durability and chemical analysis [1, 2, 46, 47].
The obtained results revealed the distortion of metaphosphate chains (Q2) which suggests the formation of phosphate dimmers (Q1) [46, 47].
Moreover, Zotov et al. have been studied the manganese phosphate glasses with a general formula (MnO)x(NaPO3)1-x when x = 0.0, 0.024, 0.048 and 0.167 mol [48].
These investigations have been performed using X ray diffraction, EXAFS and Raman spectroscopy. The increase of MnO content causes the depolymerization of metaphosphate chains leading to the decrease of the average chain length [48].
For zinc phosphate-silicate glasses, chemical compositions of the prepared glasses, picked from literature, have been chosen with higher level of SiO2 and lower P2O5 content [2, 20, 24, 34].
Aguiar et al. have been studied the Na2O-MgO-CaO-P2O5-SiO2 bioactive glasses using Raman, 31P MAS-NMR and 29Si MAS-NMR spectroscopies. The glass compositions were prepared with varying SiO2 content from 25 to 54 mol% and the P2O5 proportion from 2 to 11 mol% [2, 20, 24].
Furthermore, Szumera et al. have detailed the effect of MoO3 addition on silicate phosphate glasses using spectroscopic analysis such as FTIR, Raman and 31P MAS-NMR. The molar content of SiO2 decreases from 41.6 to 39.6 mol% and the P2O5 proportion increases from 5.7 to 7.8 mol% [44]. The obtained results revealed the cleavage of oxygen bridges which suggests that acts as a network modifier [44].
In our knowledge, the thermochemical data of zinc-, magnesium-, and manganese-based phosphate and phosphate-silicate glasses are rare in literature. For this purpose, the aims of this research were to study the correlations between structural changes, thermal investigations, optical properties and thermochemical behaviors of these glassy compounds with the incorporation of zinc, magnesium and manganese oxides.
However, it is well known that phosphate glasses have a poor chemical durability, volatile nature and hygroscopic character. These disadvantages decrease their stability which limits their use in technological applications [5].
The addition of alkali and alkaline earth cations with the decease of phosphorus content can depolymerize the glass network which suggests the cleavage of P▬O▬P bridges [28].
The incorporation of certain network modifier cations (Na+, K+, Mg+, Ca2+…) disrupts the glassy network, leading to the structure depolymerization and the formation of non-bridging oxygen atoms (NBO), also named terminal oxygen (OT) [2, 4].
The addition of transition metal oxides (CuO, MgO, ZnO, MnO, CaO…) into the vitreous network (TMO) disrupted the P▬O▬P bridges leading to the structure depolymerization and the formation of non-bridging oxygen atoms (NBO) [2, 5, 6, 8, 9, 13, 21, 25, 32, 35]. Which induces the formation of P▬O▬M bonds replacing the easily hydrosoluble P▬O▬P bridge that improve the chemical durability of the phosphate network [21].
Among several oxides mentioned above, zinc oxide gained considerable attention because Zinc doped glasses find numerous applications in optic field can be used as LED light sources and substrates for optical waveguides. It can also play an important role in bone formation and mineralization [3].
Zinc phosphate compositions are chemically durable, have processing temperatures under 400°C and can be co-formed with high temperature under 400°C polymers to produce unusual organic/inorganic composites [19].
In recent years, there have also appeared some publications on the influence of the addition of ZnO on the structure of glasses with a mixed phosphate-silicate structure [7, 34].
Among the wide class of phosphate glasses, ZnO-based glasses have low glass transition temperature in the region of 280–380°C and significantly high chemical durability [14].
Furthermore, the addition of ZnO to the glass network is expected to improve the chemical stability of the structure. It can also ameliorate the electrical, optical and magnetic properties of glasses due to the appearance of P▬O▬Zn ionic bond which induces the increase in the compactness and the rigidity of the glass network [5, 13, 32, 35].
In fact, in crystalline solid compound, the structure was described by a repetitive arrangement of a large scale patterns contrary to amorphous structure which exhibits a short range order.
Additionally, ZnO is an intermediate oxide. It can act as former or modifier network depending on his content in phosphate network. When it occupies the tetrahedral sites by forming ZnO4 structural units, ZnO oxide plays the role of glass former. But, when it occupies the octahedral sites coordinated, ZnO oxide acts as glass modifier [7, 25].
The literature concerning zinc in various mixed oxide compounds revealed that with the exception of few structures, zinc has tetrahedral oxygen ligancy and the zinc-oxygen distance varies only slightly [25, 34]. It was concluded that zinc had a coordination number of six in borate and silicate glasses [34].
Interestingly, the heat treatment of glasses at temperature higher than their glass transition (Tg) or crystallization temperature (Tc) improves the electrical conductivity of the glassy compound due to the “structural relaxation” of the glass network [36].
ZnO is widely used in glass production because it improves the glass quality by enhancing mechanical properties and chemical durability and by reducing the thermal expansion [7]. Zinc is a microelement that plays an important role in the bone formation and mineralization. Zinc containing glasses and glass ceramics has been developed for bone engineering applications [7]. The small size of Zn ion (0.74 Å) helps it to locate itself into smaller cavities of the network [37].
Moreover, MgO oxide is of interest from a biological viewpoint because Mg2+ is known to play a physiological role in positively influencing bone strength which can be substituted into apatites [13, 18].
The bioactive behavior of magnesium rich glasses is identified as their ability to react chemically with living tissues, forming with them mechanically strong and lasting bonds. These bone bondings are attributed to the formation of an apatite-like layer on the glass surface, with composition and structure equivalent to the mineral phase of bone.
In fact, bioactive glasses have received special attention due to their better bone bonding ability in vivo. Due to their good bioactive and tailorable degradation properties, these glasses can be used for various biomedical applications such as bone graft, filler, dental, implant coating.
Furthermore, by increasing the concentration of modifier oxides, electrical conductivity of the glass increases. This property was probably influenced by the structural changes resulting from the disruption of the glass network which affected the mobility of the cations and anions when the modifying oxide was progressively introduced [38].
Glasses containing transition metal oxides possess interesting electronic, optic and magnetic properties due to the ability to exist in more than one valence state. However, the electronic conduction of these glassy compounds is resulting from the electronic transfer of cation that exists in different valence sates [4, 38].
Compared with phosphate glasses, silicate glasses exhibit superior chemical resistance which makes them compatible with the fabrication process in the development of optical devices [3].
Silicate glasses are an attractive host matrix for transition metal ions due to their excellent optical and mechanical properties, good chemical durability, good chemical stability and low thermal expansion coefficient leading to strong thermal resistance [6]. Silicate glasses have many advantages rather than phosphate glasses. Silicate-based glasses are chemically durable, thermally stable and optically transparent at excitation and lasing wavelength. However, the higher viscosity of these glasses allows the glass to be formed without crystallization process. In addition, these amorphous materials are useful in optics as lenses or beam splitters in optical telecommunications, micro- and optoelectronics and in near IR-windows [6, 39, 40].
Glasses of the (50-x/2)Na2O-xMO-(50-x/2)P2O5 (M = Zn, Mn, Mg) (0 ≤ x ≤ 33 mol%), (50-x)Na2O-xMO-50P2O5 (M = Zn, Mn) (0 ≤ x ≤ 33 mol%), and (0.9-x)NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 mol) compositions have been prepared using a melt quenching technique.
A series of glasses were prepared by varying the MO (M = Zn, Mn, Mg) content from 0 to 33 mol% using reagent grade compounds, NaH2PO4, NH4H2PO4, MgO, ZnO, MnCO3 with a high purity (99% purity), in the suitable proportions.
The mixture corresponding to the desired compositions was heated in platinum crucible at 400°C in order to evaporate water and start the condensation of phosphate groups. The temperature was then progressively increased to 750–900°C, depending on glass composition, and held constant for 30 min. The batch was finally quenched to room temperature under air atmosphere in order to produce vitreous compounds.
Using the same technique, phosphate-based silicate glasses with a general formula (0.9-x)NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 mol) have been synthesized using reagent grade compounds, NaH2PO4.H2O ZnO and SiO2 with a high purity (99% purity) with the desired compositions.
The mixture was then putted in platinum crucible at 400°C for 1 hour in order to eliminate residual water. The temperature was raised progressively to 1200°C for 30 min in order to homogenize the melting mixture. Finally, the batch was quenched to room temperature under air atmosphere in order to obtain glasses.
The amorphous state was confirmed by X-ray diffraction. All the products were annealed at about 20°C below their glass transition temperature for 2 hours in order to eliminate internal tensions and get a more homogenized sample.
Phosphorus, sodium, magnesium, zinc, manganese and silica were analyzed by inductively coupled plasma atomic emission spectroscopy (Jobin Yvon Ultra C).
Density of glass is a strong function of its composition and its intrinsic property which shed light on the short range structure of the glassy material [4, 37]. This work presents a series of glasses with various amounts of modifier oxides. These modifies, depending on their polarity and size, tend to occupy the interstices within the network and form new bonds resulting in a change in the structure and properties of the glass [4, 37].
Glass density measurements have been determined using the standard Archimedes method using diethyl orthophthalate as immersion fluid. The relative error of these measurements is ±3%.
The molar volume of glasses has been calculated from the density (Vm = M/ρ) and the molar weight.
For (50-x/2)Na2O-xMO-(50-x/2)P2O5 (M = Zn, Mn, Mg) where 3 ≤ O/P ≤ 3.49 and (50-x)Na2O-xMO-50P2O5 (M = Zn, Mn) with O/P = 3 (0 ≤ x ≤ 33 mol%) series glasses, the density increases gradually with the incorporation of MO oxide. The increase in density indicates that the MO oxide reticulate the vitreous network because P▬O▬M bond are more ionic than P▬O▬P [11, 12, 13, 15, 29, 32, 33, 42].
Table 2 shows that the molar volume deceases monotonically with the increase of ZnO content for phosphate glasses.
Glass composition | X | Density (g cm−3) | Vm (cm3 mol−1) | Tg (°C) | Tc (°C) | ΔT (°C) |
---|---|---|---|---|---|---|
(50-x/2)Na2O-xZnO-(50-x/2)P2O5 | 0 5 10 15 20 25 30 33 | 2.43 ± 0.07 2.47 ± 0.07 2.62 ± 0.08 2.70 ± 0.08 2.75 ± 0.08 2.85 ± 0.09 2.93 ± 0.09 2.98 ± 0.09 | 42.00 ± 1.30 41.00 ± 1.23 38.15 ± 1.14 36.63 ± 1.10 35.60 ± 1.10 34.00 ± 1.02 32.70 ± 1.00 32.00 ± 1.00 | 280 ± 5 - 285 ± 5 - 287 ± 5 294 ± 5 306 ± 5 314 ± 5 | 290 ± 5 - 371 ± 5 - 368 ± 5 439 ± 5 456 ± 5 446 ± 5 | 10 - 86 - 81 145 150 132 |
Density, molar volume, glass composition, glass transition temperature Tg, Tc, ΔT of (50-x/2)Na2O-xZnO-(50-x/2)P2O5 (0 ≤ x ≤ 33 mol%) phosphate glasses.
The decrease in the molar volume for (50-x/2)Na2O-xMO-(50-x/2)P2O5 (M = Zn, Mn, Mg) where 3 ≤ O/P ≤ 3.49 and (50-x)Na2O-xMO-50P2O5 (M = Zn, Mn) with O/P = 3 (0 ≤ x ≤ 33 mol%) series glasses could be explained by the higher field ΔF (ΔF = Z/r2; with z is the valence cation and r is the ionic radius) of M2+ compared to that of Na+ [11, 12, 13, 15, 29, 32, 33, 42].
The decrease in the molar volume is extensively related to structural changes due to the incorporation of MO oxide that disrupted the average chain length of metaphosphate resulting from the following reaction:
The variation of these properties is closely related to the structural modification when M2+ ion is progressively introduced.
The effect of composition on the density and molar volume for zinc phosphate-silicate glass, having a general formula: (0.9-x)NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 mol), shows that the replacement of NaPO3 by SiO2 oxide induces a decrease of density as mentioned Table 3. This is due to the lower molecular weight of SiO2 than that of NaPO3 (
Glass composition | x | Density (gcm−3) | Vm (cm3 mol−1) | n | Tg (°C) | Tc (°C) | ΔT (°C) |
---|---|---|---|---|---|---|---|
(0.9-x)NaPO3-xSiO2-0.1 ZnO | 0 0.02 0.04 0.06 0.08 0.1 | 2.60 ± 0.10 2.58 ± 0.10 2.57 ± 0.10 2.55 ± 0.10 2.55 ± 0.10 2.53 ± 0.10 | 38.50 ± 1.20 38.35 ± 1.20 38.24 ± 1.20 38.13 ± 1.10 38.00 ± 1.10 37.80 ± 1.10 | 1.44 ± 0.05 1.45 ± 0.05 1.46 ± 0.05 1.47 ± 0.05 1.48 ± 0.05 1.49 ± 0.05 | 280 ± 5 287 ± 5 289 ± 5 293 ± 5 294 ± 5 296 ± 5 | 371 ± 5 400 ± 5 427 ± 5 439 ± 5 475 ± 5 466 ± 5 | 86 113 138 146 163 170 |
Density, molar volume, refractive index, glass composition, glass transition temperature Tg, Tc, ΔT of (0.9-x)NaPO3-xSiO2-0.1 ZnO (0 ≤ x ≤ 0.1 mol) glass series.
As the same for the molar volume, this quantity decreases monotonically with the incorporation of SiO2 oxide (Table 3). This variation indicates that SiO2 oxide reticulates the vitreous network suggesting the increase in the rigidity of the structure.
Furthermore, the regular decrease in the molar volume is closely related to the nature of bending in the glass structure, because P▬O▬Si are more ionic than P▬O▬P bridges, suggesting the compactness of the vitreous network [11, 12, 13, 15, 29, 32, 33, 42].
Generally, the glass transition phenomenon occurs due to the increasing viscosity of the overcooled liquids so Tg strongly depends on the polymerization ratio of the network [40].
The glass transition temperatures were determined on 40–50 mg of samples using DSC-ATD Netzsch 404 PC with a 10°C/min heating rate (accuracy ±5°C).
With increasing MO content, glass transition temperature, Tg, increases linearly for all glass compositions as mentioned Table 2.
This behavior is undoubtedly corresponding to some changes in the nature of bonding in the structural network. This parameters is strictly related to the bond strength of the glass network which can be explained in terms of bond length (which is the charge divided by the square of the cation-oxygen distance) affected by the cation field strength resulting in a higher of Tg values [11, 12, 13, 15, 29, 32, 33, 42].
These variations indicate the progressive increase of the reticulation and the rigidity of the glass network by gathering the non-bridging oxygen atoms (NBO) with the increase of MO proportion. As a result the formation of P▬O▬M bonds suggesting the increase in the rigidity and the compactness of the structure that ameliorate the chemical durability of glasses.
A similar behavior has been observed for zinc phosphate-silicate glasses [11, 12, 13, 15, 29, 32, 33, 42].
According to Dietezel, the thermal stability of glasses (ΔT) can be expressed by the temperature difference between Tg and Tc, ΔT = Tc–Tg, in which Tg and Tc are the glass transition and crystallization temperature. Increasing ΔT delays the nucleation process, indicating a better stability of the glass [29].
Inspecting these data, one can note that the undoped ZnO and SiO2 oxide glass matrix has the lowest thermal stability indicating a tendency towards crystallization as shown Tables 2 and 3.
∆T increases when SiO2 oxide is progressively added, indicating a better stability of the glass. The larger value of ∆T, the stronger is the inhibition to nucleation and crystallization process as mentioned Table 3 [12, 13, 15, 29, 32, 33].
The ability to control the physical properties of glasses, e.g., the refractive index, by variation in glass composition suggests the feasibility of chemically controlling the materials according to the needs of a given application [3].
For glassy compounds, refractive index is a fundamental parameter that strongly relevant to optical devices performance and reliability in the basic elements in all optical instruments. Hence, a large number of researchers have carried out investigations to ascertain the relation between refractive index and glass composition [10].
This parameter is one of the fundamental properties of materials, because it is closely related to the electric polarizability of ions and the local field inside the material [3, 10, 11].
The carat eristic feature of phosphate glasses is the low value of the refractive index that is in the order of 1.49. The variation of this quantity for zinc phosphate-based silicate glasses is presented in Table 4. Inspecting these data, one can note that n increases from 1.44 to 1.49 when x rises from 0 to 10 mol% of SiO2 oxide which suggests that the refractive index of glassy compounds depends essentially on the density of glass network [3, 10, 11].
Glass composition | n | αm (Å3) | Rm (cm3 mol−1) | Eopt (ev) | (αm/Vm) × 10−25 | |
---|---|---|---|---|---|---|
0.9NaPO3-0.1ZnO | 1.44 | 4.06 | 10.23 | 1.50 | 1.05 | |
0.88NaPO3-0.02SiO2-0.1ZnO | 1.45 | 4.07 | 10.30 | 1.70 | 1.06 | |
0.86NaPO3-0.04SiO2-0.1ZnO | 1.46 | 4.13 | 10.40 | 2.00 | 1.08 | |
0.84NaPO3-0.06SiO2-0.1ZnO | 1.47 | 4.15 | 10.50 | 2.25 | 1.09 | |
0.82NaPO3-0.08SiO2-0.1ZnO | 1.48 | 4.18 | 10.55 | 2.35 | 1.10 | |
0.8NaPO3-0.1SiO2-0.1ZnO | 1.49 | 4.20 | 10.60 | 2.35 | 1.11 |
Refractive index, molar refractivity (Rm), molar electronic polarizability (αm) and band gap energy of (0.9-x)NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 Mol) glass series.
In addition to density, many parameters can prevails the refractive index such as density, polarizability of the first neighbor ions coordinated with it (anion), coordination number of ion, electronic polarizability of the oxide ion and optical basicity [3, 10, 11]. The molar refractivity (Rm) was estimated from the refractive index and the molar volume (Vm) using the Lorenz-Lorenz Equation [3, 10, 11]:
The molar electronic polarizability αm was calculated using the relation of Clasius-Mosotti as follows [3, 10, 11]:
where the value of
Table 4 shows that Rm increases from 10.23 to 10.60 m3 mol−1 and αm are between 4.06 and 4.20 Å. These variations indicate that the refractive index as a function of both density and molar electric polarizability of glassy compounds [3, 10, 11].
In the present work, we found that the refractive index (n) depends on the ratio (
For Na2O ionic-based glasses, the polarizability of oxygen ions has the smaller value (
Duffy et al. suggested that increasing the optical basicity (
The decrease in the molar volume for zinc-based phosphate-silicate glasses induces an increase in the rigidity and the compactness of the vitreous network, when SiO2 oxide is progressively introduced, because Si▬O▬P bonds are more ionic that P▬O▬P.
Infrared spectra of the glass series have been recorded by Perkin-Elmer (FTIR 2000) spectrometer using KBr pellets in the frequency range 400–4000 cm−1 at room temperature. The samples were prepared by grinding about 9 mg of glass powder with 300 mg of spectroscopic grade dried KBr.
For undoped zinc phosphate glasses, NaPO3, FTIR spectrum revealed an asymmetric and symmetric stretching vibration band of PO2 groups in metaphosphate chains situated respectively at 1280 and 1150 cm−1. The asymmetric and symmetric stretching vibration bands of PO3 chain end groups situated respectively at 1100 and 1000 cm−1 [12, 13, 15, 29, 32, 33].
Furthermore, the asymmetric and symmetric stretching vibrations of P▬O▬P bands are around 880, 780 and 720 cm−1. The deformation mode of P▬O▬(PO43−) groups at 535 and 480 cm−1 [12, 13, 15, 29, 32, 33].
FTIR spectra of (50-x/2)Na2O-xZnO-(50-x/2)P2O5 and (0.9-x)NaPO3-xSiO2-0.1ZnO glasses are shown in Figures 1a and b.
As MO oxide is introduced, the asymmetric band of PO2 shifts from 1280 cm−1 shift to lower frequency as showed Figures 1a and b indicating the depolymerization of phosphate chains when x increases [12, 13, 15, 29, 32, 33].
For higher ZnO content, FTIR spectra revealed the displacement of the asymmetric stretching mode vibration of the P▬O▬P band from 880 to 920 cm−1 when x rises from 0 to 33 mol%. This result can be correlated to the increase in the covalence character of P▬O▬P bridges when monovalent cation Na+ was replaced by divalent cation (such as Zn2+) [12, 13, 15, 29, 32, 33].
It may be also attributed to the shortening of phosphate chain length due to the higher field strength and the size of the metallic cation, when the ratio O/P increases for (50-x/2)Na2O-xZnO-(50-x/2)P2O5 [12, 13, 15, 29, 32, 33].
The FTIR spectra of NaPO3 glasses revealed also two bands in the frequency range 780–720 cm−1 which are attributed to the presence of two P▬O▬P bridges in metaphosphate chains based on (P2O6)2− groups (Figure 1a and b) [12, 13, 15, 29, 32, 33]. However, it is interesting to note that for the series glasses containing 30 and 33 mol% ZnO FTIR spectra exhibit only a single band at 740 cm−1 assigned to the P▬O▬P linkage in pyrophosphate group (P2O7)4− (Figure 1a) [12, 13, 15, 29, 32, 33]. These spectral changes depend essentially on the glass composition.
On the other hand, this result could be explained by disruption of the infinite metaphosphate chains when MO oxide is gradually incorporated suggesting the depolymerization of the skeleton of (P2O62−)∞ into short phosphate groups such as: P2O74− and PO43− [12, 13, 15, 29, 32, 33].
Similar FTIR spectra have been recorded for zinc phosphate-based silicate glasses, with a general formula: (0.9-x)NaPO3-xSiO2-0.1ZnO, (0 ≤ x ≤ 0.1 mol) as mentioned Figure 1b.
The FTIR spectra of zinc phosphate-based silicate glasses revealed the appearance of some bands assigned to phosphate-silicate glasses in the range 1000–1300 cm−1 as shown Figure 1b. The asymmetric stretching vibration bands of silicate and phosphate tetrahedron is located at 1100 cm−1. It seems that a smaller band at 840 cm−1 is attributed to the symmetric stretching vibration of O▬Si▬O in metasilicate (Q2) when SiO4 tetrahedron shared two oxygen with their neighbor (Si▬O▬2NBO). The symmetric stretching vibration band Si▬O▬Si is about 1080 cm−1. In addition, the bending vibration of Si▬O▬Si and O▬Si▬O bonds is around 460 cm−1 [12, 13, 15, 29, 32, 33].
When SiO2 is incorporated, FTIR spectra revealed the displacement of the asymmetric stretching mode vibration of PO2 band from 1280 to 1250 cm−1 when x increases from 0 to 10 mol%. This result can be probably due to the depolymerization of the infinite metaphosphate chains with the addition of SiO2 oxide.
Furthermore, for 0.8 NaPO3-0.1 SiO2-0.1 ZnO glass composition, FTIR spectrum revealed the appearance of a only a single band at 760 cm−1 assigned to P▬O▬P bands in phosphate dimers (P2O74−) as mentioned Figure 1b. These spectral changes have been observed for magnesium and manganese phosphate glasses that could be attributed to the reduction of infinite phosphate groups (P2O62−) into shorter phosphate groups such as: P2O74− and PO43− [12, 13, 15, 29, 32, 33].
Raman spectroscopy is an adequate technique for the analysis of glass matrix structure. Raman bands are generally characteristics of structures involving chains of linked tetrahedral that may be found in crystalline, glassy phosphates and silicate because it can detect the local changes in the environment of Si▬O▬Si and P▬O▬P bonds [1, 2].
The Raman spectra were recorded on powder of glasses using a Labram HR800 micro Raman model operating in the 50–4000 cm−1 range at room temperature equipped with an internal He-Ne laser source (λ = 488 nm).
Figure 2a reported the Raman spectra of zinc phosphate glasses having a general formula (50-x/2)Na2O-xZnO-(50-x/2)P2O5 (0 ≤ x ≤ 33 mol%) with an O/P ratio varies from 3 to 3.49.
a. Raman spectra of (50-x/2)Na2O-xZnO-(50-x/2)P2O5 glasses: (a) 0 mol% ZnO, (b) 10 mol% ZnO, (c) 20 mol% ZnO, (d) 25 mol% ZnO, (e) 30 mol% ZnO, (f) 33 mol% ZnO. b. Raman spectra of (0.9-x)NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 mol) glasses: (a) 0 mol SiO2, (b) 0.02 mol SiO2, (c) 0.04 mol SiO2, (d) 0.06 mol SiO2, (e) 0.08 mol SiO2, (f) 0.10 mol SiO2.
For undoped zinc phosphate glasses, Raman spectrum revealed a large band around 1274 cm−1 and three weaker peaks at 1164, 685, and 380 respectively as shown in Figure 2a.
The bands located at 1274 and 1164 cm−1 are assigned to the asymmetric and symmetric vibrations of PO2 groups in metaphosphate chains (Q2) [12, 13, 15, 29, 32, 33]. The large band at about 685 cm−1 is attributed to the symmetric vibration of the bridging oxygen linking two PO4 tetrahedrons (P▬O▬P) in metaphosphate chains [12, 13, 15, 29, 32, 33]. The low frequency attributed to the faint band at 380 cm−1 is related to the bending motion of phosphate polyhedral [12, 13, 15, 29, 32, 33].
With increasing MO content, we observe some decrease of the overall background located at 600–800 cm−1 and 1100–1300 cm−1. These spectral changes can be correlated to the distortion of P▬O▬P band which induces the shortening of the infinite metaphosphate chains suggesting the formation of pyrophosphate groups (Q1) with the increase of the O/P ratio [12, 13, 15, 29, 32, 33].
From Figure 2a, it seems that the intensity of bands located at 1164 and 685 cm−1 decrease when MO oxide is progressively introduced. However, the Raman spectra revealed the displacement of these bands to higher frequencies to 1180 (d, e) and 780 cm−1 (d, e, f) with 30 and 33 mol% of MO level. This result can be probably due to the higher π character of P▬NBO bands that induces the depolymerization of infinite metaphosphate chains when MO oxide is progressively added.
Similar Raman spectroscopic analysis have been recorded for (0.9-x) NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 mol) glass compositions as shown in Figure 2b.
The incorporation of SiO2 oxide to the phosphate network generates the appearance of the asymmetric band around 850 cm−1 attributed to Si▬O▬Si bending modes. The band located at 560 cm−1 is attributed to Si▬O▬Si intertetrahedral linkages obtained in calcium and magnesium rich silicate glasses in order to link the distorted metaphosphate groups when SiO2 oxide is added [12, 13, 15, 29, 32, 33].
The 31P MAS-NMR spectra of (50-x/2)Na2O-xZnO-(50-x/2)P2O5 glasses are shown in Figure 3.
31P MAS-NMR spectra of (50-x/2)Na2O-xZnO-(50-x/2)P2O5 glasses: (a) 0 mol% ZnO, (b) 5 mol% ZnO, (c) 10 mol% ZnO, (d) 15 mol% ZnO, (e) 20 mol% ZnO, (f) 25 mol% ZnO, (g) 30 mol% ZnO, (h) 33 mol% ZnO.
The characteristic features of undoped zinc phosphate glasses are isotopic peaks at −21 and −6.88 ppm. The first one is attributed to the Q2 tetrahedral sites in metaphosphate groups and the second is assigned to the Q1 groups at the end of chain [12, 13, 15, 29, 32, 33].
Based on literature, the chemical shift at +1.4 ppm is attributed to NaPO3 chain end groups. From Figure 3, one can note the appearance of two isotopic peaks around 21–−18.80 ppm and −6.88–−3.90 ppm for the glass series.
When the MO oxide is introduced to the vitreous network, the intensity peak attributed to Q1 tetrahedral sites increases and becomes the major spectral feature [12, 13, 15, 29, 32, 33]. These results are in good agreement for zinc phosphate glasses with the structural study of (100-x)NaPO3-xZnO glasses (0 ≤ x ≤ 33.3 mol%) performed by Montagne et al. [12, 13, 15, 29, 32, 33].
From Figure 3, it seem that 31P MAS-NMR spectra exhibit only single peak assigned to Q1 tetrahedral sites attributed to pyrophosphate groups resulted from the distortion of metaphosphate chains when MO oxide is progressively introduced.
Furthermore, the phosphorus chemical shift depends essentially on the phosphorus-ligand bond (P-O) for phosphate compounds and the electronic density of the non-bridging oxygen (NBO). Figure 3 mentioned that the Q2 chemical shift becomes less shielded when MO is added. This decrease is probably due to the higher electronegativity of M2+ compared to Na+ also the increase of π fraction of P-NBO resulting from the decondensation of phosphate chains when ZnO is incorporated. This suggests that Zn2+ ions are only bonded to pyrophosphate groups described by Q1 tetrahedral sites. As a result the increase of shielding Q2 sites from −21 ppm in NaPO3 glass to −18.80 ppm in 33.5 Na2O-33 ZnO-33.5 P2O5 glass composition [12, 13, 15, 29, 32, 33].
UV-VIS-NIR absorption spectra of the glassy compounds were carried out by means of Perkin-Elmer Lambda 950 spectrometer at room temperature under air. Optical measurements were recorded in the range of 200 and 1800 nm.
Optical absorption, particularly the absorption edge, is useful for the investigation of optically-induced transitions and for getting information about the band gap energy [4, 10, 11, 14, 16, 37, 41]. This parameter is very interesting for the applications of the materials to be studied. It is known that the optical transition occurs through the region between conduction and valence bands (optical band gap) directly or indirectly.
However, the optical transition involves an energy transfer caused by electron transitions between conduction and valence bands [4, 10, 11, 14, 16, 37, 41].
The optical absorption coefficient α(hν) of the prepared glasses was calculated at different wavelengths by using the relation [4, 10, 11, 14, 16, 37, 41]:
where d represents the thickness of the glass composition and ln
For the optical measurements, one can note the absence of the absorption sharp edge which characterizes the vitreous nature of the prepared glasses [4, 10, 11, 14, 16, 37, 41].
According to Davis and Mott, the expression of the absorption coefficient α (ν) as a function of photon energy (hν) for direct and indirect optical absorption, was given by the relation as follows:
where
A: an energy-independent constant
Eopt: the optical band gap energy
n: a constant which determines the type of the optical transition. For direct allowed transition n = 2 and in the case of indirect allowed transition n=
For glassy materials, the indirect transitions are valid according to Tauc relations [4, 10, 11, 14, 16, 37, 41].
Figure 4 represents the variation (αhν)2 versus photon energy (hν) for (0.9-x)NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 mol) series glasses.
The (αhν)2 as a function of photon energy of hν of (0.9-x)NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 mol) glasses: (a) 0 mol SiO2, (b) 0.02 mol SiO2, (c) 0.04 mol SiO2, (d) 0.06 mol SiO2, (e) 0.08 mol SiO2, (f) 0.10 mol SiO2. *Obtaining the lines corresponding to the curves of (αhν)2 against photon energy (hν) is probably due to the superposition effect for all the glass compositions.
The values of indirect optical band gap energy (Eopt) were determined by the extrapolation of the linear region of (αhν)2 against photon energy (hν) plots at (αhν)2 = 0. This latter shows that the Eopt increases with the incorporation SiO2 from 1.5 to 2.35 eV. This quantity is not only influenced by the chemical composition also by the structural rearrangement in the glass matrix [4, 10, 11, 14, 16, 37, 41]. Figure 5 shows clearly that the Eopt values dependent strongly on the composition of the glass also on the oxygen bonding in the vitreous network [4, 10, 11, 14, 16, 37, 41]. Any changes of oxygen bonding suggesting the formation of non-bridging oxygen (NBOs) causes a change of the absorption characteristics of the glass [4, 10, 11, 14, 16, 37, 41].
Variation of optical band gap energy of (0.9-x)NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 mol) glass series.
The higher energy is required to excite an electron from bridging oxygen (BO) than from non-bridging oxygen (NBO). As a result the increase in Eopt values [4, 10, 11, 14, 16, 37, 41].
When x increases from 0 to 10 mol% of SiO2, the optical band gap energy rises from 1.5 to 2.35 eV. This variation can be explained by the structural modifications which suggest the distortion of metaphosphate chains inducing the increase in the number of non-bridging oxygen (NBOs).
Because the NBOs bonds are predominantly ionic character and consequently have lower bond energies [34]. The higher value of the band gap energy revealed the increase of the cross-linking network due the introduction of SiO2 [4, 10, 11, 14, 16, 37, 41].
From Figure 5, it seems that the Eopt is in the order of 2.35 eV for 0.82 NaPO3-0.08 SiO2-0.1 ZnO and 0.8 NaPO3-0.1 SiO2-0.1 ZnO glass compositions. This result can be correlated to the structural changes due to the formation of P-O-Si ionic bands [11].
The calorimetric study was performed by determining the energy resulting from the dissolution of the glasses in a suitable solvent [11, 12, 13, 15, 29, 32, 33, 42].
Phosphate glasses are soluble in mineral acids [11, 12, 13, 15, 29, 32, 33, 42]. Furthermore, the dissolution process has been carried out in order to find the suitable solvent which dissolves entirely the glassy compounds and should not give rise to any secondary phenomena.
For this purpose, our investigations were covered all the usual acids, bases and their mixtures such as: HNO3, HCl, NaOH, KOH, CH3COOH.
The calorimetric profile shows that the 4.5% weight of phosphoric acid solution is the best solvent for the thermochemical requirements of phosphate glasses.
The dissolution of phosphate glasses were recorded by means the C80 (SETARAM) at 25°C. This equipment possesses two identical cells: the reference and the measuring cell. The reference cell should contain only the solvent but the measuring cell was provided with the solid to be dissolved or the liquid to be mixture. The superior compartment contains the attack solution (solvent) which is tightly separated from the lower one by a movable cover.
The reference and the measuring cell are surrounded by thermoelectric piles with high performance. These latters permit to detect the heat flow resulted from the dissolution, mixing or dilution process. The integration of the raw signal determined the heat dissolution of the studied compound.
Experiments were carried out by dissolving the same mass of solids (25 mg) in 4.5 ml of solvent.
The plots of heat dissolution of glasses (ΔsolH (kJ mol−1)) are shown in Figure 6.
Evolution of molar enthalpy of dissolution of (100-x)NaPO3-xZnO(0 ≤ x ≤ 33 mol%) glasses.
For (50-x/2)Na2O-xZnO-50-x/2)P2O5 glass composition, it seems that the dissolution phenomenon is endothermic for lower ZnO content and becomes exothermic when ZnO oxide is progressively incorporated in the vitreous network.
Furthermore, the change in thermal signs is probably correlated to structural modifications of metaphosphate groups (Q2) suggesting the formation of pyrophosphate units (Q1) when ZnO oxide is introduced.
These results were correlated to spectroscopic investigations which revealed the formation of pyrophosphate groups for zinc, manganese and magnesium phosphate glasses, resulting from the cleavage of the P-O-P bridges when the amount of MO oxide is progressively increases [11, 12, 13, 15, 29, 32, 33, 42].
Calorimetric study of glasses has been carried out for several decades. However, the thermochemical investigations of glassy compounds have been considered using a thermodynamic approach based on the Miedema’s model in order to evaluate the formation enthalpy of binary alloys [42].
In the case of glassy compounds, the knowledge of the formation enthalpy is an important chemical data which can be used to determine the Gibbs free energy of formation of the selected compounds and to have an idea about their stability.
The determination of the formation enthalpy of (100-x)NaPO3-xZnO glass series involves the formation enthalpy of sodium trimetaphosphate, (NaPO3)3, crystal). Because of the very old value reported in literature [42], this quantity has been determined again by the same technique.
Sodium trimetaphosphate (NaPO3)3, was synthesized by thermal decomposition of sodium dihydrogen phosphate (NaH2PO4). This later was obtained by thermal dehydration of its commercial monohydrate from NaH2PO4.H2O (Fluka of purity higher than 99%) at 150°C during 24 hours.
NaH2PO4 was placed in the furnace, then the temperature increases from 200 to 300°C during 24 hours in order to eliminate residual water and volatile gases. After any heat treatment, the powder was crushed.
Then, the temperature increases and maintains 500°C for one night. The final product was tested using an X-ray diffraction equipped by SEIFERT-XRD 3000 TT diffractometer which confirms that the final product is the sodium trimetaphosphate.
Generally, the direct determination of the formation enthalpy of any compounds is impossible. For this reason, our investigation is based on considering a particular reaction which involves the studied compounds with other reactants and products.
The knowledge of the enthalpy of the hypothetical reaction and the formation enthalpy of the reactants and products allow determining the formation enthalpy of the compound to be studied.
For the sodium trimetaphosphate ((NaPO3)3, crystal); the following thermochemical cycle has been studied [42]. This later put into consideration the chemical reaction which involves dissolution, dilution and mixing processes.
The designed states from E1 to E4 are the solutions obtained from different chemical operations [42]:
E1 state presents the phenomena of dissolution for NaH2PO4 in Slv2 (4.45%) (ΔsolH1).
E2 state designed the dissolution process of (NaPO3)3 in Slv1 (4.5%) (ΔsolH2).
E3 state designed the dilution process of 3H2O(liq) (ΔdiH1).
E4 state designed the mixing process of E2 + E3 states (ΔmixH1).
ΔrH1 can be expressed as ΔrH1 = 3 ΔsolH1 + ΔmixH2 − ΔmixH1 − ΔsolH2− ΔdilH1 in which ΔsolH are molar quantities.
So the whole results allow to derive the enthalpy of reaction (R1) at 298.15 K. Taking into account the enthalpies of formation of NaH2PO4 (s) and H2O (liq) [42], we can derive that for sodium trimetaphosphate ((NaPO3)3, (crystal)) at 298.15 K.
For zinc-based phosphate glasses, the formation enthalpy of the glass series can be determined by considering a hypothetical reaction based on ((NaPO3)3(crystal)) and ZnO (sd). Their formation enthalpies can be derived by considering the following cycle which involves dissolution and mixing processes. The designed states from E5 to E8 are the solutions resulted from the different chemical operations [42]:
E5 state presents the phenomena of dissolution for (NaPO3)3 in Slv1 (4.5%) (ΔsolH2).
E6 state designed the dissolution process of ZnO in Slv1 (4.5%) (ΔsolH3).
E7 state designed the mixing process of E5 + E6 states (ΔmixH3).
E8 state designed the dissolution process of glasses in Slv1 (4.5%) (ΔsolH4).
According to this cycle ΔrH2 can be expressed as: ΔrH2 = (100-x) ΔsolH2 + 3x ΔsolH3 + ΔmixH3 + ΔmixH4 − 3ΔsolH4 when E7 and E8 states are identical (ΔmixH4 ≈ 0) [42]. This quantity also equals:
So, the standard enthalpy of formation of the glass can be derived as:
Tables 5–7 show the dissolution heat (Qr) of increasing the moles number (n) of NaH2PO4, (NaPO3)3 and ZnO solids in their corresponding solvents [42].
NaH2PO4 (sd) in Slv2 (ΔsolH1) | ||
---|---|---|
n (mmol) | Qr (J) | uQr (J) |
0.2548 | 1.084 | 0.05 |
0.3700 | 1.328 | 0.08 |
0.3440 | 1.282 | 0.06 |
0.3000 | 1.167 | 0.11 |
0.1366 | 0.680 | 0.06 |
Enthalpy of solution of NaH2PO4 (sd) in 4.45% (w/w) H3PO4 at the temperature T = 298.15 K and pressure p = 0.1 MPa (level of confidence = 0.68).a
Standard uncertainties u are u(T) = 0.01 K, u(p) = 10 kPa.
Qr: Heat of solution.
Slv2: 4.45% (w/w) H3PO4.
(NaPO3)3(cr) in Slv1 (ΔsolH2) | ||
---|---|---|
n (mmol) | Qr (J) | uQr (J) |
0.1995 | 0.950 | 0.08 |
0.2473 | 1.171 | 0.06 |
0.2990 | 1.370 | 0.07 |
0.3452 | 1.590 | 0.12 |
0.2626 | 1.240 | 0.10 |
Heat of solution of (NaPO3)3(cr) in 4.5% (w/w) H3PO4 at the temperature T = 298.15 K and p = 0.1 MPa (level of confidence = 0.68).a
Standard uncertainties u are u(T) = 0.01 K, u(p) = 10 kPa.
Qr: Heat of solution.
Slv1: 4.5% (w/w) H3PO4.
ZnO (sd) in Slv1 (ΔsolH3) | ||
---|---|---|
n (mmol) | Qr (J) | u(Qr) (J) |
0.2736 | −26.081 | 0.12 |
0.3625 | −34.600 | 0.33 |
0.2461 | −23.500 | 0.24 |
0.2211 | −21.215 | 0.22 |
0.3060 | −29.124 | 0.50 |
0.3350 | −32.010 | 0.21 |
Enthalpy of solution of ZnO (sd) in 4.5% (w/w) H3PO4 at T = 298.15 K and p = 0.1 MPa (level of confidence = 0.68).a
Standard uncertainties u are u(T) = 0.01 K, u(p) = 10 kPa.
Qr: Heat of solution.
Slv1: 4.5% (w/w) H3PO4.
Table 5 presents the dissolution process of NaH2PO4 with the variation of the quantity (n (mmol)) to be dissolved in 4.5 ml of phosphoric acid solution (4.45% (w/w) H3PO4) (Slv2).
Table 6 presents the dissolution process of (NaPO3)3 with the variation of the quantity (n (mmol)) to be dissolved in 4.5 ml of phosphoric acid solution (4.5% (w/w) H3PO4) (Slv1).
Table 7 presents the dissolution process for ZnO with the variation of the quantity (n (mmol)) to be dissolved in 4.5 ml of phosphoric acid solution (4.5% (w/w) H3PO4) (Slv1).
The plots of the variation of the measuring heats as a function of the moles number of solid leads to straight lines whose expressed as: Qr = An+b.
The slope (A) presents the molar dissolution enthalpy (ΔsolH) and b is the intercept increment.
Referring to a mathematical treatment developed in literature, the increment b is statistically not significant which leads to derive the dissolution enthalpy as [42]:
where (wi) is the reciprocal of the variance on ΔHi (wi = 1/σ2ΔHi), and ΔHi is the energy resulting by dissolving ni (mol) of the corresponding product in the phosphoric acid solution.
Equations of the lines are as follows [42]:
for (NaH2PO4) (sd) in 4.45% weight of H3PO4 solution.
for ((NaPO3)3, (cristal)) in 4.5% weight of H3PO4 solution.
For ZnO (sd) in 4.5% weight of H3PO4 solution.
Table 8 gathers the values of molar dissolution enthalpies with the corresponding errors.
Compound | NaH2PO4 (sd) in Slv2 | (NaPO3)3 (sd) in Slv1 | ZnO (sd) in Slv1 |
---|---|---|---|
ΔsolH (kJ/mol) | ΔsolH1 = 4.01 ± 0.47 | ΔsolH2 = 4.66 ± 0.44 | ΔsolH3 = −95.5 ± 2.7 |
Molar enthalpy of solution of dissolved compounds at the temperature T = 298.15 K and pressure p = 0.1 MPa (level of confidence = 0.68).a
Standard uncertainties u are u(T) = 0.01 K, u(p) = 10 kPa.
Slv1: 4.5% (w/w) H3PO4.
Slv2: 4.45% (w/w) H3PO4.
For zinc phosphate glasses, experiments were carried out by dissolving the same mass of solids (25 mg) in 4.5 ml of 4.5% weight of H3PO4 solution [42].
Table 9 reports the variation of the heat dissolution for the glass series published in a previous work [33]. This evolution shows that the dissolution heat decreases linearly with the incorporation of ZnO oxide. As a result the inversion in the thermal signs for the studies glasses. This variation can be explained by the structural changes of the phosphate network suggesting the distortion of metaphosphate chains revealed by 31P MAS-NMR analysis.
Composition | ΔsolH (kJ/mol) | u (ΔsolH) (kJ/mol) |
---|---|---|
NaPO3 | 4.80 | 0.45 |
95 NaPO3-5 ZnO | 3.70 | 0.20 |
90 NaPO3-10 ZnO | 2.92 | 0.15 |
85 NaPO3-15 ZnO | 1.70 | 0.10 |
80 NaPO3-20 ZnO | −1.15 | 0.10 |
75 NaPO3-25 ZnO | −3.21 | 0.20 |
70 NaPO3-30 ZnO | −6.34 | 0.32 |
67 NaPO3-33 ZnO | −13.05 | 1.00 |
Evolution of heat solution of (100-x)NaPO3-xZnO phosphate glasses in 4.5% (w/w) H3PO4 at the temperature T = 298.15 K and pressure p = 0.1 MPa (level of confidence = 0.68).a
Standard uncertainties u are u(T) = 0.01 K, u(p) = 10 kPa.
Plotting of dissolution heat versus ZnO proportion (ΔsolH (kJ mol−1)) is reported in Figure 6. It seems that the calorimetric dissolution of the glass series is endothermic for lower ZnO proportion and becomes exothermic above 18 mol% of ZnO. This variation can be correlated to the cleavage of P▬O▬P bridges which suggests the appearance of pyrophosphate groups (Q1), revealed by 31P MAS-NMR spectroscopic analysis, when ZnO oxide is progressively incorporated in the vitreous network [33].
E2 was provided with various amounts of (NaPO3)3 (6–9 mg) which have been dissolved in 4.5% (w/w) H3PO4 solution but E3 is 4.4% (w/w) H3PO4 solution. Mixing the same volumes of E2 and E3 (around 2 ml) leads to a solution having the mean value of acid composition (E4 with 4.45% (w/w) H3PO4 or [H3PO4.116.90H2O]. Consequently, the concentration of Slv2 was fixed as 4.45% (w/w) H3PO4 in order to get identical E1 and E4 states. Table 10 reports E2 + E3 mixing enthalpy for different mole number (n) of (NaPO3)3 added in E2. This allowed to express ΔmixH1 as: ΔmixH1 = 0.07 n (R2 = 0.995) leading to a value of 0.07 kJ per (NaPO3)3 mole [42].
n (mmol) | Qr (J) | u(Qr) (J) |
---|---|---|
0.0187 | 0.0005 | 0 |
0.0257 | 0.0010 | 0 |
0.0157 | 0.0003 | 0 |
0.0300 | 0.0013 | 0 |
Enthalpy of mixing: ΔmixH1 (E2 + E3) at the temperature T = 298.15 K and pressure p = 0.1 MPa (level of confidence = 0.68).a
Standard uncertainties u are u(T) = 0.01 K, u(p) = 10 kPa.
E2: Solution with various amounts of (NaPO3)3 in 4.5% (w/w) H3PO4.
E3: 4.4% (w/w) H3PO4.
Qr: Heat of mixing.
(E1 + E4) mixing process, which noted as (ΔmixH2), was considered in order to check whether or not they correspond to the same final state. This operation led to an undetectable thermal effect [42].
Mixing the same volumes (around 2 ml) of E5 and E6 which have the same concentration of phosphoric acid (4.5% (w/w) H3PO4), led to E7 solution. Were previously added to while The E5 sate was obtained by dissolving the average mass of ((NaPO3)3, (cristal)) (27.3 mg) whereas the E6 state was considered by dissolving the various amounts of ZnO so the variation of energy (Table 11) is due to the presence of ZnO in the solution. The mixture of E7 and E8 states has no detectable thermal effect [42].
Composition | ΔmixH3 (E5 + E6) (kJ/ZnO mole) | u(ΔmixH3) (kJ/mol) | ΔmixH4 (E7 + E8) (kJ/mol) |
---|---|---|---|
95 NaPO3-5 ZnO | 10.15 | 0.10 | ≈0 |
90 NaPO3-10 ZnO | 4.00 | 0.13 | ≈0 |
85 NaPO3-15 ZnO | 5.10 | 0.21 | ≈0 |
80 NaPO3-20 ZnO | 3.23 | 0.24 | ≈0 |
75 NaPO3-25 ZnO | 2.74 | 0.25 | ≈0 |
70 NaPO3-30 ZnO | 3.00 | 0.15 | ≈0 |
67 NaPO3-33 ZnO | 2.12 | 0.20 | ≈0 |
Enthalpy of mixing: ΔmixH3 and ΔmixH4 at T = 298.15 K and p = 0.1 MPa (level of confidence = 0.68).a
Standard uncertainties u are u(T) = 0.01 K, u(p) = 10 kPa.
E5: solution with 27.3 mg of (NaPO3)3 in 4.5% (w/w) H3PO4.
E6: solution with various amounts of ZnO in 4.5% (w/w) H3PO4.
E8: solution with 25 mg of glass composition in 4.5% (w/w) H3PO4.
For the cycle corresponding to the sodium trimetaphosphate, addition of water to Slv1 [H3PO4.115.54 H2O] corresponds to a dilution process. The corresponding energy was calculated by linear interpolation of literature data considering the interval to which belongs each enthalpies of solution of the initial (4.5% (w/w) H3PO4) and final states (4.4% (w/w) H3PO4) [42].
The formation and dilution enthalpies were calculated from Ref. [42] and listed below:
Calculation gives ΔdilH1 = −0.017 kJ mol−1 H3PO4.
The formation enthalpy of sodium trimetaphosphate ((NaPO3)3, crystal) has been deduced as (−3762.5 ± 175) kJ mol−1. The obtained value differs from the older by only 2.4%. It seems that the calculated value of the formation enthalpy of sodium trimetaphosphate ((NaPO3)3, crystal) is in good agreement with this determined previously in 1968. This confirms that the synthesized product is probably the sodium trimetaphosphate and not a mixture.
The variation of the formation enthalpy of the glass series are mentioned in Table 12. This latter shows that this quantity increases with the addition of ZnO oxide as reported in Figure 7 [42].
Composition | ΔfH°(kJ/mol) | u(ΔfH°) (kJ/mol) |
---|---|---|
NaPO3 | −1260 | 50 |
95 NaPO3-5 ZnO | −1213 | 49 |
90 NaPO3-10 ZnO | −1174 | 47 |
85 NaPO3-15 ZnO | −1132 | 45 |
80 NaPO3-20 ZnO | −1090 | 44 |
75 NaPO3-25 ZnO | −1070 | 43 |
70 NaPO3-30 ZnO | −1003 | 40 |
67 NaPO3-33 ZnO | −973 | 39 |
Formation enthalpy of: (100-x)NaPO3-xZnO glasses at the temperature T = 298.15 K and pressure p = 0.1 MPa (level of confidence = 0.68).a
Standard uncertainties u are u(T) = 0.01 K, u(p) = 10 kPa.
Evolution of the standard enthalpy of formation at standard temperature and pressure of (100-x)NaPO3-xZnO (0 ≤ x ≤ 33 mol%) glasses [45].
The influence of ZnO, MgO, MnO and SiO2 addition on the structure, physical and optical properties of phosphate glasses and phosphate-based silicate glasses having a general formula: (50-x/2)Na2O-xMO-(50-x/2)P2O5 (M = Zn, Mn, Mg) where 3 ≤ O/P ≤ 3.49; (50-x)Na2O-xMO-50P2O5 (M = Zn, Mn) with O/P = 3 (0 ≤ x ≤ 33 mol%) and (0.9-x)NaPO3-xSiO2-0.1ZnO (0 ≤ x ≤ 0.1 mol).
Amorphous state was investigated by means of FTIR, Raman, MAS-NMR and UV-visible spectroscopy in order to study the structural role of MO oxide.
Spectroscopic analysis revealed the formation of pyrophosphate groups (Q1) resulting from the depolymerization of infinite metaphosphate groups (Q2) when the modifying oxide is gradually incorporated.
Furthermore, the indirect optical band gap energy for zinc phosphate-based silicate glasses increases with the addition of SiO2 oxide. This suggests the increase in the NBOs resulting from the modification of P▬O▬P bridges which revealed the shortening of the metaphosphate chains.
On the other hand, the dissolution process is endothermic at lower MO content and become exothermic when MO oxide is progressively incorporated. The change in thermal sign could be correlated to the structural modification inducing the formation of P▬O▬M ionic bond which increases the rigidity and the compacity of the vitreous network.
Furthermore, the glass formation enthalpy of (100-x)NaPO3-xZnO (0 ≤ x ≤ 33 mol%) glass series were determine by considering a thermochemical cycle involving the formation enthalpy of sodium trimetaphosphate ((NaPO3)3, crystal). This later was checked in this work.
The glass formation enthalpy increases when ZnO oxide is progressively incorporated in the vitreous network.
When replacing of P by Zn induced a decrease in the binding energy which suggest the increase of the formation enthalpy of the glass series.
Furthermore, the variation of Tg values reflects an increase of the rigidity of the glass network due to the formation of P▬O▬Zn ionic bonds. As a result, the increase in the stability of the phosphate network which is tightly related to the Gibbs free energy of formation ΔrG°.
Because of the large disorder that exists in the vitreous structure, the entropy factor (ΔrS°) should prevail and induce the decrease in ΔrG° value when ZnO concentration increases.
Travel and tourism has become the world’s largest and fastest growing industry, and its growth shows a consistent year to year increase [1]. The sector contributes directly to 5% of the world’s GDP, one in 12 jobs globally, and is a major export sector for many countries, both in the developing and developed world [1]. The increase in global tourism numbers (1 billion in 2012) compared to 710 million in 2000 [2] has resulted in intense competition between destinations to grow their market shares. According to [3], international tourist arrivals rose by 6% in 2018 to hit the 1.4 billion mark from 1.3 billion in 2017. The [3’s] tourism forecast which was published in 2010 suggested that the 1.4 billion arrivals would be attained in 2020, yet the rapid tourism growth on the international scale has seen that target being attained two years ahead of time [4]. This growth has seen tourist destinations wrestle fiercely not only for the tourist’s expenditure but also for their voice and mind.
\nThe intangibility of tourism products means that their image is the only way which potential tourists have of comparing destinations and choosing between them and therefore it is important to create and transmit favorable images to potential tourists in target markets [4]. As tourism services are intangible, images become more important than reality [5]. This makes the tourist’s perceptions of the product not only a fundamental component of the decision-making process, but also a key determinant of the performance of the tourist destination. The idea of DI was introduced into tourism studies in the early 1970s by [5, 6, 7] and Pike [4], and has since become one of the most researched topics in tourism-related research [8] due to its association with tourism performance [9]. However, there is a less marked mention of DI recovery and performance in literature, that is, the DI recovery-performance correlation has been marginalized. Some studies have focused on DI and tourist loyalty [10, 11]. Others have examined DI and technology (film, Internet and others) [12, 13]. There is therefore an interstice in research on the DI recovery and performance nexus.
\nGlobally, France, Switzerland, Austria, Germany, the United States, Spain and China continue to top the rankings in terms of both international arrivals and receipts in 2018 [14]. The sound performance of these tourist destinations tended to suggest a strong DI. The European continent continued to lead in terms of arrivals in 2017 (713 million, plus six percent) [15]. Africa’s weak image was generally attributed to political upheavals, disease, a poor infrastructure, poverty, and frequent droughts [16]. These factors negatively impacted the economies of the African destinations and specifically, the tourism economies. [16] identified Africa’s ‘unfortunate’ image as an obstacle to the region’s competitiveness in the global tourism market, ascertaining that there is overwhelming evidence to suggest that Africa faces a huge challenge in counteracting the continent’s prolonged negative image and perceived risks as a tourist destination. This was part of the reason why in 2018 Africa attracted only five percent of the international overnight visitors, accounting for 67 million international tourists [3] against a global total of 1.403 billion international tourists. Tourist arrivals in sub-Saharan Africa grew by 6% with the island destinations, namely Cabo Verde, Reunion and Mauritius registering strong growth [3]. What seems to be emerging from the above discussion is that many African destinations, including Zimbabwe, are faced with a challenge of a weak DI and an equally weak performance of the tourism sector. There is a dearth of research on destination performance [17]. Scholars who have explored tourism performance include [17, 18, 19, 20, 21, 22, 23]. However, these researchers did not explore DI and the performance of the tourism sector jointly. This study sought to fill this interstice.
\nThe [14] has made efforts to react to the tarnished image through various promotions focusing on rebranding as an exceptional ingredient in order to give the country’s tourism a facelift [24]. This has seen Zimbabwe as a tourist destination rebranding three times between 1980 and 2011 [24]. However, it appears that the negative image has remained in place, well after the hosting of the highly touted UNWTO General Assembly. Zimbabwe’s tourism arrivals and receipts indicate that the sector has been on an unstable path in the last decades, with fluctuating performances in tandem with the deteriorating local economic conditions and the global economic crisis in 2008 exacerbated by the global economic crisis/credit crisis which affected mostly developed world tourism markets and led to many traditional tourists cutting back on their travel and leisure expenditure [26]. In Zimbabwe, tourism is one of the four pillars anchoring economic growth after Agriculture, Mining and Manufacturing [25].
\nZimbabwe is grappling with a negative tourist DI and a decline in the performance of the tourism sector. Despite several studies, for example [23, 27, 28, 29] which have been carried out to improve the performance of the country’s tourism sector, image and performance remain problematic. Zimbabwe’s travel and tourism competitiveness index (ranking) has not been impressive. In 2015, Zimbabwe was ranked 115 out of 141 tourist destinations across the world and an equally low 114 out of 136 destination in 2017 [30]. In terms of prioritization of travel and tourism, Zimbabwe was at 105 out of 136 while it scored a very low 134 out of 136 destinations for its business environment in 2017 [30]. In terms of international arrivals, the 2011 figure of 2423 20 was marginally better than the 2017 figure of 2,422,930 [14]. This suggested a lack of tangible growth in terms of arrivals. The country’s image in the source markets is still associated with political instability, policy inconsistency, and disease outbreaks [30]. There is a strong market perception that the destination is not price competitive and that the overall product is tired [14]. In [26], it is noted that [14] has been promoting tourism through beauty pageants, carnivals and sporting events such as soccer tournaments. The ZTA website has also served as a promotional tool [14]. However, as indicated by [25], Zimbabwe is still failing to gain its previous position as a destination of choice. Furthermore, the goal of a middle income economy for Zimbabwe by 2030 may remain a pipe dream unless there is an improvement in the economic, social and political environments [25]. Although Zimbabwe’s tourist figures have increased here and there since 2008, as a destination, it is still struggling to restore itself to its former glory as a competitive force in southern Africa. Zimbabwe’s negative perception hinders its visibility in the international markets as a tourist destination which in turn is reflected in weak demand among international tour operators and travel agencies [4]. Negative perceptions of tourist destinations lead to the poor performance of the industry [31]. The highest number of tourists the country has received (2579974) in 2018 almost equals that of 2007 (2505988), that is, twelve years ago. Unless this problem of a weak image is resolved, Zimbabwe’s negative perception in the source markets will remain, and the performance of the tourism sector will remain depressed resulting in a low tourism multiplier effect. The study will benefit tourism and hospitality stakeholders such as tourists, the Zimbabwe Tourism Authority, tourism and hospitality researchers, planners, policy formulators, tourism and hospitality business operators and local communities.
\nThe major objective of the study was to develop a destination image recovery model to enhance tourism performance in Zimbabwe. The specific objectives of the study were to assess the current situation with regards to destination image and performance of the Tourism sector in Zimbabwe, examine the determinants of destination image and performance of the tourism sector in Zimbabwe, investigate the extent to which destination image affects performance of the tourism sector in Zimbabwe and develop a destination image recovery model for enhancing performance of the Tourism sector in Zimbabwe.
\nH1: Price is significantly positively related to affective image.
\nH2: There is a significant and positive relationship between amenities and affective image.
\nH3: Ancillary services have a significant relationship with affective image.
\nH4: Accessibility has a significant positive influence on affective image.
\nH5: Price significantly influences performance.
\nH6: Amenities significantly influence performance.
\nH7: Ancillary services significantly influence performance.
\nH8: Accessibility significantly influences performance.
\nLiterature review entailed describing, comparing, contrasting and evaluating the major theories, arguments, themes, approaches and controversies in the scholarly literature on DI and performance of the tourism sector. Literature review was also conducted to identify gaps in literature with regards to DI and performance of the tourism sector. This was done in order to fill such gap or gaps with new knowledge thereby contributing towards extending the frontiers of knowledge in terms of DI recovery and tourism performance.
\nThe study on which this chapter is based was premised on the stakeholder theory [32] and the [33] tourism performance model. Its tenets are that organizations depend on a wide range of audiences or groups of stakeholders in order to realize their objectives [34]. Modern life and tourism in particular is affected by a wide range of variables which include technology, social dimensions, political developments, environmental factors and others. The stakeholder groups cited in this theory include clients, end users (the other theories above do not make this distinction), employees, suppliers, pressure groups, local communities and the media and each stakeholder makes a decisive role in the organization’s future. This theory is currently popularly used in tourism development and in destination image recovery and in the enhancement of tourism performance.
\nAccording to [33], there are eight drivers of tourism performance which are indicated here in their order of importance: (1) tourism and related infrastructure; (2) economic conditions; (3) security, safety, and health; (4) tourism price competitiveness; (5) government policies; (6) environmental sustainability; (7) labor skills and training; and (8) natural and cultural resources. This theory links quite well with destination image recovery in that it focuses on attributes which are central to destination image recovery. The [30] noted that these destination attributes are important in generating a destination’s appeal. However, the limitations of this theory are that it assumes that any factor outside these eight may not as critical as those included on this list.
\nMost frequently, the concept of DI has been operationalized as consisting of two components: A perceptual-cognitive component that captures knowledge and beliefs about a destination’s attributes and an affective component that describes feelings towards a destination [35]. The cognitive component of the image refers to a person’s beliefs and knowledge about a destination and its attributes, which together help to form an internally accepted mental picture of the place [36]. It also includes a set of attributes that mainly correspond to the resources of a tourist destination [37, 38]. Those resource attributes generally involve the natural environment (scenic beauty, weather, beaches); Amenities (hotels, restaurants, service quality, shops); Attractions (water sports, well - known attractions, a variety of tourist activities); Accessibility (convenient transportation, developed infrastructure, ease of access, Social Environment (personal safety - security, friendly local people, good value for money, a clean environment) [37]. All these can induce an individual to visit a specific destination. The affective component refers to the evaluation stage, concerning the feelings that the individual associates with the place of visit [38]. The affective component generally covers a number of categories: distressing -relaxing, unpleasant-pleasant, boring-exciting, sleepy-lively [39]. The destination should conjure the right emotions in the potential visitor for it to earn a visit [40]. The Conative component (behavioral intention) of DI has been considered by several researchers in DI formation [31, 41, 42]. For these researchers conation is part of the image formation process which is “analogous to behavior evolving from cognitive and affective images” [43] denoting the “intent or action component” [44]. Understanding tourists’ intention or the likelihood of visiting a destination is crucial for destination marketing managers.
\nDestination image comprises functional characteristics, psychological characteristics, common and unique dimensions [45]. Common psychological attributes refer to the friendliness of the locals or beauty of the landscape, whereas unique psychological factors include feelings associated with places of religious pilgrimage or some historic event. [46] indicates that functional characteristics can be easily measured while psychological characteristics, on the contrary, cannot be easily measured. However, together they influence the formation of DI explaining why the use of mixed methods in DI studies has gained prominence [46, 47, 48].
\nThe determinants of DI include natural resources; general infrastructure; tourist infrastructure; tourist and leisure recreation; culture, history and art; political and economic factors; the social environment and the atmosphere of the place [49]. However, this view tends to marginalize the role of the tourist’s reasoned and emotional interpretation in DI formation. Most studies [50, 51] tend to consider image to be a concept formed by the consumer’s reasoned and emotional interpretation as the consequence of two closely inter-related concepts: perceptive/cognitive evaluations referring to an individual’s own knowledge and beliefs about the object, and affective appraisals related to the individual’s feelings towards the object. The combination of these two factors produces an overall, or compound, image related to the positive or negative evaluation of the product or brand [52].
\nThe demand side of determinants of DI and performance relates to issues which pertain to tourists’ socio-demographic factors [53], tourists’ nationality [54, 52] tourists’ level of awareness or familiarity with a particular destination [55]. In [56], internal factors influencing the image construct include socio-demographic factors. Specifically, the social and cultural environment relate to socio-demographic aspects of a human being [57]. It is postulated that today’s tourists play a leading role in image projection [58]. They have become an active agent who use Web 2.0 tools to disclose their opinion, experiences and feelings about the destination visited [59]. Figure 1 depicts factors which influence DI recovery.
\nFactors influencing DI recovery. Source: Adapted from Harahsheh (2009, p. 78).
According to [60], the natural resources are the main attraction of the tourism destination. Thus, they influence destination perception and performance [33]. Scenery for example, constitutes one of the dimensions used by researchers to measure DI [61]. The natural environment is one of the three dimensions of DI and performance [33]. The first dimension comprises the socio-cultural amenities such as wonderful cultural traditions, interesting local arts and interesting cultural diversity. Second, natural amenities such as: beautiful mountains, outstanding natural wonders, wonderful sightseeing opportunities, and appealing opportunities for exploring wilderness and nature. And third, climate attributes interrelation: appealing winter climate, appealing summer climate. Nature tends to feature prominently in the classifications of the destination-image management dimensions by different scholars. In [62], for example, came up with nine dimensions/attributes that determine the perceived DI of an individual. These include natural resources such as weather and its variations, beaches and their variations, wealth of countryside such as protected nature reserves and variety and uniqueness of flora and fona.
\nLiterature does not directly point out the direct effect which DI has on performance of the tourism sector. It spells out the effect of DI on value, satisfaction and loyalty of the tourists [63, 64] and not that of DI and destination performance. However, given that a direct relationship between DI, satisfaction and revisit intentions [65], it follows that there is a relationship between DI and destination performance but it appears that it is more of a derived effect than a direct one. In [64], it is found out that the tourism image is a direct antecedent of perceived quality, satisfaction, intention to return, and willingness to recommend the destination. In [53], it is conducted a study on DI, perceived value, tourist satisfaction and loyalty focusing on Mauritius. They reported that their results supported the proposed destination loyalty model, which advocated that DI directly influenced attribute satisfaction; DI and attribute satisfaction were both direct antecedents of overall satisfaction; and overall satisfaction and attribute satisfaction in turn had direct and positive impact on destination loyalty. The implication of these relationships seems to that it is important to develop positive images of a tourist destination in order to increase the number of tourists and tourist receipts [53].
\nIn [16], it is proposed two broad categories of DI recovery: the cosmetic and strategic approaches both of which emphasize the role of the media. Media strategies in the cosmetic approach try to change the destination’s image without really changing the reality behind it; the destination’s problems are not solved or managed but the local decision-makers try to potray it in a positive light, by using advertising or public relations campaigns [66]. Strategies within this category include ignoring the image crisis problem, disassociation from the problematic location, association with prestige locations, acknowledging a negative DI, delivering a counter-message to the negative stereotype, spinning the negative characteristic to positive and ridiculing the stereotype [16]. However, these strategies are associated with a low level of change in the destination’s characteristics including its performance while those which use the strategic approach tend to be associated with a high level of change (Figure 2).
\nThe strategic vs. cosmetic approach for altering prolonged negative images. Source: Avraham and Ketter (2013).
Destinations host major events to attract visitors, gain positive attention from the media and improve their image [16]. Zimbabwe has used special events which include hallmark and mega events to improve DI and ultimately destination performance. Special events describe specific rituals, presentations or anniversaries specifically planned or designed to mark a specific occasion, cultural or organizational goals [67]. Special events can include national days and celebrations, important civic occasions, unique cultural performances, major sporting fixtures, corporate functions, trade promotions and product launches [68, 69]. However, it appears that very little has been achieved by way of improving image and performance of Zimbabwe as a tourist destination.
\nThe conceptual framework depicts the study hypotheses, that the components of the cognitive image, in this case price, amenities, accessibility and ancillary services impact both affective image and destination performance (Figure 3). Affective image which derives from the potential tourists’ feelings towards the destination contributes to the improvement in overall destination image especially after visiting the destination [70]. This will ultimately result in; enhancing the performance of the tourism sector (destination performance) as the tourists spend money in the destination.
\nConceptual framework. Source: Author’s compilation (2018).
This study adopted the pragmatic research philosophy. This philosophy is a position that contends that the research question is the most important determinant of the research philosophy adopted for the study [71]. A mixed research methodology was used. Literature shows that the use of qualitative and quantitative research methodologies separately would not yield the best results for this study. Mixing quantitative and qualitative approaches is increasingly popular in DI research, although it appears that there is insufficient theoretical rationale for doing so [72]. However, [73] highlighted that the mixed methodology adds value in terms of increasing confidence in the research findings.
\nThe research design was more of quantitative than qualitative. The large amounts of quantitative data came from the tourists who far outnumbered the service providers and key informants who together provided mostly qualitative data. Research designs can be classified into three broad categories, namely quantitative, qualitative and mixed methods research designs [74]. Creswell [74] describes this design as concurrent procedures, in which the researcher converges quantitative and qualitative data in order to provide a comprehensive analysis of the research problem. This view is corroborated by [75] who highlighted that the purpose of doing this is to best understand or develop a more complete understanding of the research problem by obtaining different but complementary data. Both forms of data are collected simultaneously and the information is integrated in the interpretation of the overall results. Data analysis is kept independent and there is need to look for convergence, divergence, contradictions, or relationships of the two sources of data [76]. The convergent parallel mixed methods design supported the research requirements. It was the most appropriate research design in that allows for the collection and analysis of both qualitative and quantitative data separately [76]. This was consistent with what the research sought, that is, to collect qualitative data from service providers in the tourism sector and quantitative data from tourists. These two groups were mutually exclusive thus facilitating the independent collection and analysis of data. This would be followed by a comparison of the results to see if the results confirmed or disconfirmed each other. The result would be used to develop a DI recovery model which enhances and improves tourism performance in Zimbabwe.
\nA structured questionnaire was used to collect data from international tourists and a semi-structured one was applied on the service providers. For both tourists and service providers, the closed questions included a Five-point Likert scale: (For tourists) 5-Very Good, 4-Good, 3-Unsure, 2-Poor and 1-Very Poor and another one, 5-Very Important, 4-Important, 3-Unsure, 2-Somewhat Important and 1-Not Important. For service providers: 5-Strongly Agree, 4-Agree, 3-Neutral, 2-Disagree and 1-Strongly Disagree. Semi-structured interviews were conducted on key informants. Semi-structured interview is a term that typically refers to a context in which the interviewer has a series of questions that are in the general form of an interview guide but is able to vary the sequence of the questions [77]. This type of interview is used to find out what is happening, it seeks new insights, identifies general patterns and helps to understand the relationship between variables [78]. It uses a combination of open and close-ended questions and hence it is consistent with the mixed methodology research design. Data was collected from 240 international tourists, 62 service providers and 17 key informants. Figure 4 shows how the study was conducted. Price, amenities, accessibility and ancillary services are some of the determinants of cognitive image.
\nResearch design adopted in the study. Source: Author’s compilation (2018).
Descriptive analysis was applied on demographic data and on interval-scaled (Likert scale) data. Frequency table analysis and proportion percentage analysis was used to transform raw data into a form that would facilitate easy understanding and interpretation. Descriptive statistics were thus used to analyze and profile the perceptions (attitudes) and future intentions of the sampled international tourists. Quantitative data which was obtained from the tourists and some from service providers was analyzed using the Statistical Package for Social Sciences (SPSS) and AMOS version 25. The study used inferential statistics in order to analyze the multiple independent variables. Structural Equation Modeling (SEM) was used to analyze the multiple independent variables which included accessibility, amenities ancillary services and prices as well as dependent variables such as affective image and performance. Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and Bartlett’s test were used to test data for validity. Cronbach’s Alpha was used to test for data reliability. Overall, the Alpha value was 0.7 and above. SEM is one of the most often used statistical techniques used by researchers to test complex models which involve a number of dependent and independent variables [79]. Similar studies have used SEM [35, 46, 53]. Multivariate analysis was used to test hypotheses because it is optimal for analyzing multiple relationships [75]. Factor analysis was applied on the thirty eight destination image attributes which tourists rated on a Likert scale. These destination attributes were classified into constructs which included price, accessibility, amenities, ancillary services, affective image and performance. This was done in order to facilitate data analysis. Quantitative data was presented using tables and graphs.
\nDocumentary analysis was adopted to establish the trends which were emerging from international aircraft and passenger movements provided by CAAZ from 2016 and 2018 and the international arrivals provided by ZTA during the same period. Data was first captured on a template before it was cleaned (edited). Qualitative data from key informants and service providers was analyzed using NVivo version 12, thematic coding. The goal was to identify, analyze and describe patterns, or themes, across a data set [77]. Word cloud, Word tree, Hierarchical charts, Word popularity (Word frequencies) including Word query were used to analyze data. Thematic analysis was used because it allows for the classification into themes of various and divergent views from the respondents and it is a flexible research technique which is not tied to a specific philosophical orientation [77]. This approach was therefore quite consistent with the pragmatism research philosophy which informed this study. Results which emerged from the qualitative analysis were compared and contrasted with those which were obtained from quantitative analysis.
\nRespecting the respondent’s rights, needs, values and desires is emphasized when collecting research data (Creswell, 2014). Research which includes human input should ensure that they are well informed and consent sought from relevant authorities. Permission was sought from international tourists, service providers and key informants to carry out research. Various organizations which included the ZTA, CAAZ, Zimbabwe Parks and Wildlife Management Authority, the Ministry of Tourism and the Ministry of Tourism and Hospitality Industry issued letters to the researcher granting him permission to conduct the research at Robert Gabriel Mugabe International Airport. Research assistants were trained to ensure that they behaved ethically as they went about administering research instruments.
\nResponse rate refers to the total number of responses divided by the total number in the sample after ineligible respondents have been excluded [78]. A total of 397 respondents comprising 293 international tourists, 90 service providers and 17 key informants was targeted. However, the actual tally was 319 giving a response rate of 80% which was quite commendable [78]. This total of respondents consisted of 240 international tourists, 62 tourism and hospitality service providers and 17 key informants (Table 1).
\nNarration | \nTargeted respondents | \nActual respondents | \nResponse percentage | \nrate | \n
---|---|---|---|---|
International tourists | \n293 | \n240 | \n82 | \n\n |
Service providers | \n90 | \n62 | \n69 | \n\n |
Key informants | \n17 | \n17 | \n100 | \n\n |
Total | \n400 | \n319 | \n80 | \n\n |
Response rate.
Source: Field Survey (2018).
In a study sample of 319, fifty three percent were males while forty-seven were females. The slight dominance of males could be due to the fact that men traveled more for tourism than their female counterparts and feel more motivated to meet their need for sport and adventure experiences than females [81]. The [81] further noted that there were more men than women in the business world and a lot of business travel occurs across the world and as a result, men tended to travel more than females.
\nThe variation suggests that gender can influence perceptions of destination’s appeal. This is in line with [82] who asserted that females tended to engage in long-haul travel more than their male counterparts. Respondents aged between 25 and 35 years old formed the largest group (25.2%) followed by those aged between 35 and 44 years old (18.1), 17.2% of the respondents were in age group 45–54 years old, 15.1% of the respondents were in age group 55–65 years old, age group 66 or older constituted 13.4% of respondents, and age group 18-24 years old were 10.9%. The results showed that most of the tourists ranged from young to middle aged. The study findings resonate with those by [83]. A Visitor Exit Survey which was conducted at Zimbabwe’s ports of entry by [83] revealed that the majority of visitors to Zimbabwe were young (35–39) years (16.4%) and middle-aged (40–49) years (13.9%). A study which was conducted in Egypt by [84] focusing on cultural dimensions, demographics, and information sources as antecedents to cognitive and affective DI found out that tourists in the age ranges 26–35 and 36–50 were more likely to use the Internet, while younger (aged 18–25) and older (51–65) were less likely to use it. This finding in terms of age was similar to that of tourists in that most of the international tourists were fairly young. This may create a scenario whereby the young tourists are served by young service providers. This can help to create telepathy and rapport between the tourist and the server. This may enhance both employee and customer satisfaction leading to improved firm and destination performance. These results show that most of the respondents were well educated indicating that their responses were given from a position of enlightenment and knowledge.
\nMost of the tourists (37.5%) received an income of US$50000 and more before tax per annum followed by those who were earning between US$10001 and US$20000 (17.9%), and those who earned between US$20001 and US$30000 (11.9%), those who earned between US$30001 and US$40000 (10%) and those who earned US$ 40,001 to US$50000 (10%). There is limited research which has directly examined the relationship between destination attractiveness and income of the tourists. In [85], it is noted that in a study conducted in Taiwan, it was found that income was an influencer of tourist behavior. Tourists with a higher income tended to travel internationally more and were likely to stay in luxury hotels. On the other hand, travelers with less income tended to be associated with domestic trips rather than international vacations. In that regard, income was found to be an important determinant of destination choice [85].
\nReliability analysis is used to determine the extent of internal consistency that is represented by a set of items in a construct [80]. For this study, reliability analysis was used to determine the extent to which the items within each and every construct were consistent. According to [86], the optimal minimum alpha statistic is 0.7. However, other scholars such as [87] argue that even alpha statistics of 0.6 are still reliable. The reliability tests for each and every construct will be presented.
\nThe Cronbach’s Alpha statistic was 0.900, and being greater than 0.7, it follows that the construct price was internally consistent and reliable (Table 2). Further, assessing the corrected item-total correlation, none of the items had a coefficient less than 0.3 as recommended by [88] and this means that all the items extracted using PCA were reliable. For affective image, the Cronbach’s alpha statistic was 0.881. This was greater than the threshold of 0.7, and thus validates that affective image was internally consistent. On the other hand, none of the items had a corrected item-total correlation that was less than 0.3. Effectively, this meant that all the items were internally consistent. The Cronbach’s alpha for amenities was computed to be 0.842 and this was greater than 0.7. These results validate that the construct amenities were reliable. Regarding the corrected item to total correlation, the minimum observed was 0.533. This again, does fall below the 0.3 threshold set by scholars. In this regard, the researcher confirmed that amenities as a construct was reliable. With respect to ancillary services, the construct was internally consistent since the alpha statistic was 0.759, which is greater than the minimum expected 0.7. With respect to the corrected item-total correlation, the minimum was 0.431 and being greater than 0.3, none of the items were to be dropped.
\n\n | \n | Scale mean if item deleted | \nScale variance if item deleted | \nCorrected item-total correlation | \nCronbach’s alpha if item deleted | \n||
---|---|---|---|---|---|---|---|
Price | \n0.900 | \n\n | \n | \n | \n | ||
Lodging Prices | \n\n | 12.42 | \n6.011 | \n0.582 | \n0.938 | \n||
Prices of Restaurant Food | \n\n | 12.53 | \n4.969 | \n0.892 | \n0.826 | \n||
Prices of Restaurant Beverages | \n\n | 12.51 | \n5.004 | \n0.883 | \n0.830 | \n||
Prices of Goods and Services | \n\n | 12.41 | \n5.542 | \n0.767 | \n0.874 | \n||
Affective Image | \n0.881 | \n\n | \n | \n | \n | ||
Destination’s capacity to Relieve Stress | \n\n | 16.22 | \n10.162 | \n0.729 | \n0.852 | \n||
Destination’s Capacity to Provide Relaxation | \n\n | 16.16 | \n10.223 | \n0.793 | \n0.838 | \n||
Destination as a Pleasant Place | \n\n | 16.04 | \n11.278 | \n0.682 | \n0.865 | \n||
Destination as an Arousing Place | \n\n | 16.46 | \n9.779 | \n0.696 | \n0.862 | \n||
Destination as a Provider of Excitement | \n\n | 16.41 | \n9.985 | \n0.703 | \n0.859 | \n||
Amenities | \n0.842 | \n\n | \n | \n | \n | ||
Conference Facilities | \n\n | 11.08 | \n7.261 | \n0.726 | \n0.776 | \n||
Facilities for Young Children | \n\n | 10.88 | \n7.433 | \n0.729 | \n0.775 | \n||
Facilities for People living with Disabilities | \n\n | 10.65 | \n7.841 | \n0.724 | \n0.779 | \n||
Shopping Facilities | \n\n | 10.67 | \n8.766 | \n0.533 | \n0.857 | \n||
Ancillary Services | \n0.759 | \n\n | \n | \n | \n | ||
Cleanliness | \n\n | 16.80 | \n6.030 | \n0.569 | \n0.702 | \n||
Tourist Information | \n\n | 16.97 | \n5.629 | \n0.602 | \n0.688 | \n||
Quietness | \n\n | 17.15 | \n5.580 | \n0.548 | \n0.709 | \n||
Friendliness of Local People | \n\n | 16.72 | \n6.631 | \n0.514 | \n0.725 | \n||
ICT Readiness | \n\n | 17.14 | \n6.100 | \n0.431 | \n0.753 | \n||
Accessibility | \n0.801 | \n\n | \n | \n | \n | \n | |
Zimbabwe’s Accessibility as a Destination | \n\n | 12.17 | \n5.384 | \n0.524 | \n0.793 | \n||
Infrastructure at the entry point | \n\n | 12.29 | \n4.295 | \n0.736 | \n0.685 | \n||
Service at Immigration | \n\n | 12.20 | \n4.679 | \n0.685 | \n0.714 | \n||
Accessibility Destinations | \n\n | 12.00 | \n5.926 | \n0.533 | \n0.789 | \n||
Value | \n0.854 | \n\n | \n | \n | \n | ||
Value as a Vacation Destination | \n\n | 7.24 | \n2.248 | \n0.633 | \n0.612 | \n||
Value as a Business Destination | \n\n | 7.44 | \n2.329 | \n0.553 | \n0.711 | \n||
Overall Quality of the Destination | \n\n | 6.97 | \n2.615 | \n0.571 | \n0.688 | \n||
Attractions | \n0.636 | \n\n | \n | \n | \n | ||
Natural Landscape | \n\n | 4.32 | \n0.591 | \n0.472 | \n. | \n||
Climate | \n\n | 4.48 | \n0.433 | \n0.472 | \n. | \n
Reliability analysis.
From the results above, the Cronbach’s alpha for accessibility was 0.801 and being greater than 0.7, it follows, therefore, that the construct was internally consistent and reliable. With respect to the corrected item-total correlation, the lowest observed was 0.524 and because this was greater than the minimum 0.3, the researcher confirms that all the items making up the construct.
\naccessibility were reliable. The next construct that was tested was value/performance. The corresponding Cronbach’s alpha for value/performance was 0.754 and being greater than 0.7, we can confirm that the construct was reliable and internally consistent. With respect to the corrected item-total correlation coefficient, the lowest observed was 0.553 and being greater than 0.3, it followed that all the items were very reliable. The construct attractions had a Cronbach alpha statistic of 0.636, this was less than the expected minimum of 0.7 and effectively, this meant that the construct was not so reliable. This is, however, despite that the corrected item-total correlation coefficients were greater than 0.3. Overall, from the reliability analysis, it was confirmed that the reliable constructs were: Price, Affective Image, Amenities, Ancillary Services, Accessibility and Value/Performance.
\nIn order to ensure that the conditions for the use of factor analysis were satisfied, [89], argue that the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and Bartlett’s tests ought to be tested. With respect to the KMO test, which measures the adequacy of the sample, the lower expected threshold should be 0.5, with higher values being more desirable [90]. With respect to the Bartlett’s test, which is a measure of multivariate normality, the p-value ought to be significant at p < 0.05 [88]. These tests were computed and the results are summarized in Table 3.
\nKaiser-Meyer-Olkin measure of sampling adequacy | \n.843 | \n
Bartlett’s test of sphericity Approx. Chi-Square | \n4170.258 | \n
Df | \n703 | \n
Sig. | \n.000 | \n
KMO and Bartlett’s test.
Source: Data Survey.
The results above show that the KMO statistic was 0.843, and being greater than the minimum 0.5, it follows that the sample adequacy condition was satisfied. On the other hand, the Bartlett’s test was significant at p < 0.01 and this confirms the assumption of multivariate normality was met. The results above show that the KMO statistic was 0.843, and being greater than the minimum 0.5, it follows that the sample adequacy condition was satisfied. On the other hand, the Bartlett’s test was significant at p < 0.01 and this confirms the assumption of multivariate normality was met.
\nThe modeling process below looked at price, amenities, conducive environment, affective image, accessibility and performance. The research instrument comprised of 38 items that measured the determinants of destination image recovery and performance of the tourism sector in Zimbabwe. With a view to establishing the principal factors behind these determinants of destination image recovery and destination performance, [91] recommend the use of exploratory factor analysis (EFA) dimension reduction techniques. According to [92], these dimension reduction techniques help in the classification of items that share a common underlying structure into a set of similar items collectively known as components [88]. One of the major dimension reduction methods recommended by scholars is factor analysis and this was considered in this study to be the optimal dimensionality reduction technique as prescribed by [93]. To achieve this dimensionality reduction, the principal component analysis (PCA) was used as the factor analysis component extraction method.
\nBecause the normality assumption was met, the Principal Component Analysis (PCA) was used in this study as the component extraction method, instead of the principal axis factoring, which works best when the normality assumption is not met [92]. With a view to simplifying the factors extracted, rotation was used. The components were assumed to be uncorrelated and o this effect, orthogonal rotation was done instead of oblimin rotation [90]. For the orthogonal rotation, Varimax was selected and this was done with Kaiser Normalization as prescribed by [89].
\nHaving run PCA, the communalities that emerged are presented in Table 4. Generally, the communalities inform us on the extent of correlation between one item and the rest of the other items [88]. The higher the common variance, the higher is the validity of the item, and [80] recommend communalities to be at least 0.5 in magnitude.
\n\n | Initial | \nExtraction | \n
---|---|---|
Zimbabwe’s Accessibility as a Destination | \n1.000 | \n.606 | \n
Infrastructure at the Country’s Immigration (entry point used) | \n1.000 | \n.809 | \n
Service at Immigration (entry point used) | \n1.000 | \n.717 | \n
Accessibility of Tourist Destinations within Zimbabwe | \n1.000 | \n.697 | \n
Road Condition | \n1.000 | \n.600 | \n
Inland Transportation/Taxi/Bus | \n1.000 | \n.526 | \n
Natural Landscape | \n1.000 | \n.730 | \n
Climate | \n1.000 | \n.506 | \n
Tourist Attractions | \n1.000 | \n.653 | \n
Opportunities for Learning Ethnic Customs | \n1.000 | \n.751 | \n
Local Cuisine | \n1.000 | \n.600 | \n
Outdoor Activities | \n1.000 | \n.520 | \n
Cleanliness | \n1.000 | \n.639 | \n
Tourist Information | \n1.000 | \n.665 | \n
Quietness (Noise Pollution) | \n1.000 | \n.565 | \n
Friendliness of Local People | \n1.000 | \n.649 | \n
Nightlife/Entertainment | \n1.000 | \n.644 | \n
Attitude of Service Personnel | \n1.000 | \n.685 | \n
Safety and Security | \n1.000 | \n.476 | \n
ICT Readiness | \n1.000 | \n.624 | \n
Conference Facilities | \n1.000 | \n.660 | \n
Facilities for Young Children | \n1.000 | \n.800 | \n
Facilities for People living with Disabilities | \n1.000 | \n.795 | \n
Shopping Facilities | \n1.000 | \n.617 | \n
Lodging Facilities | \n1.000 | \n.726 | \n
Restaurants | \n1.000 | \n.721 | \n
Lodging Prices | \n1.000 | \n.757 | \n
Prices of Restaurant Food | \n1.000 | \n.864 | \n
Prices of Restaurant Beverages | \n1.000 | \n.863 | \n
Prices of Goods and Services | \n1.000 | \n.735 | \n
Destination’s capacity to Relieve Stress | \n1.000 | \n.753 | \n
Destination’s Capacity to Provide Relaxation | \n1.000 | \n.798 | \n
Destination as a Pleasant Place | \n1.000 | \n.724 | \n
Destination as an Arousing Place | \n1.000 | \n.712 | \n
Destination as a Provider of Excitement | \n1.000 | \n.734 | \n
Value as a Vacation Destination | \n1.000 | \n.751 | \n
Value as a Business Destination | \n1.000 | \n.689 | \n
Overall Quality of the Destination | \n1.000 | \n.727 | \n
Extraction Method: Principal Component Analysis. | \n
Communalities-destination image recovery and performance.
Source: Data Survey (2018).
From the results, only one item had a communality that was less than 0.5 and this was safety and security and the respective correlation coefficient was 0.476. Effectively, this was discarded off from the results. The rest of the other coefficients were considered to be significant for accurate factor extraction, with the highest communalities being 0.864 and 0.863 for prices of restaurant food and prices of restaurant beverages respectively. The resultant model is presented in Figure 5 below.
\nStructural equation model. Source: Data survey (2018).
The corresponding table with the detailed results is presented in Table 5 below.
\n\n | \n | \n | Estimate | \nS.E. | \nC.R. | \nP | \nStandardized | \n
---|---|---|---|---|---|---|---|
AF | \n<−-- | \nPR | \n.237 | \n.051 | \n4.681 | \n.000 | \n.320 | \n
VA | \n<−-- | \nPR | \n−.114 | \n.065 | \n−1.750 | \n.080 | \n−.128 | \n
AF | \n<−-- | \nAM | \n.089 | \n.044 | \n1.995 | \n.046 | \n.135 | \n
VA | \n<−-- | \nAM | \n.072 | \n.062 | \n1.173 | \n.241 | \n.091 | \n
AF | \n<−-- | \nAC | \n.025 | \n.046 | \n.543 | \n.587 | \n.036 | \n
VA | \n<−-- | \nAC | \n−.070 | \n.066 | \n−1.071 | \n.284 | \n−.083 | \n
AF | \n<−-- | \nAN | \n.586 | \n.146 | \n4.003 | \n.000 | \n.345 | \n
VA | \n<−-- | \nAN | \n.356 | \n.172 | \n2.066 | \n.039 | \n.175 | \n
Structural equation model - regression weights.
Source: Data Survey (2018).
From the results above, the strongest relationship was found to exist between ancillary and affective image, whose standardized coefficient was 0.345 and this was seconded by price and affective image, with a standardized coefficient of 0.320. The p-value was less than 0.05 for the relationship between Price and Affective Image (p < 0.01), amenities and affective image (p < 0.05), ancillary services and affective image (p < 0.01) as well as ancillary and value. It should be noted that only one of the four hypotheses linking performance was significant. The conclusions to the research hypotheses are indicated below:
\nWith respect to the dependent variable, affective image, the key hypothesis decisions are summarized below:
\nH1: Price is significantly positively related to affective image.
\n\nSIGNIFICANT (CR = 4.681; p = 0.000 < 0.05).\n
\nThe hypothesis is therefore accepted.
\nH2: There is a significant and positive relationship between amenities and affective image.
\n\nSIGNIFICANT (CR = 1.995; p = 0.046 < 0.05).\n
\nThe hypothesis is therefore accepted.
\nH3: Ancillary services have a significant relationship with affective image.
\n\nSIGNIFICANT (CR = 4.003; p = 0.000 < 0.05).\n
\nThe hypothesis is therefore accepted.
\nH4: Accessibility has a significant positive influence on affective image.
\n\nNOT SIGNIFICANT (CR = 0.543; p = 0.578 > 0.05).\n
\nThe hypothesis is therefore not accepted.
\n\nTable 6 presents hypothesis testing results.
\n\n | \n | CR | \np | \nResult | \nDecision | \n
---|---|---|---|---|---|
H1 | \nPrice is significantly positively related to affective image | \n4681 | \n0,000 | \np < 0,05 | \nH1►S | \n
H2 | \nThere is a significant and positive relationship between amenities and affective image | \n1995 | \n0,046 | \np < 0,05 | \nS | \n
H3 | \nAncillary services have a significant relationship with affective image | \n4003 | \n0,000 | \np < 0,05 | \nS | \n
H4 | \nAccessibility has a significant positive influence on affective image | \n0,543 | \n0,578 | \np > <0,05 | \nH4►R | \n
H5 | \nPrice significantly influences performance | \n1759 | \n0,080 | \np > <0,05 | \nR | \n
H6 | \nAmenities significantly influence performance | \n1173 | \n0,241 | \np > <0,05 | \nR | \n
H7 | \nAncillary services significantly influence performance | \n1066 | \n0,039 | \np < 0,05 | \nS | \n
H8 | \nAccessibility significantly influences performance | \n1071 | \n0,284 | \np > <0,05 | \nR | \n
Hypothesis testing.
Key: S: Hypothesis Supported. R: Hypothesis Rejected.
From these findings, it was established that the significant factors affecting the affective image were price, amenities and ancillary services. Further review into the respective magnitudes, using the critical ratios, the findings above do confirm that the most significant of the three is the issue of price. In other words, lodging prices, prices of restaurant food, prices of restaurant beverages and prices of goods and services play the most significant role towards improving the affective image. On the other hand, ancillary services such as cleanliness, tourist information, quietness, friendliness of local people as well as ICT readiness were found to be the second most important factor that has a significant positive influence on affective image. Amenities, while significant, was not so important, comparing with the above two that is price and ancillary services.
\nWith regards to the second dependent variable, that is, value/performance, it emerged that there was only one significant determinant and this was ancillary services as shown below.
\nH5: Price significantly influences performance.
\n\nNOT SIGNIFICANT (CR = -1.759; p = 0.080 > 0.05).\n
\nThe hypothesis is therefore not accepted.
\nH6: Amenities significantly influence performance.
\n\nNOT SIGNIFICANT (CR = 1.173; p = 0.241 > 0.05).\n
\nThe hypothesis is therefore not accepted.
\nH7: Ancillary services significantly influence performance.
\n\nSIGNIFICANT (CR = 1.066; p = 0.039 < 0.05).\n
\nThe hypothesis is therefore accepted.
\nH8: Accessibility significantly influences performance.
\n\nNOT SIGNIFICANT (CR = -1.071; p = 0.284 > 0.05).\n
\nThe hypothesis is therefore not accepted.
\nFrom the outcome above, accessibility, amenities and price were not significant determinants of performance. However, ancillary services were. One of the key aspects in the ancillary services category was the friendliness of local people. In this regard, it follows that the value of tourists was shaped more buy ancillary sub-factors such as friendliness of local people, more than traditionally known factors such as accommodation, amenities and price. The lack of significance of tourism resources such as amenities could be an indication of the evolving nature of the type of tourists now visiting Zimbabwe. Generally, the friendliness of local people is a known attribute that is valued by drifters and explorers, or rather allocentric and near allocentric tourists [94]. The lack of significance of amenities could mean that the nature of the tourists visiting Zimbabwe has drifted from being mass tourists, who from the literature, are divorced from the local people, to being drifters and explorers, who tend to interact with the local people, and will try to blend with the host community. This is further validated by the fact that attractions such as the natural landscape and climate had been dropped as not being valid, again, another indication of the evolving interests of tourists, from focusing on the attractions to showing interest in mixing with the host community. This tends to suggest the need to develop community and cultural tourism. Cultural tourism entails interacting with the local people in order to understand their history, present and future [95].
\nThe researcher went on to evaluate the overall squared multiple correlations for the two dependent variables, that is, affective image and value. The corresponding results are presented in Table 7.
\n\n | Estimate | \n
---|---|
VA | \n.204 | \n
AF | \n.467 | \n
Squared multiple correlations.
Source: Data Survey (2018).
From the results above, the r-square for value was 0.204 while that for affective image was 0.467. It follows from the above finding that the independent variables price, amenities, ancillary services, accessibility and attractions explained the greatest variance in affective image (46.7%) than in value (20.4%). What this means is that the independent variables determined more of the destination’s capacity to relieve stress, the destination’s capacity to provide relaxation, the destination as a pleasant place, the destination as an arousing place as well as the destination as a provider of excitement than they defined the value of the destination.
\nThe research model originally comprised of two endogenous variables as well as four main exogenous variables and these are presented in the quotations below:
\nInitial Eq. 1:
\nPrice, amenities, and accessibility had four items each, and hence i1-i4, while ancillary services had five items, and hence i1-i5. The equation in simple terms was,
\nWhere:
\ni: Items.
\nκ: intercept.
\nε: Error term.
\nα, φ, ϑ, η: Path coefficients.
\nPR: Price.
\nAM: Amenities.
\nAN: Ancillary services.
\nAC: Accessibility.
\nAF: Affective image.
\nInitial Eq. 2:
\nAgain, for Eq. 2, price, amenities, and accessibility had four items each, and hence i1-i4, while ancillary services had five items, and hence i1-i5.\n
\nThe equation in simple terms was:
\nWhere:
\ni: Items
\nκ: Intercept.
\nε: Error term.
\nε, β, χ, λ: Path coefficients.
\nPR: Price.
\nAM: Amenities.
\nAN: Ancillary services.
\nAC: Accessibility.
\nVA: Performance.
\nFrom the above, ε,β, χ, λ, α, φ, ϑ, η were all weights of the exogenous variables that were used to predict the endogenous variables. κ was the intercept and ε was the error term, or residuals. Nevertheless, upon testing the structural equation model, some of the variables were dropped off after their p-values were found to be non-significant (p > 0.05). In this regard, the original equations were subsequently revised. Upon structural equation modeling, for Eq. 1, accessibility was dropped off as it did not have a significant effect on affective image and the subsequent equation comprised one endogenous variable and three exogenous variables as shown below:
\nRevised Eq. 1:
\nOn the other hand, for Eq. 2, price, amenities and accessibility did not have a significant impact on value (performance), and in this regard, these were dropped off and the subsequent equation comprised one endogenous variable and one exogenous variable as shown below:
\nRevised Eq. 2:
\nWhere:
\nκ: Intercept
\nε,χ: Path coefficients.
\nAN: Ancillary services.
\nVA: Performance.
\nWith a view to testing the validity of a structural equation model above, several goodness-of-fit tests are carried out as prescribed by [96]. There are three broad categories of model fitness tests, and these include absolute fit indices, the relative fit indices as well as the parsimonious fit indices [89]. For the absolute fit indices, the CMIN/DF is the most common, and the chi-square test p-value should be greater than 0.05, while the CMIN/DF ought to be less than 3.0. On the other hand, for the relative fit indices, Goodness-of-Fit Index (GFI), Comparative Fit Index (CFI), Incremental Fit Index (IFI) and Normed Fit Index (NFI) are the most common and this ought to be greater than 0.90. With respect to the parsimonious fit indices, the most common include the Parsimony Normed Fit Index (PNFI), Parsimony Comparative Fit Index (PCFI) as well as the Root Mean Square Error of Approximation (RMSEA) according to [97]. Nevertheless, the most common is RMSEA and according to [98], the maximum acceptable is 0.08. Satisfying the goodness-of-fit at these three levels qualifies the structural model being tested to be accurate and valid [89, 97]. The model fit indices from the study are presented from Table 5.24 to Table 5.27. From the results, with respect to the absolute fit indices, CMIN/DF = 1.730 and this was less than the prescribed maximum of 3.0, and this was the first validation of the model. Table 8 shows absolute fit.
\nModel | \nNPAR | \nCMIN | \nDF | \nP | \nCMIN/DF | \n
---|---|---|---|---|---|
Default model | \n70 | \n487.929 | \n282 | \n.000 | \n1.730 | \n
Saturated model | \n351 | \n.000 | \n0 | \n\n | \n |
Independence model | \n26 | \n3752.085 | \n325 | \n.000 | \n11.545 | \n
Model fit-absolute fit indices.
Source: Data Survey (2018).
Further validation was accomplished by the relative fit indices for which IFI and CFI were 0.941 and 0.940 respectively against the expected minimum threshold of 0.90. Table 9 shows relative fit.
\nModel | \nNFI Delta1 | \nRFI rho1 | \nIFI Delta2 | \nTLI rho2 | \nCFI | \n
---|---|---|---|---|---|
Default model | \n.870 | \n.850 | \n.941 | \n.931 | \n.940 | \n
Saturated model | \n1.000 | \n\n | 1.000 | \n\n | 1.000 | \n
Independence model | \n.000 | \n.000 | \n.000 | \n.000 | \n.000 | \n
Relative fit indices.
Source: Data Survey (2018).
Regarding the model parsimony, PNFI was 0.755 and PCFI was 0.816 > 0.50. Again, both parsimony measures were greater than the expected minimum 0.50 and this confirmed that the model parsimony was not violated. Table 10shows the parsimony measures.
\nModel | \nPRATIO | \nPNFI | \nPCFI | \n
---|---|---|---|
Default model | \n.868 | \n.755 | \n.816 | \n
Saturated model | \n.000 | \n.000 | \n.000 | \n
Independence model | \n1.000 | \n.000 | \n.000 | \n
Parsimony-adjusted measures.
Source: Data Survey (2018).
Lastly, with respect the RMSEA statistic, this was found to be 0.052. Because the observed statistic was less than the expected maximum of 0.08, if follows, therefore, the model was valid. Table 11 depicts the RMSEA statistic.
\nModel | \nRMSEA | \nLO 90 | \nHI 90 | \nPCLOSE | \n
---|---|---|---|---|
Default model | \n.052 | \n.044 | \n.060 | \n.313 | \n
Independence model | \n.198 | \n.193 | \n.204 | \n.000 | \n
RMSEA.
Source: Data Survey (2018).
To test for the sampling adequacy for the model, the researcher considered the use of the Hoelter’s statistics as prescribed by [99], Barrett (2007) and [100]. Table 12 shows sampling adequacy. [100, 89] argue that a critical N of 200 or higher indicates a satisfactory fit. From the results above, both the independence model and the default model had Ns greater than 200, and thus confirming the adequacy of the samples used for this study. Overall, the above tests confirmed the validity of the model as well as the model results. Table 12 shows sampling adequacy.
\nModel | \nHOELTER 0,05 0,01 | \n
---|---|
Default model Independence model | \n397 404 245 248 | \n
Sampling adequacy.
Source: Data Survey (2018).
Minimization: 0.703.
Miscellaneous: 3.219.
Bootstrap: 0.000.
Total: 3.922.
The conceptual framework suggested the relationships between four components of the cognitive image, namely price, amenities, accessibility and ancillary services and affective image and destination performance. In order to improve destination image, the study found that ancillary services were more significant than accessibility. This was because ancillary services had a bigger influence on affective image than accessibility. Also, price did not significantly influence tourism performance. Amenities tended to influence tourism performance much more than price. This tended to contradict general perceptions among tourism and hospitality stakeholders. This also contradicted conventional wisdom. Price, amenities and ancillary services had a notable influence on affective image while price did not significantly impact tourism performance. This suggested that the conceptual framework was logical and did not deviate much from what the proposed destination image recovery model revealed. In summary, the conceptual framework was supported by the model with regards to the significant influence of ancillary services on affective image and the significant influence of ancillary services on performance. Affective image is known in literature to be a significant factor in image formation [64].
\nThe conclusions of the study were derived from the study findings. In terms of the research objective to do with the Current situation with regards to DI and performance of the tourism sector in Zimbabwe, Zimbabwe was mainly visited by tourists traveling for purposes of visiting friends and relatives (VFR). Most of the tourists traveled alone and some in groups followed by those who traveled as couples. The VFR market is known to stay in private homesteads, avoiding hotel accommodation. Africa and Europe contributed most of the tourists who visited Zimbabwe and these were mostly educated males, highly educated with an annual income of at least US$50000 per annum. However, they spent very little in the destination (at most US$1000). This was not surprising given that the destination mainly hosted the VFR market. The national airline lacked capacity to adequately fly tourists into the country and to various tourist destinations in Zimbabwe. Thus the destination’s accessibility was compromised.
\nThe second research objective covered determinants of DI and performance of the tourism sector in Zimbabwe. The most important factor which influenced image and performance of the tourism sector was the price charged by lodging facilities. It was followed by overall quality of the destination and the value tourists attached to Zimbabwe as a vacation destination. Immigration infrastructure and facilities for young children were rated highly. The ZTA and the Tourism Business Council of Zimbabwe (TBCZ), representing the government and the private organizations respectively in tourism and hospitality, were well positioned to influence DI recovery and tourism performance in Zimbabwe. However, both lacked funding to conduct image recovery activities. This implied that DI recovery could take long.
\nThe third research objective looked at the extent to which DI affected performance of the tourism sector in Zimbabwe. Most of the service providers and key informants indicated that they had been affected by Zimbabwe’s unfavorable image to a large extent. Most of them were considering relocating their businesses to neighboring countries. Tourists spent more on food and beverages than on accommodation supporting the prevalence of the VFR market or transit business. The small expenditure by tourists in the destination also indicated the huge effect which DI had on performance of the tourism sector.
\nThe fourth research objective dealt with developing a proposed DI recovery model for enhancing performance of the tourism sector in Zimbabwe. According to the proposed model, price, amenities and ancillary services had a significant influence on affective image. Ancillary services had a significant effect on tourism performance. Accessibility of Zimbabwe as a destination was found not to be significantly affecting destination performance. It can be derived from this that accessing the destination on its own is not the panacea for tourism firms to grow sales and profitability. This is because the tourist could still be constrained by prices when they are in the destination. From the study, the strongest relationship was found to exist between ancillary services and affective image. This suggests that a destination’s support services could influence a tourist’s feelings towards a place. In literature, a lot of attention tends to be put on tourist attractions-both natural and man-made and their capacity to draw tourists to the destination. It appears that the role of ancillary services in shaping DI is underrated. In view of the high prices of goods and services in Zimbabwe, accessibility becomes more of a hygiene factor than a key determinant of destination image and tourism performance. This finding suggested that accessibility would only be relevant in Zimbabwe’s tourism matrix only if the more important drivers of image and performance such as prices, amenities and ancillary services were right. The study showed that in terms of improving the affective image and value of Zimbabwe as a destination, the first thing which needed to be reviewed were the ancillary services then the price.
\nThe model has implications for theory. Past destination image recovery models assume that image recovery is synonymous with tourism performance. There was no attempt to isolate factors which influence image and the extent to which they do so and to identify factors which influence performance of the tourism sector and establish the extent to which they influence performance. This study has contributed to knowledge in that it identified specific components which form the cognitive image, measured them and established the extent to which they influence destination image. The challenge with using spinning as suggested by [16] is that there is an assumption that the tourists and potential tourists are not quite informed about the source of the problem at hand. The proliferation of modern technology makes it very difficult for destination marketers to depend on spinning nowadays. In [101], it is noted that mobile technologies which include smartphones, mobile applications and tablets have become the main devices for users to access the Internet.
\nThe model has a number of implications for policy. It was established that price is a key factor in terms of the formation of the affective image. This implies that in order for tourists to have a favorable view of Zimbabwe as a tourist destination, more attention should be given to pricing. The stakeholder approach which informed this study as indicated in the theoretical framework, needs to be adopted and utilized. Affective image influences potential tourists to consider the destination among many and influences destination choice [102]. Also, it was established that the friendliness of local people played a critical role in the performance of the tourism sector in Zimbabwe. However, the host community needed motivation. In [14], it is noted that eco-tourism could be used to motivate the host community since it increases employment opportunities and it enhances the tourism economy. Evidence is there to show the importance of the host community in tourism performance. The study revealed a need to attach more importance to the logistics and transport sector represented by the struggling national airline. This sector was key in increasing international tourist arrivals and generally enhancing the quality of inbound tourism. Another implication was the enforcement by government of green practices in logistical and transport-related operations. This was vital in enhancing environmental sustainability, reducing criminal activity and in attracting international tourists [34].
\nThis study focused on tourists and those employed in the tourism and hospitality industry. Further research could include the ordinary person and also explore strategic public-private partnerships and destination image recovery in Zimbabwean tourism. Respondents were selected from major cities and resorts such as Harare, Victoria Falls, Kariba and others. Possibly, if data had been collected from more areas, the research quality could have been better. Also, future research could explore the value attached by the tourism and hospitality industry on research.
\nSupporting women in scientific research and encouraging more women to pursue careers in STEM fields has been an issue on the global agenda for many years. But there is still much to be done. And IntechOpen wants to help.
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